H. Huang et al., “Mechanical Search on Shelves using a Novel “Bluction” Tool,” 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 6158-6164, doi: 10.1109/ICRA46639.2022.9811622.
Abstract: Shelves are common in homes, warehouses, and commercial settings due to their storage efficiency. However, this efficiency comes at the cost of reduced visibility and accessibility. When looking from a side (lateral) view of a shelf, most objects will be fully occluded, resulting in a constrained lateral-access mechanical search problem. To address this problem, we introduce: (1) a novel bluction tool, which combines a thin pushing blade and a suction cup gripper, (2) a simulation pipeline and perception model that combine ray-casting with 2D Minkowski sums to efficiently generate target occupancy distributions, and (3) a novel search policy, which optimally reduces target object distribution support area using the bluction tool. Experimental data from 2000 simulated shelf trials and 18 trials with a physical Fetch robot suggest that a bluction tool can improve the average success rate by 26% in simulation and 67% in physical experiments over the highest-performing push-only policy.