5 research outputs found
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning
Deploying multiple robots for target search and tracking has many practical
applications, yet the challenge of planning over unknown or partially known
targets remains difficult to address. With recent advances in deep learning,
intelligent control techniques such as reinforcement learning have enabled
agents to learn autonomously from environment interactions with little to no
prior knowledge. Such methods can address the exploration-exploitation tradeoff
of planning over unknown targets in a data-driven manner, eliminating the
reliance on heuristics typical of traditional approaches and streamlining the
decision-making pipeline with end-to-end training. In this paper, we propose a
multi-agent reinforcement learning technique with target map building based on
distributed Gaussian process. We leverage the distributed Gaussian process to
encode belief over the target locations and efficiently plan over unknown
targets. We evaluate the performance and transferability of the trained policy
in simulation and demonstrate the method on a swarm of micro unmanned aerial
vehicles with hardware experiments.Comment: 10 pages, 6 figures; preprint submitted to IJCAS; first two authors
contributed equall
Intelligent adaptive underwater sensor networks
Autonomous Underwater Vehicle (AUV) technology has reached a sufficient maturity level to be considered a suitable alternative to conventional Mine Countermeasures (MCM). Advantages of using a network of AUVs include time and cost efficiency, no personnel in the minefield, and better data collection. A major limitation for underwater robotic networks is the poor communication channel. Currently, acoustics provides the only means to send messages beyond a few metres in shallow water, however the bandwidth and data rate are low, and there are disturbances, such as multipath and variable channel delays, making the communication non-reliable. The solution this thesis proposes using a network of AUVs for MCM is the Synchronous Rendezvous (SR) method --- dynamically scheduling meeting points during the mission so the vehicles can share data and adapt their future actions according to the newly acquired information. Bringing the vehicles together provides a robust way of exchanging messages, as well as means for regular system monitoring by an operator. The gains and losses of the SR approach are evaluated against a benchmark scenario of vehicles having their tasks fixed. The numerical simulation results show the advantage of the SR method in handling emerging workload by adaptively retasking vehicles. The SR method is then further extended into a non-myopic setting, where the vehicles can make a decision taking into account how the future goals will change, given the available resource and estimation of expected workload. Simulation results show that the SR setting provides a way to tackle the high computational complexity load, common for non-myopic solutions. Validation of the SR method is based on trial data and experiments performed using a robotics framework, MOOS-IvP. This thesis develops and evaluates the SR method, a mission planning approach for underwater robotic cooperation in communication and resource constraint environment