Hierarchical path planning for multi-arm spacecraft with general translational and rotational locomotion mode

On-orbit construction and maintenance technology will play a significant role in future space exploration.The dexterous multifunctional spacecraft equipped with multi-arm,for instance,Spider Fab Bot,has attracted a great deal of focus due to its versatility in completing these missions.In such engineering practice,point-to-point moving in a complex environment is the fundamental issue.This paper investigates the three-dimensional point-to-point path planning problem,and a hierarchical path planning architecture is developed to give the trajectory of the multi-arm spacecraft effectively and efficiently.In the proposed 3-level architecture,the high-level planner generates the global constrained centric trajectory of the spacecraft with a rigid envelop assumption;the middle-level planner contributes the action sequence,a combination of the newly developed general translational and rotational locomotion mode,to cope with the relative position and attitude of the arms about the centroid of the spacecraft;the low-level planner maps the position/attitude of the end-effector of each arm from the operational space to the joint space optimally.Finally,the simulation experiment is carried out,and the results verify the effectiveness of the proposed three-layer architecture path planning strategy.

Integral Real-time Locomotion Mode Recognition Based on GA-CNN for Lower Limb Exoskeleton

The wearable lower limb exoskeleton is a typical human-in-loop human–robot coupled system,which conducts natural and close cooperation with the human by recognizing human locomotion timely.Requiring subject-specific training is the main challenge of the existing approaches,and most methods have the problem of insufficient recognition.This paper proposes an integral subject-adaptive real-time Locomotion Mode Recognition(LMR)method based on GA-CNN for a lower limb exoskeleton system.The LMR method is a combination of Convolutional Neural Networks(CNN)and Genetic Algorithm(GA)-based multi-sensor information selection.To improve network performance,the hyper-parameters are optimized by Bayesian optimization.An exoskeleton prototype system with multi-type sensors and novel sensing-shoes is used to verify the proposed method.Twelve locomotion modes,which composed an integral locomotion system for the daily application of the exoskeleton,can be recognized by the proposed method.According to a series of experiments,the recognizer shows strong comprehensive abilities including high accuracy,low delay,and sufficient adaption to different subjects.

DEVELOPMENT AND MOTION ANALYSIS OF MINIATURE WHEEL-TRACK-LEGGED MOBILE ROBOT

A miniature wheel-track-legged mobile robot to carry out military and civilian missions in both indoor and outdoor environments is presented. Firstly, the mechanical design is discussed, which consists of four wheeled and four independently controlled tracked arms, embedded control system and teleoperation. Then the locomotion modes of the mobile robot and motion analysis are analyzed. The mobile robot can move using wheeled, tracked and legged modes, and it has the characteristics of posture-recovering, high mobility, small size and light weight. Finally, the effectiveness of the deve-loped mobile robot is confirmed by experiments such as posture recovering when tipped over, climb-ing stairs and traversing the high step.

Development and control of flexible pneumatic wall-climbing robot

A new kind of flexible pneumatic wall-climbing robot,named WALKMAN-I,was proposed. WALKMAN-I is basically composed of a flexible pneumatic actuator (FPA),a flexible pneumatic spherical joint and six suction cups. It has many characteristics of low-cost,lightweight,simple structure and good flexibility. Its operating principle was introduced. Then three basic locomotion modes,which are linear motion,curvilinear motion and crossing the orthogonal planes,were presented. The safety conditions of WALKMAN-I were discussed and built. Finally,the control system was designed and experiments were carried out. Experimental results show that WALKMAN-I is able to climb on the vertical wall surface along a straight line or a curved path,and has the ability of crossing orthogonal planes and obstacles. The maximum rotation angle reaches 90°,the maximum velocity reaches 5 mm/s,and the rotation angle and the moving velocity of WALKMAN-I can be easily controlled.

An Optimization System for Intent Recognition Based on an Improved KNN Algorithm with Minimal Feature Set for Powered Knee Prosthesis

In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event.Then,the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization(BGWOPSO)algorithm is used to select features.And,the selected features are optimized and assigned different weights by the Biogeography-Based Optimization(BBO)algorithm.Finally,an improved K-Nearest Neighbor(KNN)classifier is employed for intention recognition.This classifier has the advantages of high accuracy,few parameters as well as low memory burden.Based on data from eight patients with transfemoral amputations,the optimization system is evaluated.The numerical results indicate that the proposed model can recognize nine daily locomotion modes(i.e.,low-,mid-,and fast-speed level-ground walking,ramp ascent/decent,stair ascent/descent,and sit/stand)by only seven features,with an accuracy of 96.66%±0.68%.As for real-time prediction on a powered knee prosthesis,the shortest prediction time is only 9.8 ms.These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.