Details

Autor(en) / Beteiligte

• Gkouletsos, Dimitris
• Iannelli, Andrea
• de Badyn, Mathias Hudoba
• Lygeros, John

Titel

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.