Motion planning for autonomous robots and vehicles in presence of uncontrolled agents remains a challenging problem as the reactive behaviors of the uncontrolled agents must be considered. Since the uncontrolled agents usually demonstrate multimodal reactive behavior, the motion planner needs to solve a continuous motion planning problem under these behaviors, which contains a discrete element. We propose a branch Model Predictive Control (MPC) framework that plans over feedback policies to leverage the reactive behavior of the uncontrolled agent. In particular, a scenario tree is constructed from a finite set of policies of the uncontrolled agent, and the branch MPC solves for a feedback policy in the form of a trajectory tree, which shares the same topology as the scenario tree. Moreover, coherent risk measures such as the Conditional Value at Risk (CVaR) are used as a tuning knob to adjust the tradeoff between performance and robustness. The proposed branch MPC framework is tested on an overtake and lane change task and a merging task for autonomous vehicles in simulation, and on the motion planning of an autonomous quadruped robot alongside an uncontrolled quadruped in experiments. The result demonstrates interesting human-like behaviors, achieving a balance between safety and performance.
翻译:由于不受控制的代理物通常表现出多式反应行为,因此,运动计划者需要在这些行为下解决一个连续的运动规划问题,其中含有一个离散元素。我们提议了一个分支模型预测控制(MPC)框架,用于规划反馈政策,以利用不受控制的代理物的被动行为。特别是,一个假想树是根据不受控制的代理物的有限政策构建的,而分支MPC则以轨迹树的形式解决反馈政策,而轨迹树与情景树具有相同的特征。此外,一致的风险措施,如风险中条件值(CVaR),被用作调整功能和稳健性之间的平衡。拟议的分支预测控制(MPC)框架在一项超载和车道变化任务上进行了测试,并在模拟中将自主飞行器合并,以及一个自主四分立机器人的动作规划,同时进行不受控制的四分立试验。结果显示了令人感兴趣的人种行为,在安全性与性之间实现平衡。