Unmanned vehicles are emerging as an attractive tool for persistent monitoring tasks of a given area, but need automated planning capabilities for effective unattended deployment. Such an automated planner needs to generate collision-free coverage paths by steering waypoints to locations that both minimize the path length and maximize the amount of information gathered along the path. The approach presented in this paper significantly extends prior work and handles motion uncertainty of an unmanned vehicle and the presence of obstacles by using a Markov Decision Process based approach to generate collision-free paths. Simulation results show that the proposed approach is robust to significant motion uncertainties and reduces the probability of collision with obstacles in the environment.
Physics-Aware Informative Coverage Planning for Autonomous Vehicles
2014
7 pages
Report
No indication
English
Statistical Analysis , Computers, Control & Information Theory , Navigation Systems , Human Factors Engineering , Self operation , Stochastic control , Unmanned , Vehicles , Algorithms , Artificial intelligence , Automation , Collision avoidance , Computerized simulation , Digital maps , Gaussian noise , Linearity , Markov processes , Monitoring , Multisensors , Paths , Planning , Position(location) , Probability , Robotics , Ship motion , Target detection , Uncertainty , Usv(unmanned surface vehicles) , Autonomous surface vehicles
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