期刊
ROBOTICS AND AUTONOMOUS SYSTEMS
卷 115, 期 -, 页码 174-193出版社
ELSEVIER
DOI: 10.1016/j.robot.2018.11.017
关键词
Motion planning; Kinodynamic; Real-time; Obstacle avoidance; Quadrotor; Unmanned aerial vehicle; Machine learning; Human-robot interaction
资金
- United Technologies Research Center through the UTRC-2 Graduate Fellowship in Aerospace Systems
The objective of this paper is to present a full-stack, real-time motion planning framework for kinodynamic robots and then show how it is applied and demonstrated on a physical quadrotor system operating in a laboratory environment. The proposed framework utilizes an offline-online computation paradigm, neighborhood classification through machine learning, sampling-based motion planning with an optimal cost distance metric, and trajectory smoothing to achieve real-time planning for aerial vehicles. This framework accounts for dynamic obstacles with an event-based replanning structure and a locally reactive control layer that minimizes replanning events. The approach is demonstrated on a quadrotor navigating moving obstacles in an indoor space and stands as, arguably, one of the first demonstrations of full-online kinodynamic motion planning, with execution cycles of 3 Hz to 5 Hz. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory. (C) 2019 Elsevier B.V. All rights reserved.
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