PRE2018 3 Group17

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Group members

Group Members	Student nr.
Diederik Geertsen	1256521
Cornelis Peter Hiemstra	0958497
Joël Peeters	0939193
Benn Proper	0959190
Laila Zouhair	1260529

All Robot Ideas

Below are all ideas that were thought up for the robots everywhere project, they are ordered by the order in which they were thought up. The final idea that was elaborated on was the disaster observation drones.

• Tilting 3D printer to eliminate support material
• Breakfast bot
• Robot to remove microplastics from water
• Clothes folding robot
• Building guidance robot
• Disaster observation drones

Problem Description

The employment of drones in disaster areas is an obvious application which can aid in the fast gathering of information concerning the situation and locating survivors. In recent years, extensive research has been conducted into using drones for this cause. The focus of these studies were, among other topics:

• Computer vision for recognizing survivors [Rivera, Villalobos, Monje, Mari ̃nas OppusRivera .2016].
• Communication among drones [Saha .2018].
• Optimal routing [Mersheeva .2015].

Past research has created a good foundation towards the development of an actual product, however a practical issue that still remains is the limited operation time of these drones. This problem is especially challenging because when applied to disaster areas, a solution must be independent of existing infrastructure which is likely to be damaged. The aim of this project is to develop a solution to extend the operation time of aerial drones, which are very suitable for disaster area monitoring due to their independence of ground infrastructure, but have an especially limited operation time compared to e.g. ground vehicles. Drones can be applied to many kinds of disaster, for the scope of this project we will focus specifically on earthquakes.

Users

Our users require a way to quickly get information about a large disaster. This would mean that we must automate this information gathering on different scales. For example, when there is a very large earthquake. The emergency services have no good way to get to the disaster, they do no immediately know the scale of the disaster and they do not know which parts really require their attention. This all costs a lot of time, which can be greatly reduced. To get all this information really quick, drones are often used. These are manual controlled. This means that they can only gather information at as many places as they have people available. If we can make the robots independent and automated, while communicating with each other and giving important information to the users, this process would become much faster. A major problem in the automation of these search processes is the limited operation time of the drones used. This is a problem for the operators, as it severely limits the range of the drone system, and requires the users to manually make sure drones are recharged. A system that would provide a solution to the limited operation time of the drones would benefit the rescue operations.

Who are the stakeholders?

There are different stakeholders with different roles in this project involved. They would all take advantage of a solution we provide to their problem. The three stakeholders are Users, Society and Enterprise. We will describe per category why this particular stakeholder is involved with our problem and how our project will contribute to a solution for their problems.

Users

The biggest group of stakeholders are the users, which consists of civilians, government organizations, and private organizations or non-government organizations. These would all take advantage of the solution we provide, in particular, those which are our intended end-user, i.e. the groups which will be involved during a natural disaster. We shall describe how these groups use our solution.

• Government Organizations

Organizations formed by the government to combat natural disasters will take the most advantage of our solution. When a natural disaster will take place on large scale, emergency services or other organizations want to gather information as quick as possible. With our solution, this will be possible in a shorter period of time and with less involvement of personnel necessary.

• Civilians

Civilians struck by natural disasters benefit from our solution. The quicker help comes, the smaller problems arising for civilians will be. This counts for medical care, but also search and rescue and preventing loss of private property.

• Private organizations/non-government organizations

Organizations could also use our solution to work for different purposes. For example as security of property. Next, our solution to the described problem could be used as a good solution for similar problems as government organizations are describing.

Society

The society as a whole would benefit greatly from our solution. Our solution is relatively cheap and would be a great addition or replacement for existing solutions. Our solution would contribute to prevent loss of life, loss of property and would help organizations greatly.

Enterprise

The enterprise would also benefit from our solution. Firstly, the usage of drones would be far greater than before. This would mean that enterprises could cash in into our solutions.

• Aerialtronics

Aerialtronics is the company we have interviewed so that we would be able to involve the user into the process. Aerialtronics makes use of a LiPo battery with a weight of 2,3 kg, which is approximately one fourth of the total weight of the drone. The drone has a flight time of approximately 18 minutes and a range of 1000 meter. The limited operation time is a major issue and it is therefore that there is a cooperation with the European commission to use power pylons to charge the drones. The drones would be charged by means of induction. The frequencies that are used are common used frequencies with a bandwidth of 900 Hz. For control radio frequency 2,4 GHz is used, which is a common used frequency and it is also the frequency that is used by the wireless computer networks.In dense housing areas this could cause issues, due to the large amounts of wireless signals. The used frequency for video transmission is 5,4 GHz. The used frequency for telemetry is 6,68 MHz. Drone delivery is something, that is certainly going to happen according to the company. The limited operation time, however, makes this very hard to realize on a bigger scale.
The use of drones to explore an area is a common task for aerialtronics. They make use of circulating drones, so that there will be an overlap. All information will be collected and nothing will be missed in this way. We could use this idea in our own project. The circulating drones will make it possible that no time will be wasted due to the swapping of the batteries. While one battery of a drone gets replaced, the other drones will then move in a circle to keep collecting data.

Approach

There are four functions that the system will need to be able to perform, each of which is listed below:

• 1. Drones need to be able to reach the system. This may include something that allows the system to move around in a potentially chaotic and hard to traverse disaster area.

• 2. The system needs to be able to either recharge or swap the batteries of a drone so that the drone can continue its search afterwards.

• 3. The system needs to keep working long enough to be able to increase the effectiveness of the swarm before having to be recharged/refuelled itself.

• 4. The system needs to be able to communicate their location to drones and receive information on battery levels and location of drones.

In order to keep a narrow and well-defined problem, a general solution for the design will be chosen based on the benefits and drawbacks of some chosen concepts. This general design will then be used to choose an optimal design for each function in order. That means, for instance, that the design for function 4 will be chosen to be chosen to work best combined with the designs for the other three already chosen functions. Once each function has been chosen, they will be worked out in greater detail, this time making sure all designs are fully compatible. In short, only one design option will be researched at a certain time, and the design of any option will be constrained by choices already made for the other options.

Deliverables

The final deliverables will consist of a design concept that has been expanded using technical drawings and a 3D model. These drawings and the model will be based on decisions made with the help of a mathematical model. This mathematical model will also be provided to show the effect of changing different properties of the design and whether or not changing these parameters to see if they have a positive effect on the system's effectiveness.

RPCs

The RPCs for the system will be defined as follows:

Requirements

• The system must swap batteries of the drones.
• The vehicle needs to be mobile.
• The vehicle must have a 25 km system range extension.
• The vehicle must be fully autonomous positioned.
• The system must execute the tasks fully autonomous.
• The vehicle must return to the base after task completion or when in need to service.
• The battery swap should take max. one minute.
• The vehicle must service 10 drones in its operation cycle (about 2 kg payload).
• The system must service at least 1 drone at a time.

Preferences

• The system should still function in "bad" weather conditions (rain, wind up to a certain speed).
• The vehicle should be safe for human interaction.
• The system should have low manufacturing and operation costs.
• The system should support a manual override.
• The system should be easy employable.

Constraints

• The system functions independently of available infrastructure.
• The vehicle is operational on rough terrain.

Phase 1: Traversal of environment

Here the concepts will be discussed on how the drone moves around the environment, a choice will also be made afterwards.

Concepts

In this section, several different concepts for the transport of the system will be discussed. The benefits and drawbacks of each concept in the given environment will be discussed so a well-informed choice can be made.

Ground vehicle

A ground drone that can move around the disaster area that can serve as a charging platform for drones. This comes with some up- and downsides. First of all the drone could move a bigger payload with the same amount of fuel when compared to a flying drone. It will also be less sensitive to weather conditions than an airborne drone. It does, however, have limited speed and movement depending on the state of the terrain.

This last point could prove troublesome after an earthquake. After such a disaster the rubble will prevent proper movement for smaller drones, larger drones could, however, have less trouble with this but can also need more space to move through. It is clear that a ground vehicle cannot follow the searching drones as well as it could if it were flying, this further complicates its function as a mobile charging platform.

It also operates in a more dangerous environment compared to aerial drones. It must be mindful of unstable areas and if they hit an obstacle. It could, for example, cause some walls or a building to collapse. This then further endangers the lives of both the drone and survivors of the disaster.

Ground drones do need to be given more attention to how they move around a disaster area. In general, some path planning has to be done from beginning to end. Considering that the area will be difficult to traverse and certain locations are destroyed, the drone will need some type of advanced vision analysis [2][4][5]. This would either require a constant connection to a computation server, or a considerable amount of processing power to achieve this.

Balloon UAV

A balloon is added to a quadcopter, this balloon's lift partially cancels the weight of the quadcopter. This allows it to stay in the air for a considerably longer amount of time. The drone also does not have any problem with broken infrastructure due to the ability to fly.

This idea also has some downsides. First of all it would be difficult to keep stable in windy areas [6]. It would, therefore, have difficulty with charging the drones using any method due to the movement of the two drones. The size of the balloon would also have to be large to cancel the weight enough for it to fly considerably longer [7]. This is therefore not an ideal solution for the main drone design.

Continuous flying large drone

A drone that can fly continuously without having to land. This drone uses up a lot of energy especially considering that drones usually only have a battery life of about 30 minutes during operation. The obvious solution to this would be by using alternative fuel sources or additional batteries. Alternative fuel sources could include diesel or petrol, which have a considerably higher energy density and therefore extend its range, even considering the lower efficiency of combustion engines. Such a large drone is however very costly to manufacture and uses a lot of resources during operation, which may be limited in a disaster scenario. More power cells could also be an improvement, but this would also increase the total weight of the drone and therefore decrease the flight time. This drone also suffers from other problems that aerial drones usually face. Its relatively large size and mass do make it more stable in daunting weather conditions [6].

Hopping drone

Drones fly ahead and land at certain spots creating an infrastructure for the observation drones to have their battery swapped and extend operation time. A certain amount of drones can form a grid of charging stations. The observation drones can fly to the nearest station when in need of service. Because the drone lands on its designated location, it can stay relatively simple and cheap compared to a drone that has to stay airborne for a longer time. The system itself takes the form of a larger drone, which should be large enough to carry what is needed to recharge or swap the batteries of the other drones.

Large drone with chain of recharging drones

Fuel powered large drone. Can fly for around 1 hour without being refuelled or recharged itself. With a chain of refuelling drones, the large drone will be refuelled/recharged. The hopping drone is a concept that combines the best of both worlds in regards to aerial and terrestrial drones. It solves the traversal of the environmental problem in the ground drone, but also solves the stability problem of aerial drones, as it can land and charge the drones on the ground.

Conclusion

Out of the ideas and concepts discussed, the hopping drone seems to be the best solution. This concept combines the positive aspects of the aerial- and ground-based drones, these positive aspects are:

• Use less power than an aerial drone due to the fact that it will rest on the ground when it has reached its destination.
• Will stay stable when changing batteries due to standing on the ground when it does so.
• Solves the terrestrial navigation problem by being able to fly over obstacles.
• Drones can easily reach a target that is standing still.

These aspects are all contributing factors to choosing this concept over others.

Phase 2: Increasing flight time of drones

For the drone to succeed in its function, it would need a method to extend the flight time of the drone. This can be done in a multitude of ways, below several concepts are discussed and a decision is made.

Concepts

Below several concepts for increasing the lifetime of the search drones are discussed. The pros and cons will both be discussed so an informed decision can be made.

Changing Batteries

Changing the batteries of a drone is one of the quickest method to extend the drone's flight time. In this case, the downtime of the drone only consists of the time it takes to change the battery. This method also has zero power loss, which is a considerable advantage compared to charging the battery in the field.

It does, however, have a few drawbacks. Relative positioning of the drone has to be far more precise compared to some charging methods. Multiple solutions have already been made for this problem, the solutions range between a 50% success rate to an almost 100% success rate [9][10][11][12]. These systems are quite similar, however the system of [10] has the biggest success rate and will therefor be the basis for our design.

Refuelling

Refuelling a drone using liquid fuel has its benefits. It is more energy dense, therefore, carrying fuel containing an equal amount of energy as batteries will be lighter. It is also already possible to refuel UAVs in the air, which means that refuelling on the ground would be easier to implement [13]. The major drawback to this idea is the fact that the drones would have to be fuel based drones. These drones usually aren't common and are more difficult to develop, complicating the rescue drone design. Also, the fact that fuel-based vehicles and machines are slowly being phased out, makes an alternative solution more desirable.

Charging

Recharging the drone's batteries is the most obvious choice to increase the flight time of the drones. This would require a way for the drone to be connected to the charging station. This can be approached by using the feet of the drone as charging points by making that and the contact point with the main drone conductive [14], it can also be approached by using wireless charging, a relatively new method of charging electronic equipment[8]. Both of these are relatively easy to implement, but they both have drawbacks. Both of these options suffer from power loss while transferring energy from one drone to the other, wireless charging wireless charging is in this case worse than wired with an efficiency of around 65% [8]. The tradeoff made here is the ease of implementation and the efficiency of the transferal of power.

Replacing Drones

This concept is an extension of the charging concept. This system will consist of a certain amount of fully charged drones that are transported using the main drone. When a currently flying drone is running low on battery, the drone can fly towards one of these 'Hub' drones and essentially get replaced by one of the dormant drones. This new drone will then continue with where the other drone left off in regards to its search for survivors. The drone that was just replaced can then be charged in the meantime.

This idea has the smallest downtime, but will also require a lot of additional weight to be carried by the main drone. The charging time of the drones is also of importance, it should be able to finish charging a drone in the same time it takes for a drone to need a recharge. This means that an additional charging solution needs to be devised for it to function properly. The additional dormant drones are however also a waste of resources in a way, they could be used for searching instead of lying around not doing anything.

Conclusion

For this design step, it was decided to use the battery replacement concept. The reasoning for this is as follows:

• As shown in the provided sources, it is possible to have a 100% success rate for changing batteries.
• It is quicker than most conventional methods of charging.

This concept does also have some drawbacks which come in the form of the weight of the added batteries. This problem is however alleviated because the system would still need to carry a set of batteries that can charge an equal amount of drones. The fact that the drone doesn't stay in the air for long also helps with the added weight, as it doesn't use as much power. Any additional problems with the longevity due to this weight will be solved in the next section which addresses it directly.

Phase 3: Energy storage

In order for the drone to keep flying, it will need to store the energy to do so. This can be done using batteries or fuel, and either bring their own possibilities and problems. In this phase, it is discussed how this energy storage will be executed.

Concepts

Big interchangeable batteries

The energy density of Lithium metal batteries is 1.8 MJ/kg. 1

Propeller specifactions for different dimensions [15]

From this, we can deduct how much power we need per hour to keep a large UAV of a given weight in the air. And from that, we can give an estimate of how big the battery must be, and how long it will keep in the air.
We now know that we need 96.1683 W(J/s) to keep a drone in the air. This keeps 355.4167 g in the air. So, it takes 96.1683/0.3554167 = 270.579 W/Kg to keep the drone in the air.
So this means that for a drone of 2 kg, the drone would use 541.158W. Let's say this is including a 1 Kg battery. That would mean that the drone has 1.8 MJ energy stored. Using this, we get that the drone would have: 1.8 MJ / 541.158 J/s = 3326.2s of battery power. That is 0.9240 hours of power. Of course, the drone would not have a 1 kg battery, when it is only 2 kg. So let's say that 1/8 is dedicated to its own battery. That would mean that the drone has 0.1155 hours of battery, which is equal to 6.93 minutes. This is only for hovering. We excluded other systems here, for example, the control unit of the drone, the communication module, the refuelling/recharging of other batteries. That is why we came up with 1/8 is dedicated to its own battery.

Big rechargeable batteries

The uptime of a rechargeable battery would be comparable to that of a replaceable one, but the time it takes for the system to be ready again when the battery needs to be recharged is significantly larger. While charging times are heavily dependent on the quality of the charger, but it is generally not recommended to charge a battery above 10 Ampere [16]. The expected battery charge time is about an hour per kg, which means the uptime of the system would be smaller than the downtime of the system, severely reducing usefulness.

Solar energy

The option to instead charge the battery while in action is also available: Solar panels can ensure that the system does not have to return to a charging station to recharge, instead opting to simply land somewhere and wait until the battery is mostly recharged. The idea of making short flights with breaks in-between is already the idea behind the hopper drone, so these concepts might work well together. The downside of solar panels is that they are heavy, which increases the power consumption while in-flight. The question is whether or not the solar panels are feasible in terms of possible flight duration.

Light-weight solar panels are produced by companies such as Flisom [17], and their products will serve as a benchmark for the potential of solar panels in drone applications. According to their datasheet [18], an 87*41 cm solar panel weighs approximately 0.8 kilograms. It has a peak nominal power of 30 Watt and is expected to be able to function properly for ten years when used at 90% of that power. This means that a light-weight solar panel of this kind is expected to deliver 80 Watt per square meter at a weight of 2.35 kgs. Using the previous metric of 270.579 W/Kg to keep a drone flying, a solar panel would increase the net power consumption while in-flight with 554 W/m^2, while offering an on-ground energy production of 80 W/m^2. The price would be a one time purchase of 70 euros per unit.

Fuel

energy production per fuel weight and volume [19](Click to enlarge)

The above figure shows the amount of energy per mass that can be gotten from different energy sources and shows that chemical energy sources are much more effective than electrochemical energy sources. However, these energy sources require an engine to convert the energy to useful energy, reducing efficiency and adding more weight. According to [20], the power output of an engine and their weight are related linearly. Using the previous number of 270.59 W/kg for the drone to fly, a drone weighing 50 kg on its own would need an engine of 25 kg, which means that approximately a third of the drone's weight would be made up by the engine. Given the weight of the drone m, we can then calculate that a kilo of gasoline or gas could power the drone will provide power for the order of magnitude of

Joule per kilo of fuel [J/kg]/((weight + weight engine [kg]) * Watt cost per kilo [W/kg]) = 3.6*10000/((50+25)*270.59) = 2.7 hours.

This means that using a fuel-powered engine to power the rotor blades of the drone is by far the most efficient method of storing energy.

Conclusion

Since a fuel engine and tank are the most efficient way to store energy for the drone, this is the concept we will be working out further. Refuelling the service drone will still have to be done manually, but given the large potential range of fuel-powered flying vehicles, this should not serve to be too much of a problem. The drone can stay active for a long time using the hopper principle, and then return to a fuel station where it can be manually refuelled, a process which won't take very long, especially compared to charging a large battery/ The addition of an engine and a fuel tank does add the danger of explosions, which could cause the drone to crash down to earth causing danger to anyone standing below. This means that the engine should not be pushed to extreme operating points, and its temperature will need to be monitored.

Phase 4: Collecting Data from rescue drones

After contacting a business that specialized in drones, different design aspects were communicated to best incorporate them into the design. This information helped with designing this component of the battery replacement drones.

A system consisting of multiple surveillance drones which have to share a limited number of service drones, depends heavily on communication to determine which surveillance drone should be serviced when. To keep the system as low-cost and simple as possible, communication should be limited.

Key information to transmit by surveillance drone:
1. Position
2. Battery status
3. Surveillance data

Information to submit by service drone:
1. Position
2. Status (available / empty / damaged)

The communication method should preferably also be the same as the surveillance drones of which the batteries are being replaced
We can calculate how much bandwidth/upload and download speed we need for all this data.

Position

The position can be determined using GPS. This does not cost much data, and can easily be used be multiple drones to keep track of the position. Also, GPS is a widely used technology which is almost everywhere available. The error of GPS is <= 0.175 meters
https://www.gps.gov/systems/gps/performance/accuracy/
Another way to track the position is using A-GPS. This is similar to GPS, but instead of using GPS-satellites, it uses other sources to determine its position. It can use WIFI-networks, network for mobile phones. With this, the location can be delivered by the GPS system more quickly. Since the area where the drones work can be destroyed, we then need to find a way to give the system a way to get the location. This would require more work, but the benefits are still there. Plus, A-GPS works like normal GPS when no such networks are available.
RFID is also a possibility. It uses stored objects to get the location of the device, for example QR codes. Since our enivorenment is not controllable and probably destroyed, this option would be less convenient than GPS or A-GPS.

echnically, there are several data consumption when using a GPS tracker.

GPS locating, LBS/GSM locating, heart beat data and other operations.

Usually, GPS locating uses 88 bytes each location uploading. LBS locating uses 109 bytes and hear beat/ link data is 62 bytes every 4 minutes.

Therefore, when the personal tracker uses 10 minutes interval time, or even 1 minute, the monthly data consumption will be less than 30 Mb.

If you need some personal GPS tracker[1], ReachFar Tech[2] is a nice choice.

https://www.quora.com/How-much-data-does-a-gps-tracker-use-if-you-locate-it-only-once-a-day

Controlling drones

We can use radio control to control the drones. The business said that using a frequency of 2.4 GHz is the most common radio frequency to connect the ground transmitter to the drone. One problem arises that, since wireless computer network also uses this frequency. This can cause "black outs" of the drone with crashes. This happens most of the times when the drone is in an area with many wireless networks, like a living area or industry area.

Another option is using 5.8 GHz. Then you avoid the problem of the network, but this only has a range of 500 meters. Another problem which arises, is that with this frequency connection is lost when the drone is behind a tree, hill or building from the perspective of the transmitter.
For long range FPV flying, you would need a frequency of 1.2 or 2.4 GHz. The first one offers a range of 10 km. A downfall of this frequency is, that once you loose connection with the drone, there is no way to reconnect. But it is more effective when using in areas with many walls and obstructions.
2.4 GHz has a range of between 800 meters until 5 km. This depends on the environment.
If we want to use both control and video transition, we need to use different frequencies for both. So we need one with a frequency of 2,4 GHz, and one of 5,4 GHz. The company suggested to use 2,4 GHz for control and 5,4 GHz for video. https://www.ebay.com/itm/SV6202-2W-TTL-Interface-433MHz-4-5KM-Wireless-Data-Transceiver-Module-/282787876050
New:

Vanguard

The Vanguard is a popular expensive UAV, which is capable of traveling a big distance. Its transmission range for the controller is 35 km, which is outstanding. Although we do not aim for a 35 kilometer range, the fact that there is already technology for such a transmission range is nice. The Vanguard uses a transmission frequency of 433 MHz at 800 mW for radio control and telemetry transmission. Further, it uses a 2.4 GHz frequency at 800 mW for the video transmission and receiving. With this, it uses a SkyLink Ground Station to control the UAV, transmit data and receive video input. We do not know what the transceiver is inside the UAV itself. The radio control frequency is for long range control, while it uses a higher frequency for the video since the higher the frequency, the more data can be transmitted.

Frequencies

For our own UAV, we went looking for a transceiver inside the UAV. First, we have to choose a frequency for the radio control and data transmission. There are multiple much used frequencies, where the range and reliability is different. The most used frequencies are according to hobby websites 27mHz, 35mHz, 50mHz, 900mHz, 1.2GHz, 2.4GHz and 5.8GHz. Although 2.4GHz is commonly used for drones, it does hurts the range per power usage. This, because a higher frequency requires more power, so the signal strength will be smaller at the same power usage.
5.8GHz is mostly used for the controlling of drones in close range. With the right equipped antennas, a range of 1-2 km can been done. Further, since the distance of the control is very limited, there is little chance of interruption with other frequencies. A drawback of this frequency is the poor penetration of walls or other objects.
2.4 GHz has better range and penetration than 5.8 GHz, but it can carry less information. Further, this is the most used frequency, so there exists much more interference with other sources. This means that if there are many sources in the area which uses this frequency, there is a possibility of data loss. 1.2 GHz/900 MHz has much better range and penetration. These are much used by many drones, but in many countries it is illegal to use this frequency, unless the user has a certification which proves that the usage of this frequency is legal. Further, the equipment must be much bigger than the higher frequencies.

Ground transceiver

In order to communicate the ground transceiver(controller) with the drone, it would need to be at the same frequency as the drone itself. Frequencies other than it is designed for will not be picked up.
The typical GCS is either a two-way data link (radio) for remote control or an on board computer (generally with GPS navigation) connected to the aircraft control system flight control and operating system which includes the control station(s), communication links, data terminal(s), launch and recovery systems, ground support equipment, and air traffic control interface. With the former, the pilot must apply every turn and make all elevation and maneuvering changes discretely and with the latter, a simple tracing of the finger or entering destination coordinates is all that is needed and the system works out the details.

Choosing

There is a lot of data available for picking a frequency to use for a drone. The most used guideline is, that when there is a need to cover much distance, go for a lower frequency. A much used frequency for long range control is the 900 MHz frequency. It can transfer enough data to control the UAV.
Since we only need to be able to control the drone(radio control) and to receive information(data and telemetry), we do not need to have a high frequency. We will use the Vanguard as a guideline. The Vanguard uses 433 MHz at 800 mW for the RC and telemetry. It uses a high wattage, because it needs to have a range of 35 km. We do not need to get this far, and we set our aim at 10 km. There are many options to choose from, so we made a short list of available options:

• https://www.csgshop.com/product.php?id_product=86

This transceiver is capable of having a range of 10 km at 250 mW, with a frequency of 902 to 928 MHz. It can transfer 115 kbps max.

Choosing

Instead of looking at a multitude of options. It was decided to ask a company that manufactures surveillance drone what their solution was to communicate with them. The reason for this was that the eventual battery replacement drone has to work for existing systems. This means that the type of communication would ideally have to stay consistent within such a system to minimize the number of different components present and modifications needed for the drones. After talking to the company Aerialtronics about their drones, they said that their drones use a radio control method that has a range of 1 km. It was therefore decided that this method would also be applied to the service drone.

Final Concept

To summarise, the service drone will consist of the following components:

• The drone will move around in short bursts but will rest on the ground to enable servicing of the surveillance drones
• This servicing consists of replacing the batteries of the drones to prolong their flight time
• The service drone will also function by using fuel
• Communication with other drones will be handled with a radio control similar to model planes

These components will all be worked into a final design of the drone in the detailing section.

Model

A model has been constructed to figure out an effective combination of parameters to ensure that the system works properly. This model was constructed in MATLAB and is made to support design decisions in the detailing phase. The following aspects can all be changed individually in the model.

MATLAB model visualization

• Drone speed
• Number of observation drones
• Number of service drones in a grid of X*Y
• Fuel cost per step
• Total simulation time
• Size of the environment
• Amount of fuel in the drone
• Number of batteries that the service drone carries
• Time needed to change the batteries

In this model, a certain amount of surveillance drones, and a certain amount of service drones are placed in an area with a defined size. The surveillance drones fly in a random pattern to prevent optimization for one specific search strategy or task. When a surveillance drone is low on battery, it flies to the nearest service drone that still has batteries in storage. Only one drone can be serviced at a time and the service drones start with a limited amount of batteries.

The foremost purpose of this model is to analyze what amount of service drones, and what amount of batteries would be optimal to extend the operation time of the surveillance swarm for a desired duration.

In the model, service drones are shown as squares and surveillance drones as black dots. When a surveillance drone is empty, it stops moving and a red cross appears in its place. The number next to the service drones shows the amount of batteries still left in storage.

Detailing

When designing the drone itself, it is important to take into account the effect on the surveillance drone, as well as the performance of the service drone itself. The first is done by using the model described in the section above. These will mainly be used in the sections about the number of batteries and the final

General drone design

The general drone design is characterised by 4 different layers. The top layer consists of the rotors, the second layer is the plate on which the drones can land. The third layer is a compartment where all of the electronic components needed for the system to function are stored. Finally, the fourth layer is where the landing gear is located. Each of these has its own requirements to take into account.

For the rotors, it is important that the drone can fly in a stable manner but is also able to fly for a considerable amount of time. In general, more rotors will result in a more stable and controllable flight but will be at the cost of flight time. Therefore it was decided to go for a set of four rotors that are placed in a square formation similar to a quadcopter.

For the landing platform, it is important to take into account the size of the drone that must be able to land on top of it. In this case, it is assumed that the drones will be roughly 0.6 meters in length and width. This results in a free space between the rotors of at least this size to ensure that the drone will not accidentally hit the rotors. It is best to use a plate that can be placed on top of the third layer on which the drone can land. It should also be noted that this plate has a hole in the middle, this is to make sure that the batteries can be replaced.

The third layer consists of different compartments where the different electronic components are stored. There is a compartment for the batteries needed for the surveillance drones. This can also be accessed by the manipulator which replaces the drone batteries with these stored batteries. There is also a location where the fuel tank is placed for the service drone itself, this will probably be one of the larger components due to the size of the main drone itself. The final component that has to be added here is the central computing component and a transmitter and receiver to connect with different surveillance drones.

The final layer is the landing gear. This landing gear must be designed in such a way that it can correct itself when standing on uneven terrain. This can be done by having the platform correct its orientation when detecting that the landing platform is not placed perfectly horizontal. This can be done by having the four legs of the drone be variable in length, and by using a gyroscope to measure the orientation a controller can change the length of the legs to ensure a horizontal orientation.

A couple of example drawings are given here to visualize the discussed components. Do note, this is a general simple drawing. Not a final technical drawing.

Amount of drones that can be charged at a time

Carried batteries

Fuel tank service drone

Replacing batteries

Wireless transmission system

Landing gear

The landing gear for this drone should be able to land on rough terrain. This rough terrain can take the form of slope or a place with uneven terrain. This means that the landing gear will need to adapt for multiple situations to ensure that it will always land in a stable position, otherwise, it would complicate the ability for surveillance drones to land on this service drone.

It is possible to solve this problem by using actuators [22] or using mechanical principles[21]. Considering that actuators will both be a lot heavier than using mechanical principles, and the fact that the drone should be able to land safely regardless of how much battery power is left. It was decided to use landing gear designed around mechanical principles.

Original passive landing gear for drones to land on rough terrain

The landing gear that seemed most suitable for the task was a passive landing gear designed at the University of Queensland in Brisbane, Australia [21]. This design uses the principle of a shared load between objects that are usually mechanically isolated. This design can then be worked and improved on and will result in the landing gear on the right. This landing gear was however designed for a smaller drone and will need to be upscaled to properly handle the service drone in its entirety. Things to keep in mind are the weight, strength and cost of the entire landing gear. These will all be worked into a 3D model and a technical drawing to show the exact dimensions of this landing gear.

Starting with the weight of the landing gear, it was decided that it should not weigh more than 2 kilograms. This is due to the fact that using a heavier landing gear will result in more fuel consumption for the drone, and thus impacting the longevity of the design. Another constraint on the system is to only use steel and aluminium, this is to keep the landing gear relatively cheap. Another material that was considered was carbon fibre, but this would cause the construction to be a lot more expensive due to the manufacturing of these components using carbon fibre.

To figure out the dimensions of the individual leg components the total weight is divided by the number of bars needed. This is then divided by a length that has been decided beforehand, considering the drone base being about 0.75 meters in both length and width, this would result in bars of about 0.4 meters in length. Dividing this result with the density of aluminium will result in a number representing BH, which is a constant area of the cross section of the beam. As was mentioned before the landing gear has a maximum weight of 2 kilograms, the beams themselves can then be taken as 1.5 kilograms to leave a certain margin of error. Using the method of calculating BH mentioned before this results in BH = 1.5/(0.4*12*2712) = 1.152286e-4 m^2. The deflection of a beam under load is equal to X = F*L^3/3EI. Every value of this equation is fixed except for I, this means that for a higher I, less deformation occurs in the beam. The equation for the second moment of area I for a beam is I = (1/12)*BH^3, this means that if BH is kept constant, the increase in H will outweigh the decrease in B when looking at the effect on the deflection.

Single leg designed in NX 10

Technical drawing of the leg (Click to enlarge)

For this system the force will only come from the top, so theoretically it is possible to use extremely thin plates as this would have a high I over the bending axis. It is however recommended to keep some thickness in the beams as unexpected forces could easily cause the beams to buckle due to being too thin. Therefore the ratio B:H is about 1:2, which results in a total deflection of about 2.7 cm deflection in the beam if that was the only used to hold everything up (assuming bending load).

Other components of the design include some gears, connecting plates, screws, springs and a locking mechanism to ensure that the drone does not tip over with an uneven load. These components can all easily fit within the 0.5 kg margin that left when designing the beams. This results in a landing gear that is below 2 kg in weight.

These components were designed using NX10, but during designing some dimensions were changed, this was due to the length of the legs being too large. To compensate the cross-section could have a higher area, resulting in a higher I and lower deflection. After re-evaluating the dimensions, the length, width and height of the new components are 0.25, 0.01 and 0.02 m respectively. This results in a total deflection of 2.2 mm under the worst possible conditions.

The final design has some differences when compared to the paper referenced before. First of all, the leg joint consists of two pieces of steel on both sides of the legs instead of on just one side. This is called a double shear mounting and is used to prevent torsion in the beams. This does mean that the gear used to couple the movement of different leg sections needs to be placed somewhere else. The new location of the gear is on top of the leg components, the actual gear will then be placed behind the joint plate. It is possible to mill this gear to ensure, because the beam is solid. The spring constant for the spring between a set of legs is chosen to be about 800 N/m

Some parts that aren't shown in the design are the springs, these springs are connected to the leg by placing a rod in the two openings at the top of the design. Two of these opposing rods are then connected by a spring that has a locking mechanism built into it. This ensures that the leg movement is coupled and that the design will gain some stability.

The bill of materials needed to construct these items are as follows:

Component	Cost/part (€)	Weight/part (kg)	Amount
Lower Leg	2.58	0.133	4
Upper Inner leg	2.58	0.132	4
Upper Outer leg	2.58	0.145	4
Joint plate	2.00	0.034	8
Screw guide	2.00	0.034	8
Wheels	3.00	0.033	4
Vertical guide	3.00	0.02	4
Spring	10.00	0.025	2
Locking mechanism	10.00	0.100	2

After making a couple of estimates for the more complicated components, the total price should at most be €126.96. The total weight should at most be 2.646. This weight is in fact almost half a kilogram over the actual target weight, but this is due to the fact that areas with high loads are designed using steel plates, which are four times as heavy as aluminium, but are also stronger. After discussion took place, it was decided that this weight could easily be compensated for somewhere else and therefore this is the final design for the landing gear. The full NX10 model van be found here.

Planning

Week 4

• Reformulate Idea and specify the exact approach
• Rewrite wiki
• Finish Phase 1
  • Concepts and Choice
• Write State of the art Part 1
  • Supports Phase 1 of the design

Carnaval holiday

• Finish Phase 2
  • Concepts and Choice
• Write State of the Art Part 2
  • Supports Phase 2 of the design
• Finish Phase 3
  • Concepts and Choice
• Write State of the Art Part 3
  • Supports Phase 3 of the design

Week 5

• Finish Phase 4
  • Concepts and Choice
• Write State of the Art Part 4
  • Supports Phase 4 of the design
• Start work on the design

Week 6

• Continue working on the design
• Add finished aspects to the wiki

Week 7

• Finish Design
• Finish sections design wiki
• Write Conclusion, Reflection and Discussion
• Prepare presentation
• Finalize the wiki

Week 8

• Presentation
  
• Hand in report
  

State of the art

Pathfinding through rough terrain

Path planning for robots can be done in multiple ways, but finding the right choice for rescue operations can prove cumbersome. Research has been done to improve the path planning in regards to time planning and determining if the path taken can be completed. One such research is using genetic algorithms to determining a path as shown in source [3]. This does, however, have the drawback as it assumes to know what terrain is difficult to traverse and what isn't. However, this method is able to deal with unexpected situations and plans a new path that is close to optimal to reach its goal.

Another method is to evaluate the chance the robot will tilt when moving through the disaster area [4]. This is done by determining the height of the area using sensors and constructing a height gradient. The robot can then decide on a path through this gradient after nodes have been set, it takes into account the length of the path and the chance of tilting over. This method is ideal for small case areas but would need some considerable computation power to reliably do this continuously.

A variation on this idea is to change the configuration of actuators depending on the terrain [2]. This combines the path planning of the previous idea with additional functionality to further decrease the chance of tipping over. This can, therefore, be added as an extra to existing robots, given that it knows what the path will be like when moving towards it.

Using deep reinforcement learning is also an option for terrain navigation [5]. This method uses an elevation map as well and can learn what route it should take to reach the goal. This can then be applied to a robot and it should select a successful route, it could even learn if it makes a mistake. This aspect of self-improvement is unique to deep learning.

The final option that was researched is the option of using a guidance system that will guide a robot through dangerous areas [1]. This guidance system can use a multitude of lightweight sensors that can be placed all around the area. These will then connect with the main network and determine what areas are hazardous. This system is not ideal to move around obstacles. It is however useful in finding survivors as this is another functionality of this design.

Increasing travel time of a drone

There are multiple ways to charge a drone. The main way to do it would be to allow the feet of the drone to be conductive, and for the plateau where the drone rests to also be conductive and connected to a battery [14]. This allows the drone to charge without trouble. Another option with charging is by using the wireless charging method [8]. This method uses electromagnetic fields to generate a current in a coil present in the drone.

Changing a battery is also a viable option when trying to increase the flight time of a drone. By building a system that can handle the batteries, and have the possibility of a drone landing on top of it. It is possible to have a functioning battery replacement unit [9][10][11][12]. This is, however, dependant on the way the drone is built, and if on the orientation of the drone with respect to the base. If both of these are well suited for the base, the batteries can be replaced with a 100% success rate.

Refuelling is also a possibility, over the last few years more progress has been made for autonomous refuelling in flight [13]. These systems can properly refuel flying UAVs for continued function. This could, however, just as easily be adapted to a ground based vehicle or landed drone.

Reflection

Conclusion

Discussion

Sources

• [1] Aliali, S., & Benchaiba, M. (2018). Safe route guidance of rescue robots and agents based on hazard areas dissemination. Proceedings of the 2017 International Conference on Mathematics and Information Technology, ICMIT 2017, 2018–Janua, 29–37. https://doi.org/10.1109/MATHIT.2017.8259692
• [2] Brunner, M., Bruggemann, B., & Schulz, D. (2012). Motion planning for actively reconfigurable mobile robots in search and rescue scenarios. 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2012, 00(c), 2–7. https://doi.org/10.1109/SSRR.2012.6523896
• [3] Fries, T. P. (2019). Autonomous Robot Navigation in Diverse Terrain Using a Fuzzy Evolutionary Technique. IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 1, 5618–5623. https://doi.org/10.1109/iecon.2018.8591210
• [4] Ohki, T., Nagatani, K., & Yoshida, K. (2010). Safety path planning for mobile robot on rough terrain considering instability of attitude maneuver. 2010 IEEE/SICE International Symposium on System Integration: SI International 2010 - The 3rd Symposium on System Integration, SII 2010, Proceedings, 55–60. https://doi.org/10.1109/SII.2010.5708301
• [5] Zhang, K., Niroui, F., Ficocelli, M., & Nejat, G. (2018). Robot Navigation of Environments with Unknown Rough Terrain Using deep Reinforcement Learning. 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2018. https://doi.org/10.1109/SSRR.2018.8468643
• [6] Santoso, F., Garratt, M. A., Anavatti, S. G., & Petersen, I. (2018). Robust Hybrid Nonlinear Control Systems for the Dynamics of a Quadcopter Drone. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–13. https://doi.org/10.1109/TSMC.2018.2836922
• [7] Turns, S. (2006). Thermodynamics - Concepts and Applications. New York, NY: Cambridge University Press.
• [8] Choi, C. H., Jang, H. J., Lim, S. G., Lim, H. C., Cho, S. H., & Gaponov, I. (2017). Automatic wireless drone charging station creating essential environment for continuous drone operation. 2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016, 132–136. https://doi.org/10.1109/ICCAIS.2016.7822448
• [9] Fujii, K., Higuchi, K., & Rekimoto, J. (2013). Endless flyer: A continuous flying drone with automatic battery replacement. Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013, 216–223. https://doi.org/10.1109/UIC-ATC.2013.103
• [10] Lee, D., Zhou, J., & Tze, W. (2015). Autonomous Battery Swapping System for Quadcopter. In 2015 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 118–124). Denver, Colorado: IEEE. https://doi.org/10.1109/ICUAS.2015.7152282
• [11] Liu, Z., Leo, D., Zhao, H., Wang, Z., & Liu, X.-Q. (2017). QUADO: and Autonomous Recharge System for Quadcopter. In 2017 IEEE 8th International Conference on CIS & RAM (pp. 7–12). Ningbo, China. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8274740
• [12] Swieringa, K. A., Hanson, C. B., Richardson, J. R., White, J. D., Hasan, Z., Qian, E., & Girard, A. (2010). Autonomous Battery Swapping System for Small-Scale Helicopters. In 2010 IEEE International Conference on Robotics and Automation (pp. 3335–3340). Anchorage, Alaska. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5509165
• [13] Mart, C., Richardson, T., & Campoy, P. (2013). Towards Autonomous Air-to-Air Refuelling for UAVs Using Visual Information. In 2013 IEEE International Conference on Robotics and Automation (ICRA) (pp. 5756–5762). Karisruhe, Germany. https://doi.org/10.1109/ICRA.2013.6631404
• [14] Costea, I. M., & Plesca, V. (2018). Automatic battery charging system for electric powered drones. In 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME) (pp. 377–381). Iasi, Romania. Retrieved from https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8599208
• [15] Baluta, S. 2015-10-30. How much power is needed to hover?. Retrieved from http://www.starlino.com/power2thrust.html
• [16] Determining Charge Time. Retrieved from http://batteriesbyfisher.com/determining-charge-time
• [17] Flisom, Flexible solar modules. Retrieved from https://flisom.com/
• [18] eFlex 0.8m – for Buildings & Mobility. Retrieved from https://flisom.com/wp-content/uploads/2019/01/Datasheet_eFlex_0.8m_rev.pdf
• [19] Team Droneii. 2017-06-06. Drone Energy Sources – Pushing the Boundaries of Electric Flight. Retrieved from https://www.droneii.com/drone-energy-sources
• [20] What about bying horsepower by weight?. Retrieved from http://www.nauticalweb.com/info/motore/weight_e.htm
• [21] Baker, S., Soccol, D., Postula, A., & Srinivasan, M. V. (2013). Passive landing gear using coupled mechanical design. Australasian Conference on Robotics and Automation, ACRA, 2–4. Retrieved from http://www.araa.asn.au/acra/acra2013/papers/pap175s1-file1.pdf.
• [22] Zhang, K., Grimm, M., Tzoumanikas, D., Leutenegger, S., Chermprayong, P., Smentoch, M., … Kovac, M. (2019). Bioinspired design of a landing system with soft shock absorbers for autonomous aerial robots. J Field Robotics, 36(October 2017), 230–251. https://doi.org/10.1002/rob.21840

Old Sections

• Old State of the art
• Averages of propeller specifications and old options data