基于视觉反馈的双冗余机械臂自适应控制

Abstract

Advanced humanoid robots are often equipped with robot vision system and dual-arm redundant manipulators to imitate human behavior and control strategy. The robot vision system can provide real-time feedback of environmental information, and the dual-arm ensemble can grasp different tools to accomplish assigned tasks. By fusing the abundant sensing information (including vision information, joint angle/velocity of the manipulators, joint torques and end-effector forces/torques) and transmitting them to the central controller, it can significantly improve the flexibility, intelligence and robustness of the robots to the unknown environment and model uncertainties.

In many applications, dual-arm manipulator ensemble of a humanoid robot is usually required to grasp various tools with different weights and sizes or different grasping posture/point. While the geometrical uncertainty of these tools would lead to the uncertainty of the grasp matrix, and further losing control of internal force. Since the dual-arm system usually handles heavy tools, unknown dynamics of the tools would also deteriorate the performance of the system if without any compensation. Furthermore, general end-effectors, i.e. dexterous hand or anthropomorphic prosthetic hand, are usually mounted at the end of the manipulators to enable the system to grasp various tools. Due to the arbitrary grasp type, kinematics (Jacobian matrix) and dynamics of the manipulators themselves are therefore uncertain and cannot be exactly known. These uncertainties are the most urgent to be addressed in the context of humanoid robot manipulation since they may limit the adaptability and manipulation dexterity. Therefore, this paper takes the robot astronaut ground experimental platform as the research object to seek solutions to the above problems. We first systematically study the active binocular vision from the two aspects of image processing and servo tracking control. Then by using the visual feedback of the operation point of the grasped tool, arm-eye coordination control is realized. Considering multiple kinds of parameter uncertainties, a robust adaptive multi-task control for single redundant manipulator and an adaptive hybrid position/force control for cooperative dual-arm manipulators are proposed respectively. Finally, the proposed control schemes and algorithms are verified on the experimental platform.

This paper studies the image processing algorithm and controller design of the active vision system to obtain real-time target pose information and smooth and non-lagging head servo tracking motion. The Kalman filter is used to predict the pose signal to eliminate the tracking lag problem caused by visual processing

哈尔滨工业大学工学博士学位论文

and communication delay. Then, the typical nonlinear factors, such as friction and hysteresis, and the dynamics of the 3-DOF head are modeled. Based on this system model, a robust adaptive controller is designed to deal with a variety of nonlinear perturbations and dynamic uncertainties. By using the Lyapunov theorem, a rigorous proof of the stability of the system and the convergence of the tracking error is given. The simulation results show that the designed controller has the advantages of high tracking precision and smooth control torque.

To achieve multi-task tracking control of the redundant manipulator under multiple uncertainties, rational allocation of the relationship and priority of the multi-level tasks are achieved by introducing the multi-priority control architecture. With the real-time pose information of the tool’s operation point provided by the active vision algorithm, we discuss and design the robust adaptive controller in independent case and dependent case, respectively. A strategy is established to comprehensively utilize multi-task tracking information to accelerate the concurrent adaptation process. Unlike the general damping least square method, an improved singularity-robust technique is incorporated to minimize the effect the damping factor acting on the task tracking and alleviate the bad behavior of damped projected Jacobian. The idea of continuous dead zone is used to cope with the problem of unpredictable adaptation when reconstruction error exists in the subtask. Besides, transition shaping technique is also applied to the commanded torques to eliminate the discontinuity led by the potential discontinuous projection. Along with the improvement of the multi-task tracking performance, smoothness of the commanded torques is still guaranteed and the noisy joint accelerations/task velocities are not required. Finally, Simulink/SimMechanics 2G is used to model the system. The effectiveness and superiority of the proposed algorithm are verified by comparison simulation. This research also provides a redundancy solution for the adaptive hybrid control of dual-arm redundant manipulators when multiple uncertainties exist.

From the perspective of dual-arm redundant manipulators, this paper establishes the complete dynameic and kinematic models of the dual-arm system. Detailed force analysis is also carried out. In view of the practical problem that the contact force between the tool and the environment cannot be directly measured, a novel estimation method is given based on the force analysis. At the same time, by using multi-point visual pose feedback, a new self-convergent estimation of the tool’s center of mass (COM) is proposed to obtain accurate grasp matrix. Combined with these methods, an adaptive hybrid force/position control scheme (AHPF) is proposed for the dual-arm manipulators. Stability analysis of the controller is provided based on the Lyapunov theorem. Convergence of the tracking error of position, contact force and internal force are all guaranteed.

Abstract

Three sets of simulation are implemented by Simulink and SimMechanics 2G to present three different applications respectively: free-space motion tracking, position/force tracking with flat constraint surface and that with curved surface. Simulation results show that the proposed AHPF can achieve multiple control objectives and endue the dual-arm system with adaptability to multiple uncertainties compared to the convention hybrid position/force scheme.

To validate the effectiveness of the proposed active binocular vision scheme and vision-based adaptive controllers, active visual servo experiment, arm-eye coordinated servo experiment, adaptive multi-task tracking control experiment for redundant manipulator and adaptive hybrid position/force control for dual-arm manipulators are implemented on the humanoid robot astronaut platform. Keywords:Active binocular vision, redundant manipulator, adaptive control, multi-priority control, dual-arm cooperation