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Related Experiment Video

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

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Published on: October 14, 2017

Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation.

Tianxiang Chen¹, Yipeng Huangfu¹, Sutthiphong Srigrarom¹

¹Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This study enhances robot dog navigation with advanced control and vision systems, improving stability and path planning accuracy on challenging terrains for real-world applications.

Keywords:

SLAM depth vision-based navigation map generation model predictive control multi-level trajectory planner path planning quadruped robot dog rough terrain whole-body control

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Autonomous navigation in complex terrains remains a significant challenge for quadruped robots.
Existing systems often struggle with dynamic environments and precise path planning.
The need for robust and adaptable navigation solutions is critical for applications like disaster response.

Purpose of the Study:

To enhance autonomous navigation and obstacle avoidance for quadruped robot dogs.
To develop a robust path planning algorithm for robot dogs operating on rough terrain.
To improve robot stability, maneuverability, and trajectory accuracy in diverse environments.

Main Methods:

Integration of Model Predictive Control (MPC) and Whole-Body Control (WBC) with depth vision (Intel RealSense D435i).
Customization of the EGO-Planner for dynamic terrain trajectory optimization and implementation of a multi-body dynamics model.
Development of a binocular vision tracking method for path planning and motion control, fusing sensor data with a kinematics model.

Main Results:

The RGB-D vision system demonstrated superior velocity stability (20% error reduction) and trajectory accuracy (10% improvement) compared to SLAM systems.
Smoother navigation achieved with 15% fewer path planning iterations and a 30% faster success rate recovery in challenging environments.
Successful navigation in simulated urban disaster scenarios, validating the system's potential for emergency response.

Conclusions:

The enhanced autonomous navigation system significantly improves quadruped robot performance on varied and challenging terrains.
The developed binocular vision-based path planning offers a robust framework for future robot dog navigation.
These advancements pave the way for more capable robots in complex, dynamic, and hazardous environments.