Safe Robotic Grasping: Minimum Impact-Force Grasp Selection

TL;DR

This paper proposes a method for selecting robotic grasps that minimize impact forces during collisions, enhancing safety and robot longevity in hazardous environments by integrating augmented dynamics models with grasp planning.

Contribution

It introduces a novel approach combining augmented robot-object dynamics and effective mass concepts with grasp and trajectory planning for safer post-grasp motions.

Findings

Effective in reducing impact forces in simulated collisions.

Demonstrated safety improvements on real robotic systems.

Compatible with existing grasp and trajectory planning methods.

Abstract

This paper addresses the problem of selecting from a choice of possible grasps, so that impact forces will be minimised if a collision occurs while the robot is moving the grasped object along a post-grasp trajectory. Such considerations are important for safety in human-robot interaction, where even a certified "human-safe" (e.g. compliant) arm may become hazardous once it grasps and begins moving an object, which may have significant mass, sharp edges or other dangers. Additionally, minimising collision forces is critical to preserving the longevity of robots which operate in uncertain and hazardous environments, e.g. robots deployed for nuclear decommissioning, where removing a damaged robot from a contaminated zone for repairs may be extremely difficult and costly. Also, unwanted collisions between a robot and critical infrastructure (e.g. pipework) in such high-consequence environments can be disastrous. In this paper, we investigate how the safety of the post-grasp motion can be considered during the pre-grasp approach phase, so that the selected grasp is optimal in terms applying minimum impact forces if a collision occurs during a desired post-grasp manipulation. We build on the methods of augmented robot-object dynamics models and "effective mass" and propose a method for combining these concepts with modern grasp and trajectory planners, to enable the robot to achieve a grasp which maximises the safety of the post-grasp trajectory, by minimising potential collision forces. We demonstrate the effectiveness of our approach through several experiments with both simulated and real robots.