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Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration
Ist Teil von
IEEE robotics and automation letters, 2021-10, Vol.6 (4), p.6329-6336
Ort / Verlag
Piscataway: IEEE
Erscheinungsjahr
2021
Quelle
IEEE Electronic Library (IEL)
Beschreibungen/Notizen
Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density function. In this letter, a decentralized ergodic multi-agent trajectory planning algorithm featuring limited communication constraints is proposed. The agents' trajectories are designed by optimizing a weighted cost encompassing ergodicity, control energy and close-distance operation objectives. To solve the underlying optimal control problem, a second-order descent iterative method coupled with a projection operator in the form of an optimal feedback controller is used. Exhaustive numerical analyses show that the multi-agent solution allows a much more efficient exploration in terms of completion task time and control energy distribution by leveraging collaboration among agents.