Strategy-based robotic item picking from shelves

Automating item picking in the e-commerce warehouse is pressing but challenging, due to a massive variety of items, tight environmental constraints and item location uncertainty. In this paper, we present an effective and efficient strategy-based planning approach to implement the robotic picking from shelves for e-commerce. Making full advantage of a gripper with multiple securing methods, differentiated strategies are modeled as picking primitives with different securing methods. A strategy generator is proposed to produce feasible potential pickings as quickly and as successfully as possible. A strategy evaluator considering reachability, collision, object-bias preference and the securing performance is also presented for ranking the picking strategies. Experiments were conducted to validate that the robotic picker is able to plan a picking strategy within 2 ms and pick daily items from the shelves with an average success rate of 68%.