Planning for target retrieval using a robotic manipulator in cluttered and occluded environments
This addresses the challenge of efficient object retrieval in robotics for applications like warehouse automation, but it is incremental as it builds on existing planning methods with specific optimizations.
The paper tackles the problem of planning for a robotic manipulator to grasp a target object in cluttered and occluded environments where no collision-free path exists, by proposing algorithms that minimize the number of objects to be relocated, resulting in a 25.1% reduction in total running time compared to a baseline in a known static environment with 10 objects.
This paper presents planning algorithms for a robotic manipulator with a fixed base in order to grasp a target object in cluttered environments. We consider a configuration of objects in a confined space with a high density so no collision-free path to the target exists. The robot must relocate some objects to retrieve the target while avoiding collisions. For fast completion of the retrieval task, the robot needs to compute a plan optimizing an appropriate objective value directly related to the execution time of the relocation plan. We propose planning algorithms that aim to minimize the number of objects to be relocated. Our objective value is appropriate for the object retrieval task because grasping and releasing objects often dominate the total running time. In addition to the algorithm working in fully known and static environments, we propose algorithms that can deal with uncertain and dynamic situations incurred by occluded views. The proposed algorithms are shown to be complete and run in polynomial time. Our methods reduce the total running time significantly compared to a baseline method (e.g., 25.1% of reduction in a known static environment with 10 objects