使用 CVaR 约束的大规模机器人集群风险感知非短视运动规划器
Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints
AI总结：
提出风险感知集群运动规划器 ROVER，基于高斯混合模型宏观状态构建有限时模型预测控制问题，通过施加 CVaR 约束确保避障，并利用解析表达式和序列线性规划实现高效求解，有效提升了大规模集群的灵活性、可扩展性与安全性。
摘要：
集群机器人因其能够完成复杂且同步的任务而备受关注。现有的集群机器人系统运动规划方法主要在可扩展性和安全性保证方面面临困难。为了解决这些局限性，我们提出了一种使用条件风险价值的风险感知集群运动规划器（ROVER），它能够系统地引导大规模集群穿越杂乱环境，同时确保安全。ROVER 构建了一个基于由高斯混合模型（GMM）表示的机器人集群宏观状态的有限时模型预测控制（FTMPC）问题，并集成了条件风险价值（CVaR）以确保避免碰撞。ROVER 的关键组件是在 FTMPC 中对集群 GMM 与障碍物之间的符号距离函数分布施加 CVaR 约束，以强制实现避障。利用本工作中推导的 GMM 的 CVaR 解析表达式，我们开发了一种计算高效的解决方案，通过序列线性规划求解非线性约束的 FTMPC。仿真以及与代表性基准方法的比较证明了 ROVER 在灵活性、可扩展性和风险缓解方面的有效性。
英文摘要：
Swarm robotics has garnered significant attention due to its ability to accomplish elaborate and synchronized tasks. Existing methodologies for motion planning of swarm robotic systems mainly encounter difficulties in scalability and safety guarantee. To address these limitations, we propose a Risk-aware swarm mOtion planner using conditional ValuE at Risk (ROVER) that systematically navigates large-scale swarms through cluttered environments while ensuring safety. ROVER formulates a finite-time model predictive control (FTMPC) problem predicated upon the macroscopic state of the robot swarm represented by a Gaussian Mixture Model (GMM) and integrates conditional value-at-risk (CVaR) to ensure collision avoidance. The key component of ROVER is imposing a CVaR constraint on the distribution of the Signed Distance Function between the swarm GMM and obstacles in the FTMPC to enforce collision avoidance. Utilizing the analytical expression of CVaR of a GMM derived in this work, we develop a computationally efficient solution to solve the non-linear constrained FTMPC through sequential linear programming. Simulations and comparisons with representative benchmark approaches demonstrate the effectiveness of ROVER in flexibility, scalability, and risk mitigation.
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