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Updated: Dec 6, 2025

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

A Deformable Configuration Planning Framework for a Parallel Wheel-Legged Robot Equipped with Lidar.

Fei Guo^1,2,3, Shoukun Wang^1,2,3, Binkai Yue^1,2,3

¹School of Automation, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.

Sensors (Basel, Switzerland)

|October 6, 2020

Summary

This study introduces an optimization-driven planning framework for wheel-legged hybrid robots (WLHRs). The framework enhances obstacle negotiation by dynamically adjusting robot configuration for improved adaptability in complex environments.

Keywords:

motion planning obstacle negotiation parallel mechanism trajectory optimization wheel-legged hybrid robot

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Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Wheel-legged hybrid robots (WLHRs) offer enhanced adaptability over traditional legged or wheeled robots.
Existing WLHRs can adjust height and wheelbase for diverse terrains.
Optimizing deformable configurations is key to maximizing WLHR capabilities.

Purpose of the Study:

To present an optimization-driven planning framework for WLHRs with parallel Stewart mechanisms.
To improve obstacle negotiation and path planning in confined spaces.
To leverage the deformable nature of WLHRs for enhanced environmental adaptation.

Main Methods:

Abstracting the WLHR as a deformable bounding box for planning.
Utilizing a pre-calculated signed distance field (SDF) mapping with lidar point cloud data.
Employing a KD-tree-based point cloud fusion approach.
Implementing a covariant gradient optimization method for trajectory generation.

Main Results:

Generated smooth, deformable-configuration, and collision-free trajectories in confined spaces.
Demonstrated effective obstacle-avoiding actions like adjusting foothold polygon and trunk height.
Achieved higher success rates by deforming robot configuration rather than solely bypassing obstacles.

Conclusions:

The proposed optimization-driven framework effectively enhances the obstacle negotiation and path planning capabilities of WLHRs.
The methodology proves practical for real-world applications, showcasing adaptability in various terrains.
Dynamically adjusting robot configuration offers a significant advantage for mobile robotic exercises in challenging environments.