When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning
CoRR(2023)
摘要
The hierarchy of global and local planners is one of the most commonly
utilized system designs in autonomous robot navigation. While the global
planner generates a reference path from the current to goal locations based on
the pre-built map, the local planner produces a kinodynamic trajectory to
follow the reference path while avoiding perceived obstacles. To account for
unforeseen or dynamic obstacles not present on the pre-built map, “when to
replan” the reference path is critical for the success of safe and efficient
navigation. However, determining the ideal timing to execute replanning in such
partially unknown environments still remains an open question. In this work, we
first conduct an extensive simulation experiment to compare several common
replanning strategies and confirm that effective strategies are highly
dependent on the environment as well as the global and local planners. Based on
this insight, we then derive a new adaptive replanning strategy based on deep
reinforcement learning, which can learn from experience to decide appropriate
replanning timings in the given environment and planning setups. Our
experimental results show that the proposed replanner can perform on par or
even better than the current best-performing strategies in multiple situations
regarding navigation robustness and efficiency.
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关键词
adaptive replanning strategy,navigation,autonomous,learning
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