15 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
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