ARSOn: A Robotic Search Optimization

ARSOn: A Robotic Search Optimization

Hulshof, J.

De Schutter, B. (mentor)

Mechanical, Maritime and Materials Engineering

BioMechanical Engineering

BMD

2013-06-13

Robotic search is a very active field of research, and especially search with multiple robots is of high interest. A swarm of robots equipped with sensors could be used in a variety of useful settings such as border patrol, monitoring water quality of the ocean with underwater robots, or – in case of the FireSwarm project – for finding dune fires with Unmanned Aerial Vehicles (UAVs). This thesis was assigned by the research company Almende B.V. and is related to the FireSwarm project. The main idea behind projects like FireSwarm is that a large group of cheap UAVs with cheap less reliable sensors can search more effectively than one expensive UAV with more reliable sensors. Therefore, the main focus in this thesis is on the development of efficient robotic search strategies for different sensor representations. The robotic search problem that is defined in this thesis is the problem of finding an important point (a fire) in a predefined search area as fast as possible with autonomous UAVs. Herein, a clear distinction is made between search with perfect (deterministic) sensors and search with more realistic and imperfectly (stochastically) modeled sensors. The modeling of the fire sensors is very general in order to be able to represent any kind of (fire) sensor. This is achieved by, instead of qualitatively modeling the effect of each factor on the fire sensor, modeling the sensor as a simple stochastic process. Two main conclusions can be drawn from the tests performed in this thesis. First, the optimal strategy for search with a single UAV, with perfect deterministic sensors, is shown to be a predefined sweeping path. Furthermore, the methods proposed in this thesis for predefining a search path for multiple UAVs with deterministic sensors resulted in near- optimal performance. Secondly, it is concluded that for increasingly stochastic sensors, the predefined sweeping path is easily outperformed by the simple greedy strategy proposed in this thesis. So, because of the general representation of the search problem, the results found in this thesis cannot only contribute to the development of fast efficient algorithms for the FireSwarm project but also to other real-world (robotic) search problems.

Robotic Search
UAVs
Multiple Robots
Stochastic sensors
FireSwarm

http://resolver.tudelft.nl/uuid:0b6bfe19-8687-4f03-a3c9-5392ac0fac51

Student theses

master thesis

(c) 2013 Hulshof, J.