Decentralized trajectory optimization for multi-agent exploration

TL;DR

This paper introduces a decentralized ergodic trajectory planning algorithm for multi-agent exploration that improves efficiency and energy use through limited communication and collaborative optimization.

Contribution

It presents a novel decentralized approach for multi-agent ergodic trajectory planning with limited communication constraints and an efficient optimal control solution.

Findings

Multi-agent collaboration reduces exploration time.

The approach improves control energy efficiency.

Numerical results validate the effectiveness of the method.

Abstract

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 paper, 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.