Motion Planning for Industrial Mobile Robots With Closed-Loop Stability Enhanced Prediction

Abstract

Real-time, safe, and stable motion planning in co-robot systems involving dynamic human robot interaction (HRI) remains challenging due to the time varying nature of the problem. One of the biggest challenges is to guarantee closed-loop stability of the planning algorithm in dynamic environments. Typically, this can be addressed if there exists a perfect predictor that precisely predicts the future motions of the obstacles. Unfortunately, a perfect predictor is not possible to achieve. In HRI environments in this paper, human workers and other robots are the obstacles to the ego robot. We discuss necessary conditions for the closed-loop stability of a planning problem using the framework of model predictive control (MPC). It is concluded that the predictor needs to be able to detect the obstacles’ movement mode change within a time delay allowance and the MPC needs to have a sufficient prediction horizon and a proper cost function. These allow MPC to have an uncertainty tolerance for closed-loop stability, and still avoid collision when the obstacles’ movement is not within the tolerance. Also, the closed-loop performance is investigated using a notion of M-convergence, which guarantees finite local convergence (at least M steps ahead) of the open-loop trajectories toward the closed-loop trajectory. With this notion, we verify the performance of the proposed MPC with stability enhanced prediction through simulations and experiments. With the proposed method, the robot can better deal with dynamic environments and the closed-loop cost is reduced.

Robust Task Planning for Assembly Lines with Human-Robot Collaboration

“…Human-induced uncertainties affect HRC systems mainly from the safety aspect and the time efficiency aspect. While many works have addressed these uncertainties for safety [3,4,22], few has attempted to address the impacts on time efficiency caused by these uncertainties. To address this problem, we first reckon that human-induced uncertainty mainly affects t 0 and t h .…”

“…For example, task planners for human-robot collaboration (HRC) systems in assembly lines [1,2] need to decide in real time how to assign different jobs to both human workers and robots to minimize task completion time. These HRC applications, however, raise three challenges for robotic systems: 1) predicting humans' actions and intentions [3][4][5], 2) taking humans' actions into account in the planning problem [6], and 3) designing a computationally efficient planner.…”

Autonomous Vehicle Parking in Dynamic Environments: An Integrated System with Prediction and Motion Planning

“…Motion prediction is crucial because it determines the safety constraints of the planning modules and thus the feasibility and smoothness of the motion plan [3]. Particularly, an accurate short-term motion prediction enables the AV to plan and react safely to the OVs; whereas long-term plan/mode prediction allows the AV to plan more efficiently and smoothly.…”