Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation
This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC)...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/22/7306 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850147118828748800 |
|---|---|
| author | Tianxiang Chen Yipeng Huangfu Sutthiphong Srigrarom Boo Cheong Khoo |
| author_facet | Tianxiang Chen Yipeng Huangfu Sutthiphong Srigrarom Boo Cheong Khoo |
| author_sort | Tianxiang Chen |
| collection | DOAJ |
| description | This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains. |
| format | Article |
| id | doaj-art-c2aa2b131c8a486d9e7401a1414989fe |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c2aa2b131c8a486d9e7401a1414989fe2025-08-20T02:27:39ZengMDPI AGSensors1424-82202024-11-012422730610.3390/s24227306Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision NavigationTianxiang Chen0Yipeng Huangfu1Sutthiphong Srigrarom2Boo Cheong Khoo3Mechanical Engineering, National University of Singapore, Singapore 117576, SingaporeMechanical Engineering, National University of Singapore, Singapore 117576, SingaporeMechanical Engineering, National University of Singapore, Singapore 117576, SingaporeMechanical Engineering, National University of Singapore, Singapore 117576, SingaporeThis article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains.https://www.mdpi.com/1424-8220/24/22/7306quadruped robot dogmodel predictive controlwhole-body controlmulti-level trajectory plannerpath planningdepth vision-based navigation |
| spellingShingle | Tianxiang Chen Yipeng Huangfu Sutthiphong Srigrarom Boo Cheong Khoo Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation Sensors quadruped robot dog model predictive control whole-body control multi-level trajectory planner path planning depth vision-based navigation |
| title | Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation |
| title_full | Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation |
| title_fullStr | Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation |
| title_full_unstemmed | Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation |
| title_short | Path Planning and Motion Control of Robot Dog Through Rough Terrain Based on Vision Navigation |
| title_sort | path planning and motion control of robot dog through rough terrain based on vision navigation |
| topic | quadruped robot dog model predictive control whole-body control multi-level trajectory planner path planning depth vision-based navigation |
| url | https://www.mdpi.com/1424-8220/24/22/7306 |
| work_keys_str_mv | AT tianxiangchen pathplanningandmotioncontrolofrobotdogthroughroughterrainbasedonvisionnavigation AT yipenghuangfu pathplanningandmotioncontrolofrobotdogthroughroughterrainbasedonvisionnavigation AT sutthiphongsrigrarom pathplanningandmotioncontrolofrobotdogthroughroughterrainbasedonvisionnavigation AT boocheongkhoo pathplanningandmotioncontrolofrobotdogthroughroughterrainbasedonvisionnavigation |