UDE-based Dynamic Motion/Force Control of Mobile Manipulators for Improved Interaction Performance

Core Concepts

A novel dynamic model of the manipulator on the mobile base is proposed, which incorporates the kinematic information of the mobile base into the manipulator dynamics. An uncertainty and disturbance estimator-based (UDE-based) dynamic motion/force control scheme is developed to compensate for the dynamic coupling and other unmodeled uncertainties, improving the interaction performance of the mobile manipulator system.

Abstract

The article presents a novel approach to modeling and controlling mobile manipulators, which are known for their enhanced mobility and interaction capabilities compared to fixed-base manipulators. Key highlights: A new dynamic model of the manipulator on the mobile base is proposed, which requires only the manipulator dynamics and the kinematic information of the mobile base, simplifying the modeling complexity and improving transferability. An uncertainty and disturbance estimator-based (UDE-based) dynamic motion/force control scheme is developed, which combines feedforward and feedback control mechanisms to compensate for the dynamic coupling between the mobile base and the manipulator, as well as other unmodeled uncertainties. Stability analysis is provided, proving the global asymptotic stability of the full motion control mode and the stability of the desired hybrid impedance model in the motion/force control mode. Simulation and experimental results demonstrate the effectiveness of the proposed dynamic model and the UDE-based control scheme in achieving superior motion/force tracking performance under dynamic coupling effects and other disturbances, compared to conventional control approaches. The proposed methodology represents a novel direction in the field of mobile manipulator control, addressing the challenges of dynamic coupling and unmodeled uncertainties to improve the interaction performance of these versatile robotic systems.

Stats

The mobile base is assigned to move a sine trajectory, causing random changes in its movement and leading to undesired motions of the manipulator. The manipulator is tasked to move toward the wall along a predefined motion trajectory and apply forces of 5 N and 10 N on the wall.

Quotes

"To simultaneously manage mobility and interaction tasks, the main challenge lies in the nonlinear dynamic coupling between the mobile base and the manipulator system." "Existing methods suffer from complex modeling processes and poor transferability."

Key Insights Distilled From

UDE-based Dynamic Motion Force Control of Mobile Manipulators

by Songqun Gao,... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00443.pdf

Deeper Inquiries

How can the proposed methodology be extended to handle interactions with soft environments, where the equilibrium point of interaction shifts

To extend the proposed methodology to handle interactions with soft environments, where the equilibrium point of interaction shifts, several adjustments and enhancements can be made. One approach could involve incorporating adaptive impedance control mechanisms that can dynamically adjust stiffness and damping parameters based on the environment's properties. By utilizing force sensors and tactile feedback, the system can continuously monitor the interaction forces and adapt the impedance settings to maintain stability and performance. Additionally, integrating compliance control strategies that allow the manipulator to react to varying levels of resistance in the environment can help in achieving smoother interactions with soft surfaces. By combining these adaptive control strategies with the existing UDE-based approach, the mobile manipulator can effectively navigate and interact with soft environments while ensuring stability and performance.

What are the potential limitations of the UDE-based approach in terms of computational complexity and real-time implementation

The UDE-based approach, while effective in compensating for dynamic coupling and unmodeled uncertainties, may have potential limitations in terms of computational complexity and real-time implementation. One limitation could be the computational resources required to run the UDE algorithm, especially in scenarios with high-dimensional state spaces or complex dynamic models. This could lead to increased processing times and potential delays in the control loop, impacting the system's real-time responsiveness. Additionally, the tuning of UDE parameters and filters may require careful calibration to ensure accurate estimation of uncertainties without introducing instability or oscillations. Ensuring the algorithm's robustness and stability under varying operating conditions is crucial to mitigate these limitations.

How can the perception of unknown environments be integrated with the proposed control scheme to further enhance the mobile manipulator's interaction performance

Integrating the perception of unknown environments with the proposed control scheme can significantly enhance the mobile manipulator's interaction performance. By incorporating sensors such as LiDAR, cameras, or depth sensors, the system can gather real-time data about the environment, including obstacles, terrain features, and object positions. This environmental perception data can be fused with the control algorithm to adapt the manipulator's motion and force commands based on the detected surroundings. For example, the system can autonomously adjust its trajectory to avoid collisions, optimize its path planning based on obstacle detection, or dynamically modulate its impedance settings in response to varying environmental conditions. By combining perception capabilities with the UDE-based control scheme, the mobile manipulator can achieve higher levels of autonomy, adaptability, and performance in complex and dynamic environments.