Optimization-Based Framework for Excavation Trajectory Generation

作者：Yajue Yang, Pinxin Long, Xibin Song, Jia Pan, Liangjun Zhang · 发表于：IEEE Robotics and Automation Letters · 年份：2021 · DOI：10.1109/lra.2021.3058071 · 被引用次数：60 · 研究领域：Robotic Path Planning Algorithms、Robot Manipulation and Learning、Hydraulic and Pneumatic Systems

In thisletter, we present a novel optimization-based framework for autonomous excavator trajectory generation under task-specific constraints. Traditional excavation trajectory generators over-simplify the geometric trajectory parameterization thereby limiting the space for optimization. To expand the search space, we formulate a generic task specification for excavation by constraining the instantaneous motion of the bucket and adding a target-oriented constraint to control the amount of excavated soil. The trajectory is represented with a waypoint interpolating spline. Time intervals between waypoints are relaxed as variables to facilitate generating the time-optimal trajectory in one stage. Experiments on a real robot platform demonstrate that our method is adaptive to different terrain shapes and outperforms other optimal path planners in terms of the minimum joint length and minimum travel time.