An intelligent configuration-sampling based local motion planner for robotic manipulators

Manipulator motion planning is a task which relies heavily on the construction of a configuration space prior to path planning. However when fast real-time motion is needed, the full construction of the manipulator's high-dimensional configu-ration space can be too slow and expensive. Alternative planning methods, which avoid this full construction of the manipulator's configuration space are needed to solve this problem. Here, one such existing local planning method for manipulators based on configuration-sampling and subgoal-selection has been extended. Using a modified Artificial Potential Fields (APF) function, goal-configuration sampling and a novel subgoal selection method, it provides faster, more optimal paths than the previously proposed work. Simulation results show a decrease in both runtime and path lengths, along with a decrease in unexpected local minimum and crashing issues.

Simplified PID self-tuning controller for robotic manipulators

The paper describes a self-tuning adaptive PID controller suitable for use in the control of robotic manipulators. The scheme employs a simple recursive estimator which reduces the computational effort to an acceptable level for many applications in robotics.

Designing optimal fault tolerant Jacobian for robotic manipulators

Fault tolerance of robotic manipulators is determined based on the fault tolerance measures. In this study a Jacobian of a 7DOF optimal fault tolerant manipulator is designed based on optimality of worse case relative manipulability and worse case dexterity from geometric perspective instead of numerical solution of constrained optimisation problem or construction of optimal Jacobean through a desired null space. The proposed Jacobean matrix is optimal and equally fault tolerant for a single joint failure within any joint of the manipulators.

Optimal fault-tolerant robotic manipulators

This thesis addresses “Optimal Fault-Tolerant Robotic Manipulators” for locked-joint failures and consists of three components. It begins by investigating the regions of workspace where the manipulator can operate with high reliability. It then continues with an efficient deployment of kinematic redundancies for fault-tolerant operation. Finally, it presents a novel method for design of optimal fault-tolerant manipulators.

Reliability maps and probabilistic guarantee of task motion for robotic manipulators

There are many applications for which reliable and safe robots are desired. For example, assistant robots for disabled or elderly people and surgical robots are required to be safe and reliable to prevent human injury and task failure. However, different levels of safety and reliability are required for different tasks so that understanding the reliability of robots is paramount. Currently, it is possible to guarantee the completion of a task when the robot is fault tolerant and the task remains in the fault-tolerant workspace (FTW). The traditional definition of FTW does not consider different reliabilities for the robotic manipulator's different joints. The aim of this paper is to extend the concept of a FTW to address the reliability of different joints. Such an extension can offer a wider FTW while maintaining the required level of reliability. This is achieved by associating a probability with every part of the workspace to extend the FTW. As a result, reliable fault-tolerant workspaces (RFTWs) are introduced by using the novel concept of conditional reliability maps. Such a RFTW can be used to improve the performance of assistant robots while providing the confidence that the robot remains reliable for completion of its assigned tasks. © 2012 Copyright Taylor & Francis and The Robotics Society of Japan.

Notes on Newton-Euler formulation of robotic manipulators

The equations corresponding to Newton-Euler iterative method for the determination of forces and moments acting on the rigid links of a robotic manipulator are given a new treatment using composed vectors for the representation of both kinematical and dynamical quantities. It is shown that Lagrange equations for the motion of a holonomic system are easily found from the composed vectors defined in this note. Application to a simple model of an industrial robot shows that the method developed in these notes is efficient in solving the dynamics of a robotic manipulator. An example is developed, where it is seen that with the application of appropriate control moments applied to each arm of the robot, starting from a given initial position, it is possible to reach equilibrium in a final pre-assigned position.

Performance indices for robotic manipulators: a review of the state of the art

The performance indices are important tools for motion planning and design of robot manipulators. In this paper we present a collection of some of the performance indices that have generated interest in the robotics community. These indices are four different types: kinetostatic performance indices, dynamic performance indices, indices of joint limits, and finally global performance indices. In addition, we review the strategies that have been proposed to solve the problems that occur when the units of the Jacobian matrix elements are not homogeneous. At the end of this paper, we propose a set of global performance indices that can be useful in the design of robot manipulators.

Robust Learning Control for Manipulators with Uncertainty Disturbances Based on Internal Model

In this paper, a disturbance controller is designed for making robotic system behave as a decoupled linear system according to the concept of internal model. Based on the linear system, the paper presents an iterative learning control algorithm to robotic manipulators. A sufficient condition for convergence is provided. The selection of parameter values of the algorithm is simple and easy to meet the convergence condition. The simulation results demonstrate the effectiveness of the algorithm..

Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators

This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.

Fault tolerance operation of cooperative manipulators

Autonomous or teleoperation of critical tasks in space applications require fault tolerant robotic manipulators. These manipulators are able to maintain their tasks even if a joint fails. If it is presumed that the manipulator is fault tolerant on its trajectory, then the next step is to provide a fault tolerant force at the end-effector of the manipulator. The problem of cooperative fault tolerant force is addressed in this paper within the operation of two manipulators. The cooperative manipulators are used to compensate the force jump which occurs on the force of the end-effector of one manipulator due to a joint failure. To achieve fault tolerant operation, the contribution of the faulty joint for the force of the end-effector of the faulty manipulator is required to be optimally mapped into the torque of the faulty and healthy manipulators. The optimal joint torque reconfigurations of both manipulators for compensating this force jump are illustrated. The proposed frameworks are deployed for two cooperative PUMA560 manipulators. The results of the case studies validate the fault tolerant cooperation strategies.

On the effort of task completion for partially-failed manipulators

Adding to a previous work of the authors for task completion for partially failed manipulator, other aspects of the effort are discussed. The paper aims to investigate on the strategies of maximum effort for maintaining the availability of partially failed manipulators. The failures are assumed as the joint lock failures of the manipulators. The main objective is to facilitate the existing manipulators to continue their tasks even if a non catastrophic fault occurs into their joints. The tasks includes motion tasks and force tasks. For each group of tasks a constrained optimality problem is introduced. Then in a case study a required force profile on a desired trajectory using a 3DOF planar manipulator is indicated. Through this study the joint angles and joint torques for a healthy manipulator and a faulty manipulator are shown. It is illustrated that a failure in the second joint is tolerated on the trajectory of end-effector.

Multipoint haptic mediator interface for robotic teleoperation

Despite recent advances in artificial intelligence and autonomous robotics, teleoperation can provide distinct benefits in applications requiring real-time human judgement and intuition. However, as robotic systems are increasingly becoming sophisticated and are performing more complex tasks, realizing these benefits requires new approaches to teleoperation. This paper introduces a novel haptic mediator interface for teleoperating mobile robotic platforms that have a variety of manipulators and functions. Identical master-slave bilateral teleoperation of the robotic manipulators is achieved by representing them in virtual reality and by allowing the operator to interact with them using a multipoint haptic device. The operator is also able to command motions to the mobile platform by using a novel haptic interaction metaphor rather than a separate dedicated input device. The presented interaction techniques enable the operator to perform a wide range of control functions and achieve functionality similar to that of conventional teleoperation schemes that use a single haptic interface. The mediator interface is presented, and important considerations such as workspace mapping and scaling are discussed. © 2015 IEEE.

Optimal trajectory and solution of the inverse kinematics of a robotic manipulator by genetic algorithms

This paper presents the generation of optimal trajectories by genetic algorithms (GA) for a planar robotic manipulator. The implemented GA considers a multi-objective function that minimizes the end-effector positioning error together with the joints angular displacement and it solves the inverse kinematics problem for the trajectory. Computer simulations results are presented to illustrate this implementation and show the efficiency of the used methodology producing soft trajectories with low computing cost. © 2011 Springer-Verlag Berlin Heidelberg.