In this paper, an adaptive locomotion control approach for a hexapod robot is proposed. Inspired from biological neuro control systems, a 3D two-layer artificial center pattern generator (CPG) network is adopted to generate the locomotion of the robot. The first layer of the CPG is responsible for generating several basic locomotion patterns and the functional configuration of this layer is determined through kinematics analysis. The second layer of the CPG controls the limb behavior of the robot to adapt to environment change in a specific locomotion pattern. To enable the adaptability of the limb behavior controller, a reinforcement learning (RL)-based approach is employed to tune the CPG parameters. Owing to symmetrical structure of the robot, only two parameters need to be learned iteratively. Thus, the proposed approach can be used in practice. Finally, both simulations and experiments are conducted to verify the effectiveness of the proposed control approach.

The purpose of this study is to explore and assess manual material handling problems involving a vertical rope-pulling task from a scaffold (VRPS). Twenty-five young male Chinese subjects were recruited to participate in this study. The psychophysical method was used to investigate the effects of the rope material (nylon and hemp), rope diameter (6/8'' and 4/8''), object size (bucket diameter 28 cm and 36 cm), operating with and without gloves on the maximum acceptable rope-pulling weight (MAWR), rating of perceived exertion (RPE) and heart rate, respectively. The results showed that the maximum acceptable rope-pulling weights were significantly affected by the rope material, rope diameter, object size and wearing or not wearing gloves. The MAWR for the hemp rope, coarse rope, small object size and without gloves was significantly greater than that for the nylon rope, fine rope, large object size and with gloves, respectively. However, the effect of the rope material, rope diameter, object size and with and without gloves on heart rate was not significant. The mean RPE response was significantly influenced by the rope material, object size and wearing or not wearing gloves. The most stressed body parts were the arms, fingers and wrists. The interaction effect between the rope material and wearing or not wearing gloves was significant. Generally, the VRPS for workers using hemp rope without gloves or using nylon rope without gloves was better than that for the other combinations.

Soft robots have recently received much attention with their infinite degrees of freedoms and continuously deformable structures, which allow them to adapt well to the unstructured environment. A new type of soft actuator, namely, dielectric elastomer actuator (DEA) which has several excellent properties such as large deformation and high energy density is investigated in this study. Furthermore, a DEA-based soft robot is designed and developed. Due to the difficulty of accurate modeling caused by nonlinear electromechanical coupling and viscoelasticity, the iterative learning control (ILC) method is employed for the motion trajectory tracking with an uncertain model of the DEA. A D 2 type ILC algorithm is proposed for the task. Furthermore, a knowledge-based model framework with kinematic analysis is explored to prove the convergence of the proposed ILC. Finally, both simulations and experiments are conducted to demonstrate the effectiveness of the ILC, which results show that excellent tracking performance can be achieved by the soft crawling robot.

No abstract

No abstract