PRE2018 3 Group17
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 allows these drones to be autonomously brought back into operation can save a lot of time which the people can save elsewhere.
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 over a wider area 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. Next to that, since it is not an expensive solution, it would be much more cost effective than existing solutions such as the manually controlled drone.
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.
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.
RPCs
The RPCs for the system will be defined as follows:
Requirements
- Can swap batteries of the drones.
- Needs to be mobile.
- 25 km system range extension.
- Fully autonomous positioning and task execution.
- Return to base after task completion or when in need to service.
- Max. one minute battery swap.
- Can service 10 drones in its operation cycle (about 2 kg payload).
- Can 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.
- Low manufacturing and operation costs.
- The system should support a manual override.
- The system should be easy employable.
Constraints
- The system needs to function independently of available infrastructure.
- Can be operational on rough terrain.
Phase 1: Traversal of environment
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.
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. 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. 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.
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 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 environment 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.
Large drone with chain of recharging drones
Fuel powered large drone. Can fly for around 1 hour without being refueled or recharged itself. With a chain of refueling drones, the large drone will be refueled/recharged.
Choosing
Out of the ideas and concepts discussed, the hopping drone seems to be the best solution. it's hard to keep any of the other designs stable while keeping them in the air and resting on the ground instead of flying in the air allows the system to last much longer. Drones will have an easier time reaching a still target.
Planning
Week 3
- Reformulate Idea and specify exact approach
- Rewrite wiki
- Finish Phase 1
- Concepts and Choice
- Write State of the art Part 1
- Supports Phase 1 of design
Week 4
- Finish Phase 2
- Concepts and Choice
- Write State of the Art Part 2
- Supports Phase 2 of design
- Finish Phase 3
- Concepts and Choice
- Write State of the Art Part 3
- Supports Phase 3 of design
Observation strategy
Work on simulation
Checking the RPC’s
Analysis of decisions made for the simulation and update if needed
Update the wiki
Literature study
Use cases
Week 7
Finalize simulation
Prepare presentation
Finalize the wiki
Week 8
Presentation
Hand in report
| Week | Tasks | |||||||
| 1 | Problem-statement and objectives (Cornelis and Benn) |
State-of-art (Every member provides at least five sources) |
Users and their requirements (Diederik) |
Approach, planning, milestones, and deliverables (Laila and Joël) |
||||
| 2 | Updated problem description (Benn) |
Concrete planning for project (Benn, Laila) |
Analysis of literature sources (Joël) |
Restructure Wiki (Benn) |
Requirement analysis (Benn) |
Write down sources in APA style (Laila) |
Update wiki (Laila, Benn and Joël) |
Simulation methods (Diederik and Cornelis) |
| 3 | Observation strategy |
Analysis of decisions made for the simulation and update if needed |
Use cases |
Simulation | ||||
| 4-6 | Observation strategy |
Work on simulation |
Checking the RPC’s |
Analysis of decisions made for the simulation and update if needed |
Update the wiki |
Literature study |
||
| 7 | Finalize wiki |
Prepare presentation |
State of the art
This section will be rewritten as mentioned in the new planning
It is important to first find out what has already been done on the subject matter. This is done by looking at the state of the art research done for these rescue drones. These can then be used to either help develop the proposed solution, or to use research as an additional component that works in tangent to the solution.
Existing components of the design
The fact that drones have grown massively in popularity over the past decade is clear, but scientists have shown increased interest in drone technology as well [11]. Drones and other UAVs (Unmanned Aerial Vehicles) are now used in a wide variety of applications including the making of movies, surveillance and inspection of industrial structures that take up large amounts of space (oil pipelines, train tracks), mapping geological structures and providing food and medical relief to hard to reach areas [10]. The Delft University of technology recently developed an ambulance drone which carries several useful pieces of equipment over to the specified location, so people can get a head start on helping the person in need [16].
Understanding of how to effectively use drones in different kinds of applications is increasing rapidly, and the subject of using drones in disaster areas is no exception. Research shows that as drones became more affordable, and their technology more advanced, they are becoming increasingly suitable for implementation into disaster areas. Even if rescue personnel are not adept at using the technology, it still manages to increase their efficiency [4]. For instance, the usage of drones as cellular beacons in case cell towers no longer function is being investigated [14], and drones are being used for support and observation, including providing information about the development of forest fires, and information about collapsed buildings [5].
One part of the problem of using drones in disaster areas, namely the autonomous analysis of the images captured by the drones, has been studied extensively. Systems have been developed for detecting potential obstacles for the drones [13], recognizing to what degree a building has collapsed [4], or how to recognize different types of forest fires [15]. Similar systems exist using observation from space by satellite, but these methods often lack the resolution required to get all the information required [17]. The recognition of humans from these camera images has also been studied. This is done either by teaching a system to recognize humans from a set of test images [24], or by comparing heat signatures [26]. Since these technologies are already quite well researched, we will focus on other parts of the project, namely optimizing the search strategy of the drones.
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 [fuzzy evolutionary algorithms]. 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 [attitude maneuver]. 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 [Reconfigurable robots]. 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 [Reinforcement learning]. 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 a succesful 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 [Guidance]. 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.
Useful resources for the design
- 1 Talks about how drones can autonomously find survivors by scanning the environment. They offer a high potential for fast and efficient response during a rescue mission. What should the drone do to help the survivors. Needs to observe its environment to avoid a collision.
- 7 Talks about the feasibility of a multi-tier drone architecture over single tier drones in terms of efficiency. This increases efficiency and reduces path loss.
- 12 Mainly talks about how paths are found for drones to follow. and how trajectory planning works, uses decision making and direction of target given a path to deciding what to do.
- 14. Talks about the usage of drones as cellular network beacons in cities after some calamity. Presents a stochastic model to predict how many drones are necessary for a given situation. We could do something similar for our number.
- 18. Proposes HAC-ER, a system for cooperation between information-gathering agents and humans in disaster regions. Shows great promise, problems mainly arise due to airports not easily allowing UAVs in their active airspace.
- 22. Article discusses the benefits and drawbacks of two different communication methods for drone swarms, also explains them. Very useful and relevant for later stage of our project.
- 23. Article acts as an example of how to effectively set up a communication network for a 'swarm' of agents, how to get them to perform tasks. Super useful, but hard to follow.
- 25. Design for a fully autonomous/wireless drone charging station. Useful if we want to include a charging station in our strategy.
Complementary sources
- 1 Talks about how drones can autonomously find survivors by scanning the environment. They offer a high potential for fast and efficient response during a rescue mission. What should the drone do to help the survivors. Needs to observe its environment to avoid a collision.
- 7 Talks about the feasibility of a multi-tier drone architecture over single tier drones in terms of efficiency. This increases efficiency and reduces path loss.
- 14. Talks about the usage of drones as cellular network beacons in cities after some calamity. Presents a stochastic model to predict how many drones are necessary for a given situation. We could do something similar for our number.
- 2 Discusses failure of a drone system, espionage due to hacking, and autonomous finding of survivors. (Weinig text om er meer over te zeggen)
- 8 Discusses the useful sections of implementing drones in rescue scenarios, as well as how to manage certain aspects of it. It gives a summary of various communication aspects and issues related to their deployment.
- 9 Discussion on why opportunistic networks aren't more common in todays world
- 11 Optimization approaches for different civil applications of drones and characteristics of those types of drones, drones are extremely versatile and new uses are always found for them.
- 20. Article discusses general ethicalness of CCTV surveillance. Concludes that partially automated data analysis from these systems is more ethically preferable to manual analysis. Hard to relate to our case specifically, but could spark the discussion of ethicalness of our system.
- 21. Article discusses ethical feasibility of facial recognition systems. Seems unrelated to our project entirely.
Inaccessible sources
- 3 Presents a vision where the drones provide wireless communication between survivors and cellular infrastructure. (Geen toegang tot volledige artikel)
- 19. This article is not accessible using a TU licence. Abstract talks about integrating calculations for path planning between different scales of a system (i.e. destination of each agent vs. not crashing into each other etc.).