用于多智能体探索的分散轨迹optimization
Decentralized trajectory optimization for multi-agent exploration
摘要：
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，即代理在每个区域花费的时间的一部分，这是成比例的概率信息密度函数。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。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智能体的轨迹。为了解决基本的最优控制问题，二阶下降迭代法加上一个投影算子的形式的最优反馈控制器。详尽的数值分析表明，多智能体解决方案允许一个更有效的探索方面的完成任务的时间和控制能源分配，利用代理之间的合作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
