An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
This addresses the need for efficient online planning for robotic teams in scientific and industrial applications, though it is incremental as it extends a single-vehicle heuristic to multi-vehicle scenarios.
The paper tackles the problem of online, resource-constrained multi-vehicle sensing mission planning by proposing a Genetic Algorithm heuristic for the Correlated Team Orienteering Problem, achieving solutions within 5% of optimal and being at least 300 times faster, with execution under a second for online use.
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.