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Updated: Jan 9, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Human-Risk-Aware Safe Path Planning Based on Reinforcement Learning for Autonomous Mobile Robots.

Zhongjie Long¹, Xianbo Zhang¹, Jian Mi²

¹College of Mechanical and Electrical Engineering, Key Laboratory of the Ministry of Education for Modern Measurement and Control Technology, Beijing Information Science & Technology University, Beijing 102206, China.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This summary is machine-generated.

This study introduces a reinforcement learning path planning algorithm for robots in human environments. It significantly reduces conflicts and improves success rates by predicting human movement uncertainties.

Keywords:

human-shared environments mobile robot safe path planning stochastic risk evaluation uncertainties

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Area of Science:

Robotics
Artificial Intelligence
Human-Robot Interaction

Background:

Mobile robots operate in human-shared spaces with unpredictable human movement.
Ensuring safety and efficiency in robot path planning amidst human stochasticity is a significant challenge.

Purpose of the Study:

To develop a reinforcement learning-based path planning algorithm for mobile robots in human-shared environments.
To account for human-related uncertainties at the planning stage for safer robot navigation.

Main Methods:

A Markov decision process learner generates candidate paths.
A path eliminator module ensures diversity using a novel metric.
A Monte Carlo-simulated human risk predictor selects the safest path.

Main Results:

The proposed method significantly reduces conflicts compared to A*, MDP, and RRT.
Task success rates are substantially improved across various settings.
Demonstrates effectiveness in high-density scenarios with multiple humans.

Conclusions:

The integrated algorithm enables safe and efficient robot trajectory generation.
It effectively handles stochastic human behavior without constant re-planning.
The approach offers a robust solution for real-world human-robot collaboration.