Multilevel Humanlike Motion Planning for Mobile Robots in Complex Indoor Environments

摘要

In this paper, a multilevel humanlike motion planning approach is proposed for indoor mobile robots. Compared with existing approaches, the novelty of this paper is twofold: 1) the proposed path planning framework is multilevel and humanlike to ensure both foreseeability and flexibility, wherein functions of human brain, eyes, and legs are corresponding to global path planning, sensor-level path planning, and action-level path planning, respectively, and 2) along the planned path, a new velocity-adjustable trajectory planning algorithm is put forward which is provably complete and time optimal considering multiple constraints from both the robot and the environment. Experimental results show that the proposed approach has a better performance in terms of efficiency, smoothness, foreseeability, and flexibility, and autonomous navigation is realized in largescale, dynamic, partially unknown, and unstructured indoor environments.Note to Practitioners-This paper was motivated by the motion planning problem for mobile robots in complex indoor environments. Existing approaches generally implement a two-level planning scheme, namely, global path planning and local trajectory planning. This paper proposes a novel multilevel humanlike motion planning approach, which is able to guarantee efficiency, smoothness, foreseeability, and flexibility in the presence of kinematic and environmental constraints. Experimental results show that autonomous navigation with the proposed approach is realized in large-scale, dynamic, partially unknown, and unstructured indoor environments, but human motion prediction has not yet been incorporated. In the future research, we will concentrate on human-robot interaction and environment-adaptive planning.