Realtime Trajectory Smoothing with Neural Nets

TL;DR

This paper introduces a neural network-based realtime trajectory smoothing method for industrial robots, enabling quick, smooth motion adjustments in dynamic environments to improve safety and efficiency.

Contribution

It presents a novel neural network approach for fast trajectory smoothing in realtime, integrated into a vision-motion planning loop for industrial robots.

Findings

Trajectory smoothing within 200 ms on a GPU

Effective handling of dynamic obstacles in real time

Enhanced robot motion safety and efficiency

Abstract

In order to safely and efficiently collaborate with humans, industrial robots need the ability to alter their motions quickly to react to sudden changes in the environment, such as an obstacle appearing across a planned trajectory. In Realtime Motion Planning, obstacles are detected in real time through a vision system, and new trajectories are planned with respect to the current positions of the obstacles, and immediately executed on the robot. Existing realtime motion planners, however, lack the smoothing post-processing step -- which are crucial in sampling-based motion planning -- resulting in the planned trajectories being jerky, and therefore inefficient and less human-friendly. Here we propose a Realtime Trajectory Smoother based on the shortcutting technique to address this issue. Leveraging fast clearance inference by a novel neural network, the proposed method is able to consistently smooth the trajectories of a 6-DOF industrial robot arm within 200 ms on a commercial GPU. We integrate the proposed smoother into a full Vision--Motion Planning--Execution loop and demonstrate a realtime, smooth, performance of an industrial robot subject to dynamic obstacles.