Development of a Real-Time Human-Robot Collaborative System Based on 1 kHz Visual Feedback Control and Its Application to a Peg-in-Hole Task

Method of Changing Running Direction of Cheetah-Inspired Quadruped Robot

The rapid change of motion direction during running is beneficial to improving the movement flexibility of the quadruped robot, which is of great relevance to its research. How to make the robot change its motion direction during running and achieve good dynamic stability is a problem to be solved. In this paper, a method to change the running direction of the cheetah-inspired quadruped robot is proposed. Based on the analysis of the running of the cheetah, a dynamic model of the quadruped robot is established, and a two-level stability index system, including a minimum index system and a range index system, is proposed. On this basis, the objective function based on the stability index system and optimization variables, including leg landing points, trunk movement trajectory, and posture change rule, are determined. Through these constraints, the direction changes with good dynamic stability of the cheetah-inspired quadruped robot during running is realized by controlling the leg parameters. The robot will not roll over during high-speed movement. Finally, the correctness of the proposed method is proven by simulation. This paper provides a theoretical basis for the quadruped robot's rapid change of direction in running.

High-Speed Vision and its Applications Toward High-Speed Intelligent Systems

Currently, high-speed vision based on parallel processing exists, and its various applications as high-speed intelligent systems have been proposed and implemented. The basic goal of high-speed vision is to realize vision capabilities and systems that operate at speeds necessary for intelligent systems, in which intelligence operating at the speed inherently required by the application system is achieved. This paper described the vision chip and parallel image processing architectures, presented outlines of system architectures, image-processing algorithms, and related peripheral technologies; described the concepts required to configure high-speed intelligent systems, such as hierarchical parallel distributed architecture, parallel decomposition, orthogonal decomposition, dynamics matching, latency minimization, high-speed 3D shape measurement, active vision, tracking vision, dynamic compensation, and dynamic projection mapping; and discussed a wide range of application systems in a systematic manner.

Multiple High-Speed Vision for Identical Objects Tracking

In multi-object tracking of identical objects, it is difficult to return to tracking after occlusions occur due to three-dimensional intersections between objects because the objects cannot be distinguished by their appearances. In this paper, we propose a method for multi-object tracking of identical objects using multiple high-speed vision systems. By using high-speed vision, we take advantage of the fact that tracking information, such as the position of each object in each camera and the presence or absence of occlusion, can be obtained with high time density. Furthermore, we perform multi-object tracking of identical objects by efficiently performing occlusion handling using geometric constraints satisfied by multiple high-speed vision systems; these can be used by appropriately positioning them with respect to the moving region of the object. Through experiments using table-tennis balls as identical objects, this study shows that stable multi-object tracking can be performed in real time, even when frequent occlusions occur.

Experiential and Stochastic Learning Algorithms Based on the Probability of a Fuzzy Event and Modified Fuzzy Metric Distance in Intelligent Robotic Part Micro-Assembly

Experiential and stochastic learning algorithms for improving the time consumed in robot’s part micro-assembly procedures are presented. A comprehensive comparison of the results with other recent algorithms is described. The experiential learning algorithm unites the neighboring sections in a manner that is very similar to the larger united section based on the specified criterion. If the experiential learning algorithm is consecutively repeated, the micro-assembly dynamics analysis chart will be not only minimized in the number of sections, but also formed such that each united section will have one selected optimal plan with the lowest modified fuzzy metric distance within its own section. Thus, if the new input appertaining to the section is merely identified, the final selected plan within the section will execute the related task quickly and accurately thus resulting to a significant improvement in the time spent in the part insertion. Through the stochastic learning algorithm extended from the crisp set domain to the fuzzy set domain, based on the probability of a fuzzy event and the modified fuzzy metric distance, to deal well with the uncertainties arising during the micro-assembly procedure, if one specific plan among the feasible plans is successively selected to solve its confronting micro-assembly problems, the probability that the selected plan will be continuously chosen will increase as a consequence of compensation, namely, a higher value of membership degree is continuously allocated to the selected plan. As a result, the selected control plan with the highest probability of success and the lowest degree of uncertainty acquired by the stochastic learning facilitates the stabilization of the execution phase of the part insertion task. The degree of uncertainty, which is measured by a modified fuzzy metric distance and related to the task execution of the micro-assembly, is used as a criterion to select the most valid plan. The results demonstrate that the micro-assembly stuck problems can be successfully resolved by the experiential and stochastic learning algorithms.