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, mostly aerial, drones for this cause. The focus of these studies was, among other topics, (bron achter elk onderwerp)

• 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 UAVs, 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.

Old Problem Description

A lot of research has been done on the possibility of using drones to look for survivors. This field of research has had a lot of progress in recent times, but one of the most important aspects remain unsolved as of this point. This is the method of charging the drones that are searching for survivors. The typical electric drone can fly somewhere between 15-45 minutes, this is too short of a time for drones to fly all the way to their search destination, start searching and head back on time to recharge. A mobile charging platform should therefore be developed, as this would improve the chances of a rescue system using drones to work efficiently.

This system should ideally be usable in the following disasters:

• Forest fires
• Earthquakes
• Floods
• Hurricanes or other weather related disasters
• Nuclear disasters
• (Large Scale) Terror attacks

These disasters each have their own requirements and it therefore becomes unruly to try and find a solution for all of these disasters. It was therefore decided to only focus on earthquakes. The reason for this is because they are the most common natural disaster.

Solution

The entire charging platform consists of three components:

• Moving around the terrain
• Method to charge drones
• Fuel type used in charging platform
• Method to charge the charging platform

The plan is to either design these components, or to use existing ideas found in the literature.

(Oud Stuk) The entire system consists of four components:

• Finding the roads
• Finding survivors
• Efficient searching
• Optimal path for rescue operations

All of these could be projects of their own, it was therefore decided that the focus should be placed on the efficient searching aspect of the design, and testing this search by finding survivors. If these components are finished then the other tasks will also be attempted, depending on how much time is left.

Finding the survivors and placing them on a map should be the easiest part of the project, as it should only have to identify the survivor and then mark their coordinates. It should be noted that image recognition is not used for this, it will use a chance based system on if it does indeed see a survivor at a given position. This was due to image recognition being another huge part of the project and would remove focus from the specific problem. The chance based aspect was chosen to simulate the fact that image recognition might fail.

The second component is the efficient searching for roads and survivors. This is done by dividing the search strategy into two groups. One group focusses on the search for roads, and the other focusses on finding survivors. These both work in clusters of drones which will take into account where other drones are searching so they don't check the same area twice. This is the main focus of the project, and if this works sufficiently then the remaining sections will also be developed further.

Both of these components will be implemented with drones. This is due to the fact that drones can move through the air, therefore ignoring most of the bad infrastructure after a given disaster. They are also able to move through small crevices which allow them to search inside of buildings with minimal trouble. Due to the nature of them flying there will also be a smaller chance that they cause additional damage because they accidentaly hit a weak point in a construction.

RPCs

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 certain speed?).
• The vehicle should be safe for people to use.
• Low manufacturing and operation costs.
• The system should support manual override.
• The system should be easy employable.

Constraints

• The system needs to function independent of available infrastructure.
• Can be operational on rough terrain.

(old)The proposed solution when finding survivors and the cluster of drones for efficient searching both have their own requirements that should be incorporated into the design.

The total list of requirements is as follows:

• The survivors can be found through vision
• The survivors can also be found by checking high concentrations of wireless transmission in an area
• It should be able to mark the people on a map
• It should work autonomously
• The drones should not hit each other during flight
• The drones can be sent to a disaster area soon after the disaster hits
• They should check different areas with as little overlap as possible
• Individual drones should be precise enough that they can enter buildings through small holes
• The entire map can be divided into sectors, with clusters of drones being assigned to a single sector at a time
• After finishing a sector, the cluster will move to an unexplored sector
• The drones can change their target if they have searched for their targets everywhere. An example would be searching for survivors after all the roads have been mapped

Some of these can be applied to both the cluster of drones and finding the survivors.

Approach

The approach to solving this problem is simple, first, a thorough literature study will be conducted that will be used to determine the state-of-the-art regarding drone technology as well as their current use in natural disasters and regarding the current other methods of observation during natural disasters and the problems and restrictions associated with these methods. These are then neatly summarised into different sections based on their relevance and subject matter.

Based on this information, an optimal observation strategy for a certain set of defined scenario's will be designed, based on the abilities of drones and maximizing the amount of new information a single drone can deliver. To provide evidence of the working of this strategy, a simulation will be made that shows how a network of drones would operate in such a disaster area.

Depending on if enough time is left for the project, either the other two components will be modelled, or a small scale test setup is built to test the system in reality. The final report will exist of a literature study, an explanation of the optimized observation strategy, and the simulation. It will also include an expansive explanation of the choices made during each part of the project.

Planning

Week 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)

Week 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)

Week 3-6

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
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

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.).

Total list

List Here

Users

What do the users require

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.

Model

Model will be done later