论文标题
安全：通过二元性限制感应的安全随机运动计划
Safely: Safe Stochastic Motion Planning Under Constrained Sensing via Duality
论文作者
论文摘要
考虑一个在不确定的环境中运行的机器人，具有随机的动态障碍。尽管轨迹优化具有明显的好处，但由于感应和硬件限制，通常很难在每个时间步骤跟踪每个障碍。我们介绍了安全运动计划器，这是一种退化的摩恩控制框架，该框架同时综合了机器人遵循的轨迹，以及一个传感器的选择策略，可以在每个时间步骤中规定与轨迹相关的障碍，同时尊重机器人的传感约束。我们使用顺序二次编程执行运动计划，并根据与凸子问题相关的二元性信息开出障碍物的障碍。我们通过确保机器人与任何障碍物相撞的可能性在计划中的机器人轨迹的每个时间步骤中都低于规定的门槛来确保安全。我们通过软件和硬件实验证明了安全运动计划者的功效。
Consider a robot operating in an uncertain environment with stochastic, dynamic obstacles. Despite the clear benefits for trajectory optimization, it is often hard to keep track of each obstacle at every time step due to sensing and hardware limitations. We introduce the Safely motion planner, a receding-horizon control framework, that simultaneously synthesizes both a trajectory for the robot to follow as well as a sensor selection strategy that prescribes trajectory-relevant obstacles to measure at each time step while respecting the sensing constraints of the robot. We perform the motion planning using sequential quadratic programming, and prescribe obstacles to sense based on the duality information associated with the convex subproblems. We guarantee safety by ensuring that the probability of the robot colliding with any of the obstacles is below a prescribed threshold at every time step of the planned robot trajectory. We demonstrate the efficacy of the Safely motion planner through software and hardware experiments.
