Using Contact to Increase Robot Performance for Glovebox D&D Tasks

TL;DR

This paper introduces a contact-implicit motion planning framework enabling robots to naturally interact with their environment, maintain stability, and perform manipulation tasks in constrained glovebox decommissioning scenarios.

Contribution

The novel framework allows robots to autonomously make and break contacts using complementarity constraints, improving stability and manipulation in complex environments.

Findings

The framework enables balance through support forces during manipulation.

Simulation results show improved stability and task performance.

Supports heavy object manipulation in constrained environments.

Abstract

Glovebox decommissioning tasks usually require manipulating relatively heavy objects in a highly constrained environment. Thus, contact with the surroundings becomes inevitable. In order to allow the robot to interact with the environment in a natural way, we present a contact-implicit motion planning framework. This framework enables the system, without the specification in advance of a contact plan, to make and break contacts to maintain stability while performing a manipulation task. In this method, we use linear complementarity constraints to model rigid body contacts and find a locally optimal solution for joint displacements and magnitudes of support forces. Then, joint torques are calculated such that the support forces have the highest priority. We evaluate our framework in a 2.5D, quasi-static simulation in which a humanoid robot with planar arms manipulates a heavy object. Our results suggest that the proposed method provides the robot with the ability to balance itself by generating support forces on the environment while simultaneously performing the manipulation task.