Provably Constant-time Planning and Replanning for Real-time Grasping Objects off a Conveyor Belt

TL;DR

This paper introduces a novel planning framework that guarantees constant-time motion planning and replanning for real-time grasping of objects on conveyor belts, addressing the challenges of noisy perception and fast object movement.

Contribution

The paper formalizes the class of Constant-Time Motion Planning algorithms (CTMP) and applies it to real-time grasping tasks, providing provable planning guarantees.

Findings

Introduces the CTMP class of algorithms with guaranteed planning time.

Develops a framework for real-time grasping with provable guarantees.

Addresses noisy perception and dynamic object movement in conveyor environments.

Abstract

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick and place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. Besides the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time, it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose a planning framework that meets these requirements by providing provable constant-time planning and replanning guarantees. To this end, we first introduce and formalize a new class of algorithms called Constant-Time Motion Planning algorithms (CTMP) that guarantee to plan in constant time and within a user-defined time bound. We then present our planning framework for grasping objects off a conveyor belt as an instance of the CTMP class of algorithms.