双臂机器人两手臂间的协调运动

本文研究了双臂机器人的运动学协调问题.双臂机器人在工作过程中,两操作手的运动必须是相互配合的、协调的.本文结合其结构与工作特点.就两手同时抓持一物体运动时,由主动手的运动状态.推导出了从动手在其自身坐标系中的运动状态.     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

Position control of two-arm manipulators for coordinated point-to-point motion

A trajectory planning and motion control algorithm is; presented for the point-to-point (PTP) motion of two-arm manipulators cooperating on a task. The proposed method considers the multi-arm manipulator as a system when formulating its kinematic model and obtains a global solution to the system, as opposed to individual arm solutions. For PTP motion control between two arm configurations, a simple trajectory is first assumed by defining joint velocity profiles and maximum allowable task space errors between the two end effectors of the manipulator. The task space errors during the motion are then continuously monitored to take corrective action when necessary to prevent those errors from exceeding the given tolerance limits. The main objective of this method is to reduce the number of inverse kinematics solutions during the real-time control of the two-arm system. The algorithm is illustrated by a numerical example for an eight degree-of-freedom kinematically redundant planar two-arm system.     

Convex Optimization Strategies for Coordinating Large-Scale Robot Formations

This paper investigates convex optimization strategies for coordinating a large-scale team of fully actuated mobile robots. Our primary motivation is both algorithm scalability as well as real-time performance. To accomplish this, we employ a formal definition from shape analysis for formation representation and repose the motion planning problem to one of changing (or maintaining) the shape of the formation. We then show that optimal solutions, minimizing either the total distance or minimax distance the nodes must travel, can be achieved through second-order cone programming techniques. We further prove a theoretical complexity for the shape problem of O(m^1.5) as well as O(m) complexity in practice, where m denotes the number of robots in the shape configuration. Solutions for large-scale teams (1000's of robots) can be calculated in real time on a standard desktop PC. Extensions integrating both workspace and vehicle motion constraints are also presented with similar complexity bounds. We expect these results can be generalized for additional motion planning tasks, and will prove useful for improving the performance and extending the mission lives of large-scale robot formations as well as mobile ad hoc networks.     

在人机协作中基于动量观测和优化算法的全机械臂单点接触信息实时估计

针对协作型机器人,提出了一种依据机器人控制与运动状态信息,基于动量观测与优化算法相结合的全机械臂单点接触信息实时估计方法.该方法将接触位置估计问题转化为优化算法有界搜索问题,搜索范围由动量观测方法确定的接触臂长度确定,两种方法的结合保证了估计的精度和实时性.基于UR机械臂实验平台的仿真与实验结果表明,本文方法能够实时、准确地估计除第1关节以外首次发生接触的机械臂任意位置处的力和位置信息.本研究可以在不借助传感器的情况下实现机械臂对外力的感知.     

Motion control and shape optimization of a suitlike flexible arm

A method of motion control as well as shape optimization is proposed for the preliminary design of a suitlike flexible arm, which is composed of some variable-length and fixed-length beams. The large deformation, variable geometry and motion of the flexible structure are calculated by dynamic finite element analysis (FEA) using the step by step time integration method. A neural-networks-inverse-model, which learns nonlinear behaviour of the flexible structure, has been applied for the motion control as an inverse model of the flexible arm. For this geometrically nonlinear structure and time response problem, the optimum shape of the cross-section has been calculated under constraints of stress, stiffness and minimum weight with FEA and sensitivity analysis combined with fuzzy rules. This method has been applied for the design of a flexible arm, which simulates a process of lifting a human body and moving it. The calculated optimum shape has a much higher stiffness with decrease in weight in comparison with the initial shape. Moreover, the calculated motion agrees well with the one aimed for and the flexible arm reduces the impact force.     

Bearing-only Cooperative Localization

In cooperative localization a group of robots exchange relative position measurements from their exteroceptive sensors and their motion information from interoceptive sensors to collectively estimate their position and heading. For the localization errors to be bounded, it is required that the system be observable, independent of the estimation technique being used. In this paper, we develop a test-bed of three ground robots, which are equipped with wheel encoders and omnidirectional cameras, to implement the bearing-only cooperative localization. The simulation and experimental results validate the observability conditions, derived in Sharma et al. (IEEE Trans Robot 28:2, 2011), for the complete observability of the bearing-only cooperative localization problem.     

Humanoid robot arm performance optimization using multi objective evolutionary algorithm

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria; minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.     

Unfolding of a rectangular cloth from unarranged starting shapes by a Dual-Armed robot with a mechanism for managing recognition error and uncertainty

We propose a method for unfolding a rectangular cloth placed on a table in an arbitrary unarranged shape, using a dual arm robot. There are many situations where the manipulation of fabric products by dual arm robots is slow due to operation complexity. Also, observation of fabric products in unarranged shapes can be fraught with uncertainty, posing further difficulties for robotic manipulation. In this article, we address these problems for our specific task, implementing a ‘pinch and slide motion’ to address the former issue, and an operation selection mechanism implemented as a partially observable Markov decision process to address the latter. We used this approach to let a robot unfold a rectangular cloth, thereby experimentally verifying the effectiveness of our approach.     

Markerless human–robot interface for dual robot manipulators using Kinect sensor

Remote teleoperation of robot manipulators is often necessary in unstructured, dynamic, and dangerous environments. However, the existing mechanical and other contacting interfaces require unnatural, or hinder natural, human motions. At present, the contacting interfaces used in teleoperation for multiple robot manipulators often require multiple operators. Previous vision-based approaches have only been used in the remote teleoperation for one robot manipulator as well as require the special quantity of illumination and visual angle that limit the field of application. This paper presents a noncontacting Kinect-based method that allows a human operator to communicate his motions to the dual robot manipulators by performing double hand–arm movements that would naturally carry out an object manipulation task. This paper also proposes an innovative algorithm of over damping to solve the problem of error extracting and dithering due to the noncontact measure. By making full use of the human hand–arm motion, the operator would feel immersive. This human–robot interface allows the flexible implementation of the object manipulation task done in collaboration by dual robots through the double hand–arm motion by one operator.     

快速收敛的机器人部队任意队形分布式控制算法

针对多智能体协作完成特定任务时难以在全自主控制的前提下协作形成任意队形和队形向量不易确定的问题 ,通过由各智能体自主简单的确定自己的队形向量 ,从理论上扩展基于队形向量的队形控制原理以生成任意队形 ,改进机器人的运动方式以提高收敛速度 ,提出一种快速收敛的机器人部队任意队形分布式控制算法 .仿真结果表明 ,该算法可以形成任意队形 ,比现有控制算法的收敛速度快 ,队形收敛所需的时间仅为现有算法的 10 %左右     

多移动机器人系统运动协调研究综述

运动协调是多移动机器人系统领域的主要研究热点之一。在阐述多机器人合作与运动协调两者关系的基础上,给出了多机器人系统运动协调的问题描述及其分类;从主要研究方法的角度,归纳总结了多机器人系统运动协调的国内外研究动态。最后,对运动协调在多移动机器人系统领域的前景和研究方向作出了展望。     

Safe and efficient motion planning of multiple mobile robots based on artificial potential for human behavior and robot congestion

In order for robots to exist together with humans, safety for the humans has to be strictly ensured. On the other hand, safety might decrease working efficiency of robots. Namely, this is a trade-off problem between human safety and robot efficiency in a field of human–robot interaction. For this problem, we propose a novel motion planning technique of multiple mobile robots. Two artificial potentials are presented for generating repulsive force. The first potential is provided for humans. The von Mises distribution is used to consider the behavioral property of humans. The second potential is provided for the robots. The Kernel density estimation is used to consider the global robot congestion. Through simulation experiments, the effectiveness of the behavior and congestion potentials of the motion planning technique for human safety and robot efficiency is discussed. Moreover, a sensing system for humans in a real environment is developed. From experimental results, the significance of the behavior potential based on the actual humans is discussed. For the coexistence of humans and robots, it is important to evaluate a mutual influence between them. For this purpose, a virtual space is built using projection mapping. Finally, the effectiveness of the motion planning technique for the human–robot interaction is discussed from the point of view of not only robots but also humans.     

Convergence and robustness of a discrete-time learning control scheme for constrained manipulators

The constrained motion control is one of the most common control tasks found in many industrial robot applications. The nonlinear and nonclassical nature of the dynamic model of constrained robots make designing a controller for accurate tracking of both motion and force a difficult problem. In this article, a discrete-time learning control problem for precise path tracking of motion and force for constrained robots is formulated and solved. The control system is able to reduce the tracking error iteratively in the presence of external disturbances and errors in initial condition as the robot repeats its action. Computer simulation result is presented to demonstrate the performance of the proposed learning controller. © 1994 John Wiley & Sons, Inc.     

Routing-based navigation of dense mobile robots

In the future, many teams of robots will navigate in home or office environments, similar to dense crowds operating currently in different scenarios. The paper aims to route a large number of robots so as to avoid build-up of congestions, similar to the problem of route planning of traffic systems. In this paper, first probabilistic roadmap approach is used to get a roadmap for online motion planning of robots. A graph search-based technique is used for motion planning. In the literature, typically the search algorithms consider only the static obstacles during this stage, which results in too many robots being scheduled on popular/shorter routes. The algorithm used here therefore penalizes roadmap edges that lie in regions with large robot densities so as to judiciously route the robots. This planning is done continuously to adapt the path to changing robotic densities. The search returns a deliberative trajectory to act as a guide for the navigation of the robot. A point at a distant of the deliberative path becomes the immediate goal of the reactive system. A ‘centre of area’-based reactive navigation technique is used to reactively avoid robots and other dynamic obstacles. In order to avoid two robots blocking each other and causing a deadlock, a deadlock avoidance scheme is designed that detects deadlocks, makes the robots wait for a random time and then allows them to make a few random steps. Experimental results show efficient navigation of a large number of robots. Further, routing results in effectively managing the robot densities so as to enable an efficient navigation.     

An Attempt to Raise the Level of Software Abstraction in Assembly Robotics through an Apposite Choice of Underlying Mechatronics

Robot manipulators were meant to be the production engineer"s flexible friend. Assembly robots, however, have failed to fulfill their promise. The problem that has continuously plagued robotic assembly is that of spatial uncertainty. It is our thesis that the ubiquitous problem of spatial uncertainty is an artefact of the fact that current industrial manipulators are designed for an operational paradigm that assumes position control is of primary importance. In this paper we propound an alternative approach based on sliding as the primary motion primitive. We first present a model that uses sliding to allow us to raise the level of abstraction of robot programming tasks. We then describe an inherently accommodating, (planar) three degree of freedom, direct-drive robot arm that was constructed to test our approach. Finally, we present data collected from representative (planar) manipulation tasks that substantiate our claims.     

Capturing a Convex Object With Three Discs

This paper addresses the problem of capturing an arbitrary convex object P in the plane with three congruent disc-shaped robots. Given two stationary robots in contact with P, we characterize the set of positions of a third robot, the so-called capture region, that prevent P from escaping to infinity via continuous rigid motion. We show that the computation of the capture region reduces to a visibility problem. We present two algorithms for solving this problem, and for computing the capture region when P is a polygon and the robots are points (zero-radius discs). The first algorithm is exact and has polynomial time complexity. The second one uses simple hidden surface removal techniques from computer graphics to output an arbitrarily accurate approximation of the capture region; it has been implemented, and examples are presented.     

Realization of sign language motion using a dual-arm/hand humanoid robot

The recent increase in technological maturity has empowered robots to assist humans and provide daily services. Voice command usually appears as a popular human–machine interface for communication. Unfortunately, deaf people cannot exchange information from robots through vocal modalities. To interact with deaf people effectively and intuitively, it is desired that robots, especially humanoids, have manual communication skills, such as performing sign languages. Without ad hoc programming to generate a particular sign language motion, we present an imitation system to teach the humanoid robot performing sign languages by directly replicating observed demonstration. The system symbolically encodes the information of human hand–arm motion from low-cost depth sensors as a skeleton motion time-series that serves to generate initial robot movement by means of perception-to-action mapping. To tackle the body correspondence problem, the virtual impedance control approach is adopted to smoothly follow the initial movement, while preventing potential risks due to the difference in the physical properties between the human and the robot, such as joint limit and self-collision. In addition, the integration of the leg-joints stabilizer provides better balance of the whole robot. Finally, our developed humanoid robot, NINO, successfully learned by imitation from human demonstration to introduce itself using Taiwanese Sign Language.     

Response to CBRNE and human-caused accidents by using land and air robots

In the EU, the basis of the civil protection response to CBRNE, earth/disaster and humancaused accidents is the Emergency Response Coordination Center. For a fast response, two different architectures of centers of the regional system (CRS), with land and area robots, are presented and investigated. In the case of a static center, the robots are remotely controlled from the center by an operator. In this realization, the distance to the area to be investigated is restricted by the distance of possible remote control by the operator. The second one is a dynamic CBRN protection system, where the majority of CRS functions are organized on a car separate from the CRS building. By the car', the distance from the CRS to the accident place can usually be reached at the car motion speed. This is the main advantage of the dynamic system.     

A Complete and Scalable Strategy for Coordinating Multiple Robots Within Roadmaps

This paper addresses the challenging problem of finding collision-free trajectories for many robots moving toward individual goals within a common environment. Most popular algorithms for multirobot planning manage the complexity of the problem by planning trajectories for robots individually; such decoupled methods are not guaranteed to find a solution if one exists. In contrast, this paper describes a multiphase approach to the planning problem that uses a graph and spanning tree representation to create and maintain obstacle-free paths through the environment for each robot to reach its goal. The resulting algorithm guarantees a solution for a well-defined number of robots in a common environment. The computational cost is shown to be scalable with complexity linear in the number of the robots, and demonstrated by solving the planning problem for 100 robots, simulated in an underground mine environment, in less than 1.5 s with a 1.5 GHz processor. The practicality of the algorithm is demonstrated in a real-world application requiring coordinated motion planning of multiple physical robots.