Force Observer-Based Motion Adaptation and Adaptive Neural Control for Robots in Contact With Unknown Environments

This article proposes a spatial learning control system for robots to achieve a desired behavior during interacting with unknown environments. In contacting with the environment, the force is estimated by a force observer, so sensing devices are not required. Motivated by the human interaction versatility, the reference trajectory of the robot is updating with a learning law such that the interacting force can be maintained at a desired level. Compared with the trajectory iteration algorithm based on time domain, which requires maintaining a fixed motion speed for each iteration, the proposed method can remove this limitation and have better feasibility. The adaptive controller with neural networks can compensate the uncertain dynamics of the system and ensure the control accuracy. Through Lyapunov's theory, the system is proved to be stable, and all the states are bounded. Comparative simulations and experiments are conducted on a robot platform to verify the effectiveness of the proposed method.

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation

Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) humanoid manipulator based on the arm structure of human bionics. The 3-2-2 structural arrangement of the motors and the structural modifications at the wrist allow the manipulator to act more similar to a man. Meanwhile, to manipulate the steering wheel stably and compliantly, an admittance control approach is firstly applied for this case. Considering that the system parameters vary in configuration, we further introduce parameter fuzzification for admittance control. Designed simulations were carried out in Coppeliasim to verify the proposed control approach. As the result shows, the improved method could realize compliant force control under extreme configurations. It demonstrates that the humanoid manipulator can twist the steering wheel stably even in extreme configurations. It is the first exploration to operate a steering wheel similar to a human with a manipulator by using admittance control.