From task parameters to motor synergies: A hierarchical framework for approximately-optimal control of redundant manipulators

We present a hierarchical framework for approximately-optimal control of redundant manipulators. The plant is augmented with a low-level feedback controller, designed to yield input-output behavior that captures the task-relevant aspects of plant dynamics but has reduced dimensionality. This makes it possible to reformulate the optimal control problem in terms of the augmented dynamics, and optimize a high-level feedback controller without running into the curse of dimensionality. The resulting control hierarchy compares favorably to existing methods in robotics. Furthermore we demonstrate a number of similarities to (non-hierarchical) optimal feedback control. Besides its engineering applications, the new framework addresses a key unresolved problem in the neural control of movement. It has long been hypothesized that coordination involves selective control of task parameters via muscle synergies, but the link between these parameters and the synergies capable of controlling them has remained elusive. Our framework provides this missing link.     

Force polytope and force ellipsoid for redundant manipulators

Evaluation of force/motion capabilities for a manipulator is useful both in the design phase and in the operational phase. Manipulability ellipsoids and polytopes are well-known tools used to represent these capabilities graphically. This article focuses on the evaluation of force capabilities for redundant manipulators, for which additional constraints must be imposed on the available joint torques to satisfy the static assumption. An algorithm to correctly evaluate the task space force polytope is given and a new definition of the force ellipsoid is proposed. The obtained results can be applied also to nonredundant manipulators in singular configurations. Numerical results are provided in the case of a planar redundant arm. ©1997 John Wiley & Sons, Inc.     

From task parameters to motor synergies: A hierarchical framework for approximately optimal control of redundant manipulators

We present a hierarchical framework for approximately optimal control of redundant manipulators. The plant is augmented with a low‐level feedback controller, designed to yield input‐output behavior that captures the task‐relevant aspects of plant dynamics but has reduced dimensionality. This makes it possible to reformulate the optimal control problem in terms of the augmented dynamics, and optimize a high‐level feedback controller without running into the curse of dimensionality. The resulting control hierarchy compares favorably to existing methods in robotics. Furthermore, we demonstrate a number of similarities to (nonhierarchical) optimal feedback control. Besides its engineering applications, the new framework addresses a key unresolved problem in the neural control of movement. It has long been hypothesized that coordination involves selective control of task parameters via muscle synergies, but the link between these parameters and the synergies capable of controlling them has remained elusive. Our framework provides this missing link. © 2005 Wiley Periodicals, Inc.     

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.     

Motion planning for cooperating mobile manipulators

We address the problem of motion planning for nonholonomic cooperating mobile robots manipulating and transporting objects while holding them in a stable grasp. We present a general approach for solving optimal control problems based on the calculus of variations. We specialize this approach to solving the motion planning problem and obtaining trajectories and actuator forces/torques for any maneuver in the presence of obstacles. The approach allows geometric constraints such as joint limits, kinematic constraints such as nonholonomic velocity constraints, and dynamic constraints such as frictional constraints and contact force constraints to be incorporated into the planning scheme. The application of this method is illustrated by computing motion plans for several examples, and these motions plans are implemented on an experimental testbed. ©1999 John Wiley & Sons, Inc.     

冗余机器人的双向自运动路径规划

冗余机器人的自运动路径规划是在保持手端任务向量不变的情况下,在关节空间内寻找一条连接机器人初始关节构形和期望关节构型的几何路径.本文给出一种双向自运动路径规划算法,其基本思想是使位于初始关节构形的真实机器人和位于期望关节构形的虚拟机器人在自运动流形上运动并收敛到同一关节构形,从而得到一条连接初始和期望关节构形的自运动几何路径.该算法克服了以往算法容易陷入局部极小构形的缺陷.仿真结果证实了算法的有效性.     

A collision‐avoidance scheme for redundant manipulators: Theory and experiments

The development of a collision‐ and self‐collision‐avoidance scheme for redundant manipulators is discussed in this paper. The method is based on modeling the arm and its environment by simple geometric primitives (cylinders and spheres). A compact method of detecting collisions between two cylinders is introduced. By resorting to the notions of dual angles and dual vectors for representing the axes of cylinders in space, a characterization of different types of collisions is introduced. The performance of the proposed scheme is demonstrated for a seven degrees‐of‐freedom redundant manipulator via simulations and experiments. © 2005 Wiley Periodicals, Inc.     

Constraint finite‐time control of redundant manipulators

This work addresses the problem of the accurate task‐space control subject to finite‐time convergence. Dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the end effector. Furthermore, the movement is to be accomplished in such a way as to optimize some performance index. Based on suitably defined task‐space non‐singular terminal sliding vector variable and the Lyapunov stability theory, we derive a class of inverse‐free robust controllers consisting of a Jacobian transpose component plus a compensating term, which seem to be effective in counteracting uncertain dynamics, unbounded disturbances and (possible) kinematic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a Selective Compliant Articulated Robot for Assembly (SCARA) type consisting of three revolute kinematic pairs and operating in a two‐dimensional task space illustrate performance of the proposed controllers. Copyright © 2016 John Wiley & Sons, Ltd.     

Failure-tolerant path planning for kinematically redundant manipulators anticipating locked-joint failures

This work considers kinematic failure tolerance when obstacles are present in the environment. It addresses the issue of finding a collision-free path such that a redundant robot can successfully move from a start to a goal position and/or orientation in the workspace despite any single locked-joint failure at any time. An algorithm is presented that searches for a simply-connected, obstacle-free surface with no internal local minimum or maximum in the configuration space that guarantees the existence of a solution. The method discussed is based on the following assumptions: a robot is redundant relative to its task, only a single locked-joint failure occurs at any given time, the robot is capable of detecting a joint failure and immediately locks the failed joint, and the environment is static and known. The technique is illustrated on a seven degree-of-freedom commercially available redundant robot. Although developed and illustrated for a single degree of redundancy, it is possible to extend the algorithm to higher degrees of redundancy.     

On the execution of the secondary task of redundant manipulators

In the paper, we compare two different approaches to accelerate the execution of the secondary task of redundant manipulators when a standard task-priority control scheme is utilised. In the task-priority approach, the vector of the desired increment in the secondary task space is projected in the null space of the primary task. This projection may distort the projected vector and the secondary task may not adequately be executed. One approach to avoid the difficulty is to optimise the weight of the pseudoinverse matrix. The problem is that the change of the weight simultaneously influences the execution of the primary task. In the second approach, we directly optimise the null space projection matrix by minimising the difference between the projected and the desired vector of the secondary task increment. In the latter approach, we have the freedom to choose the weight of the pseudoinverse matrix.     

Hierarchical trajectory planning of redundant manipulators with structured intelligence

This paper deals with trajectory generation for redundant manipulators with structured intelligence. Recently, behavior engineering for robotic systems has been discussed as a new technological discipline. The intelligence of a robot depends on the structure of hardware and software for processing information, i.e. the structure determines the potentiality of intelligence. This paper proposes a robotic system with structured intelligent based on subsumption-like architecture. Based on perceptual information, a robot with structured intelligence makes decisions and takes action from four levels in parallel. In addition, the robot generates its motion through interaction with the environment and, at the same time, gradually acquires its skill based on the generated motion. To acquire skill and motion, the robot requires internal and external evaluations at least. This paper applies a virus-evolutionary genetic algorithm to trajectory planning for redundant manipulators with structured intelligence. Furthermore, we discuss its effectiveness through computer simulation results.     

A direct variational method for planning monotonically optimal paths for redundant manipulators in constrained workspaces

This paper proposes a path planner for serial manipulators with a large number of degrees of freedom, working in cluttered workspaces. Based on the variational principles, this approach involves formulating the path planning problem as constrained minimization of a functional representing the total joint movement over the complete path. We use modified boundary conditions at both ends of the trajectory to find more suitable start and end configurations. The concept of monotonic optimality is introduced in order to optimize the manipulator paths between the resulting end configurations. For obstacle avoidance, volume and proximity based penalizing schemes are developed and used. The presented planner uses a global approach to search for feasible paths and at the same time involves no pre-processing task. A variety of test cases have been presented to establish the efficacy of the presented scheme in providing good quality paths. The extent of advantage accruing out of the measures of free end-configurations and monotonic optimality are also analyzed quantitatively.     

Potential‐based path planning for robot manipulators

In this paper, a potential‐based path‐planning algorithm for a high DOF robot manipulator is proposed. Unlike some c‐space‐based approaches, which often require expensive preprocessing for the construction of the c‐space, the proposed approach uses the workspace information directly. The approach computes, similar to that done in electrostatics, repulsive force and torque between objects in the workspace. A collision‐free path of a manipulator will then be obtained by locally adjusting the manipulator configuration to search for minimum potential configurations using that force and torque. The proposed approach is efficient because these potential gradients are analytically tractable. Simulation results show that the proposed algorithm works well, in terms of computation time and collision avoidance, for manipulators up to 9 degrees of freedom (DOF). © 2005 Wiley Periodicals, Inc.     

A kinematically compatible framework for cooperative payload transport by nonholonomic mobile manipulators

In this paper, we examine the development of a kinematically compatible control framework for a modular system of wheeled mobile manipulators that can team up to cooperatively transport a common payload. Each individually autonomous mobile manipulator consists of a differentially-driven Wheeled Mobile Robot (WMR) with a mounted two degree-of-freedom (d.o.f) revolute-jointed, planar and passive manipulator arm. The composite wheeled vehicle, formed by placing a payload at the end-effectors of two (or more) such mobile manipulators, has the capability to accommodate, detect and correct both instantaneous and finite relative configuration errors. The kinematically-compatible motion-planning/control framework developed here is intended to facilitate maintenance of all kinematic (holonomic and nonholonomic) constraints within such systems. Given an arbitrary end-effector trajectory, each individual mobile-manipulator's bi-level hierarchical controller first generates a kinematically-feasible desired trajectory for the WMR base, which is then tracked by a suitable lower-level posture stabilizing controller. Two variants of system-level cooperative control schemes—leader-follower and decentralized control—are then created based on the individual mobile-manipulator control scheme. Both methods are evaluated within an implementation framework that emphasizes both virtual prototyping (VP) and hardware-in-the-loop (HIL) experimentation. Simulation and experimental results of an example of a two-module system are used to highlight the capabilities of a real-time local sensor-based controller for accommodation, detection and corection of relative formation errors.     

Encoder based online motion planning and feedback control of redundant manipulators

This paper proposes and investigates an online motion planning and feedback control (OMPFC) scheme for redundant manipulators via techniques of quadratic programming and rotary encoder. The proposed OMPFC scheme is performed on a planar six degrees-of-freedom (DOF) manipulator. This robotic scheme incorporates the feedback of task-space position error. The joint state is obtained in real time via rotary encoders equipped on the physical manipulator. The original scheme is finally reformulated as a unified quadratic program (QP). The QP is solved online during the joint motion by employing an efficient numerical algorithm. Simulation and experimental results validate the physical realizability, online property, and efficacy of the proposed OMPFC scheme (including the employed numerical algorithm).     

非完整移动机械臂的避障运动规划

针对有空间障碍物避免的移动式操作机器人系统运动规划问题，提出了一种基于特殊的人工势函数，使用局部距离信息实现非完整移动机械臂系统实时避障运动规划方法，并且用Lyapunov定理证明了闲环系统的稳定性。用提出的方法对非完整移动机械臂系统进行仿真．仿真结果表明了它的正确有效性。     

一种末端任务给定的移动机械手动态路径规划方法

移动机械手末端任务给定情况下的路径规划在喷绘、焊接等工作中有广泛的应用,但这方面的研究还比较少,尤其是在动态环境中.针对该情况提出一种动态规划方法,该方法包含预处理和动态规划两个阶段,使移动机械手在完成任务的前提下,能够有效地回避静态和动态障碍物.仿真结果证明了该方法的正确性.     

A distributed multi-agent production planning and scheduling framework for mobile robots

Inspired by the new achievements in mobile robotics having as a result mobile robots able to execute different production tasks, we consider a factory producing a set of distinct products via or with the additional help of mobile robots. This particularly flexible layout requires the definition and the solution of a complex planning and scheduling problem. In order to minimize production costs, dynamic determination of the number of robots for each production task and the individual robot allocation are needed. We propose a solution in terms of a two-level decentralized Multi-Agent System (MAS) framework: at the first, production planning level, agents are tasks which compete for robots (resources at this level); at the second, scheduling level, agents are robots which reallocate themselves among different tasks to satisfy the requests coming from the first level. An iterative auction based negotiation protocol is used at the first level while the second level solves a Multi-Robot Task Allocation (MRTA) problem through a distributed version of the Hungarian Method. A comparison of the results with a centralized approach is presented.     

Low-level flexible planning for mobile manipulators: a distributed perception approach

The paper proposes a method to improve flexibility of the motion planning process for mobile manipulators. The approach is based on the exploitation of perception data available only from simple proximity sensors distributed on the robot. Such data are used to correct pre-planned motions to cope with uncertainties and dynamic changes of the scene at execution time. The algorithm computes robot motion commands aimed at fulfilling the mission by combining two tasks at the same time, i.e. following the planned end-effector path and avoiding obstacles in the environment, by exploiting robot redundancy as well as handling priorities among tasks. Moreover, a technique to smoothly switch between the tasks is presented. To show the effectiveness of the method, four experimental case studies have been presented consisting in a place task executed by a mobile manipulator in an increasingly cluttered scene.