Safe and Efficient Robot Manipulation: Task-Oriented Environment Modeling and Object Pose Estimation

Abstract

Mobile manipulation is essential for robots to accomplish everyday household chores such as set the table. In order to successfully perform manipulation tasks, a 3-D representation of the environment and the pose of the target object are needed. In this article, we propose a systematic solution for safe and efficient robot manipulation. Innovatively, a task-oriented environment modeling strategy is presented for collision-free navigation and motion planning, which integrates the grid-based 2-D map and local real-time octree-based 3-D representation. Furthermore, we introduce a task-oriented object pose estimation approach based on the fiducial marker and ontology technology. In particular, a property-driven target object inference and pose estimation algorithm is designed, which allows the mobile robot to implement the task-oriented object manipulation. The proposed solution is evaluated through real-world experiments, where memory usage, computation time, collision checking, and object pose estimation are extensively investigated. Also, application scenarios are presented to validate the effectiveness and efficiency of our proposal.