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.

中文翻译：

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多智能体遍历探索的分散轨迹优化

自主探索是机器人技术中越来越重要的应用。一个有前途的策略是遍历轨迹规划，其中代理在每个区域花费的时间与其概率信息密度函数成正比。在这封信中，提出了一种具有有限通信约束的分散遍历多智能体轨迹规划算法。代理的轨迹是通过优化包含遍历性、控制能量和近距离操作目标的加权成本来设计的。为了解决潜在的最优控制问题，使用了二阶下降迭代方法以及最优反馈控制器形式的投影算子。