Robust Locomotion Navigation in Partially Observable Environments with Safety GuaranteesDownload PDF

Jun 15, 2020 (edited Jul 09, 2020)RSS 2020 Workshop Robust Autonomy Blind SubmissionReaders: Everyone
  • Abstract: This study is working towards an integrated task and motion planning method for dynamic locomotion in partially observable environments with safety guarantees. This planning framework is composed of a symbolic task planner and a reduced-order-model-based motion planner, which are connected by a mid-level keyframe decision-maker. The mid-level keyframe decision maker generates a keyframe plan via reachability analysis and proposes a robust keyframe policy, which is used to generate low-level phase-space trajectories. The high-level task planner employs a linear temporal logic approach for a reactive game synthesis between the robot and its environment while incorporating the robust keyframe transition policies into the formal task specification design. A belief abstraction method in the task planner enables belief estimation of dynamic obstacle locations and guarantees safe locomotion with collision avoidance.
  • Keywords: Safe navigation, Dynamic locomotion, Robustness, Belief Planning, Task and motion planning
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