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

Multi-robot mission optimisation : an online approach for optimised, long range inspection and sampling missions

Mission execution optimisation is an essential aspect for the real world deployment of robotic systems. Execution optimisation can affect the outcome of a mission by allowing longer missions to be executed or by minimising the execution time of a mission. This work proposes methods for optimising inspection and sensing missions undertaken by a team of robots operating under communication and budget constraints. Regarding the inspection missions, it proposes the use of an information sharing architecture that is tolerant of communication errors combined with multirobot task allocation approaches that are inspired by the optimisation literature. Regarding the optimisation of sensing missions under budget constraints novel heuristic approaches are proposed that allow optimisation to be performed online. These methods are then combined to allow the online optimisation of long-range sensing missions performed by a team of robots communicating through a noisy channel and having budget constraints. All the proposed approaches have been evaluated using simulations and real-world robots. The gathered results are discussed in detail and show the benefits and the constraints of the proposed approaches, along with suggestions for further future directions

Distributed multi-AUV cooperation methods for underwater archaeology

Abstract—Autonomous Underwater Vehicles (AUVs) are a useful tool for science and industry. They significantly reduce the risk to humans in operations in hazardous and high cost situations. The use of multiple AUVs can enhance the operational capabilities by introducing specialisation of AUV capabilities and parallelising task execution. The coordination of the multi-AUV team requires communication among its members. Underwater communications are low bandwidth, high latency and error prone. This paper studies different task allocation strategies for an underwater archaeological inspection scenario under communication constraints. Three different distributed methods are implemented and compared in simulation. The first is a greedy allocation method used as a baseline for comparison. The second is a k-Means based formulation aiming to balance the load among the robots. The third is the linear programming formulation of the multiple travelling salesmen problem. Results are analysed in the scope of mission completion time and the distance travelled by the robots. Results indicate that the k-Means method performs better when communication error rates are lower, while the mTSP method performs better when communication error rates are higher. I