Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilistic safety guarantees by imposing an upper bound on the collision probability of the planned trajectory. Yet, this results in overly conservative behavior on the grounds that the gap between the obtained risk and the specified upper limit is not explicitly restricted. To address this issue, we propose a real-time capable approach to quantify the risk associated with planned trajectories obtained from multiple probabilistic planners, running in parallel, with different upper bounds of the acceptable risk level. Based on the evaluated risk, the least conservative plan is selected provided that its associated risk is below a specified threshold. In such a way, the proposed approach provides probabilistic safety guarantees by attaining a closer bound to the specified risk, while being applicable to generic uncertainties of moving obstacles. We demonstrate the efficiency of our proposed approach, by improving the performance of a state-of-the-art probabilistic planner, in simulations and experiments using a mobile robot in an environment shared with humans.

翻译：在具有不确定动态的拥挤场景中进行规划时，平衡安全性与效率极具挑战性，因为既需完成机器人任务，又须避免任何安全违规。通常，规划问题中会引入机会约束，通过对规划轨迹的碰撞概率施加上限来提供概率安全保证。然而，这会导致过于保守的行为，因为所得风险与指定上限之间的差距未受到明确限制。为解决这一问题，我们提出一种实时计算方法，用于量化由多个并行运行的概率规划器（各自采用不同可接受风险水平上限）所生成规划轨迹的相关风险。基于评估的风险，只要其相关风险低于指定阈值，就选择最不保守的方案。通过这种方式，所提方法在实现对指定风险更紧密逼近的同时，提供了概率安全保证，且适用于移动障碍物的一般不确定性。我们通过改进一种最先进概率规划器的性能，在共享人类环境的移动机器人仿真与实验中，证明了所提方法的高效性。