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Value Iteration Networks with Double Estimator for Planetary Rover Path Planning
2021
Sensors
Path planning technology is significant for planetary rovers that perform exploration missions in unfamiliar environments. In this work, we propose a novel global path planning algorithm, based on the value iteration network (VIN), which is embedded within a differentiable planning module, built on the value iteration (VI) algorithm, and has emerged as an effective method to learn to plan. Despite the capability of learning environment dynamics and performing long-range reasoning, the VIN
doi:10.3390/s21248418
pmid:34960508
pmcid:PMC8709000
fatcat:iyphuncbpzbcdpgordioqdtvui