- The paper introduces a bi-level control strategy that integrates variable damping and whole-body optimization to maintain upper-body stability during complex locomotion and manipulation tasks.
- It employs real-time terrain estimation with wheel-foot force data to dynamically adjust motor torques and compensate for friction without preset environmental models.
- Experimental validation on the X-Man robot demonstrates robust navigation across uneven terrains, confirming effective energy efficiency and adaptive performance.
Whole-Body Impedance Coordinative Control of Wheel-Legged Robots on Uncertain Terrain
This paper presents a sophisticated framework for whole-body impedance coordinative control of wheel-legged humanoid robots, designed to navigate complex and uncertain terrains while maintaining upper body stability. The research addresses a persistent challenge in robotics: balancing robust locomotion with stability in manipulation tasks, particularly in environments with varied terrain features.
Framework and Key Contributions
The proposed framework employs a bi-level control strategy:
- Outer Level Control: A variable damping impedance controller focuses on optimizing damping parameters to ensure upper body stability during locomotion, especially while the robot is engaged in manipulation tasks like holding an object. This component allows the robot to adjust to dynamic conditions passively, limiting energy expenditure typically required for stabilization.
- Inner Level Control: The Whole-Body Control (WBC) optimization integrates real-time terrain estimation using wheel-foot position and force data to produce motor torques. This addresses dynamic constraints and joint limits while incorporating model-free friction compensation. It thus generates actuation tasks that guarantee the fulfiLLMent of impedance requirements and the adaptability to dynamically changing terrains.
Integration and Experimental Validation: The extension of this method was validated empirically on X-Man, a novel quadruped humanoid robot. The system demonstrates a combination of maintaining upper-body stability while adapting to diverse terrains, such as cobblestone paths and slopes, without pre-set environmental knowledge. The test results indicate that the X-Man could perform a water-carrying task over these terrains, evidencing the robustness of the control strategy.
Theoretical and Practical Implications
From a theoretical viewpoint, the paper contributes significantly to the discourse on robotic adaptability in dynamic environments. It integrates impedance control into the WBC framework to stabilize the robot's upper body passively. This integration offers a way to utilize a robot's inherent mechanical and dynamic properties rather than relying solely on computational processes, ultimately conserving energy.
Practically, this development has profound implications for robotic deployment in human-centric environments where robots need to interact seamlessly while performing utilitarian tasks, such as logistics and service delivery, on non-uniform surfaces. By deriving the external forces and adjusting control parameters in real-time, the robot exhibits both predictive and adaptive capabilities that are critical for autonomous operations.
Future Prospects
Future work could explore enhancing the adaptation to even more variable conditions by refining real-time estimation algorithms for terrain features and contact forces. Also, increasing computational efficiency of the WBC for integration into more compact systems or those with more limited processing capabilities could be beneficial. Additionally, incorporating machine learning could provide automated refinement of the impedance parameters based on historical performance data, further improving the adaptability and energy efficiency of humanoid robotics systems.
In summary, the paper succinctly demonstrates a sophisticated approach to control systems in robotics, emphasizing the significance of coordinated control methods and heuristic terrain evaluation. Such research lays groundwork for future innovations in robotic adaptability and autonomy in diverse and unpredictable environments.