SafeGuardPF: 保障安全的反应势场用于未知和动态环境中的移动机器人
SafeGuardPF: Safety Guaranteed Reactive Potential Fields for Mobile Robots in Unknown and Dynamic Environments
AI总结：
本文提出SafeGuardPF方法，通过将反应势场运动控制器建模为混合自动机，并利用微分动态逻辑形式化验证其安全性，确保机器人在动态环境中避免碰撞。
摘要：
自主导航在未知和动态环境中实现可靠避障仍具挑战性，尤其当存在移动障碍物时。为应对这一问题，本文采用反应势场方法，该方法在每个周期内仅需机器人相对于最近障碍物点的当前状态即可计算势场，从而更高效且适用于多智能体场景。本文的主要贡献是将反应势场运动控制器建模为混合自动机，并通过微分动态逻辑形式化验证其安全性，确保机器人在碰撞时不会发生故障，即只有在机器人静止时才可能发生碰撞。所提出控制器及验证结果通过仿真实验和Pioneer P3-AT机器人实现进行验证。
英文摘要：
An autonomous navigation with proven collision avoidance in unknown and dynamic environments is still a challenge, particularly when there are moving obstacles. A popular approach to collision avoidance in the face of moving obstacles is based on model predictive algorithms, which, however, may be computationally expensive. Hence, we adopt a reactive potential field approach here. At every cycle, the proposed approach requires only current robot states relative to the closest obstacle point to find the potential field in the current position; thus, it is more computationally efficient and more suitable to scale up for multiple agent scenarios. Our main contribution here is to write the reactive potential field based motion controller as a hybrid automaton, and then formally verify its safety using differential dynamic logic. In particular, we can guarantee a passive safety property, which means that collisions cannot occur if the robot is to blame, namely a collision can occur only if the robot is at rest. The proposed controller and verification results are demonstrated via simulations and implementation on a Pioneer P3-AT robot.
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