Safe Motion Planning for a Mobile Robot Navigating in Environments Shared with Humans
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In this paper, a robot navigating an environment shared with humans is considered, and a cost function that can be exploited in RRT^X, a randomized sampling-based replanning algorithm that guarantees asymptotic optimality, to allow for a safe motion is proposed. The cost function is a path length weighted by a danger index based on a prediction of human motion performed using either a linear stochastic model, assuming constant longitudinal velocity and varying lateral velocity, and a GMM/GMR-based model, computed on an experimental dataset of human trajectories. The proposed approach is validated using a dataset of human trajectories collected in a real world setting.
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