New Discrete-Solution Model for Solving Future Different-Level Linear Inequality and Equality With Robot Manipulator Control

Robotic manipulators designed for home assistance and new surgical procedures often have significant uncertainty in their actuation due to compliance requirements, cost constraints, and size limits. We introduce a new integrated motion planning and control algorithm for robotic manipulators that makes safety a priority by explicitly considering the probability of unwanted collisions. We first present a fast method for estimating the probability of collision of a motion plan for a robotic manipulator under the assumptions of Gaussian motion and sensing uncertainty. Our approach quickly computes distances to obstacles in the workspace and appropriately transforms this information into the configuration space using a Newton method to estimate the most relevant collision points in configuration space. We then present a sampling-based motion planner based on executing multiple independent rapidly exploring random trees that returns a plan that, under reasonable assumptions, asymptotically converges to a plan that minimizes the estimated collision probability. We demonstrate the speed and safety of our plans in simulation for (1) a 3-D manipulator with 6 DOF, and (2) a concentric tube robot, a tentacle-like robot designed for surgical applications.

In recent years, a number of trajectory optimization algorithms have been proposed and established for motion planning of robot manipulators in complex, but static, predefined environments. To enable reactive motion planning under uncertain conditions caused, for example, by moving obstacles, this paper proposes a formulation of the trajectory optimization problem that is tailored for model predictive control. The proposed algorithmic solution leverages off-the-shelf computational tools for nonlinear model predictive control, optimization, and collision checking. In addition, a motion planning paradigm is introduced to allow for online collision-free motion when following a joystick command. The approach is validated in the context of an industrial pick-and-place application using MATLAB® and a Kinova® robot manipulator, both in simulation and with actual hardware.

Real-time motion planning and control of multi-joint robot manipulators are studied using neural networks. The proposed neural network approach consists of two modules: one for real-time collision-free motion planning, the other for real-time fine control of the robot manipulators. The motion planning module is a biologically inspired, parallel connected, topologically organised neural network, where each neuron is characterised by a shunting equation, whose state space is the robot configuration joint space. This module is capable of planning a real-time optimal path for robot manipulators through the dynamic activity landscape of the neural network. The motion control module involves of a feedforward neural network together with a traditional PD feedback loop. It is capable of achieving real-time fine motion control of robot manipulators under significant uncertainties and without any prior knowledge of the robot dynamics. This module can quickly compensate sudden changes in the robot dynamics. The real-time fine control of robot manipulators is achieved through the online learning of the neural network. Both the motion planning and motion control modules are computationally efficient, and their global stabilities are proved using Lyapunov stability theory.

PurposeThe purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.Design/methodology/approachAnother RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.FindingsIt is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.Originality/valueThe paper presents a new nested RRT algorithm for fault tolerant motion planning.

This paper describes the use of the probabilistic motion planning technique SBL "single-query bidirectional probabilistic algorithm with lazy collision checking" or in motion planning for robot manipulators. We present a novel strategy to remedy the PRM "probabilistic roadmap" paths which are both excessively long and discontinuous in velocity and acceleration. The optimization of the path will be done first through coarse optimal lazy A* optimization and then through a fine cutting-triangles-edge one, the edges discontinuities are smoothed with cubic polynomials taking the robot's specific dynamic and cinematic constraints. The whole strategy is applied to the 6 axes robot Manipulator MOTOMAN SV3X.

This paper presents a new inverse kinematics algorithm of 6-DOF robot manipulator via the analytic solution with Conformal Geometric Algebra (CGA). To simplify the calculation, angles of joints 1, 5 and 6 are obtained using the analytic method. On the other hand, the remaining joint angles θ 2 , θ 3 and θ 4 are figured out by CGA which describes the geometric relationship intuitively. The proposed method is verified and simulated in Matlab and Rviz, respectively. Experimental results show the high accuracy of proposed approach. Moreover, it can be easily integrated in the motion planning of robot manipulator.

Objectives. The work analyzes features of one of the most promising approaches to solve the problems for motion planning of autonomous robotic manipulators of various types and purposes using the rapidly exploring random tree (RRT) method. The development of modern robotics is shown to be inextricably linked with the improvement of the designs of the created samples, for which the placement of a manipulator on platform becomes a typical layout option. Prospects for using the RRT method as a constructive basis for creating a universal motion planner are evaluated for mobile and robotic manipulators, including autonomous robotic systems with a manipulator on a moving platform.Methods. The object of the research is the RRT method and its well-known modifications RRT* and RRT-Connect. The effectiveness of applying such methods for solving problems associated with planning the motions of robotic manipulators of various types was evaluated using computer and natural simulation methods.Results. Based on a review of the literature and the results of the research, the wide possibilities of the RRT method can be used for solving motion planning problems not only for mobile and robotic manipulators, but also for robotic systems on whose transport platform an onboard manipulator has been installed (including those having a redundant or reconfigurable structure). The effectiveness of the applied application of the RRT method is confirmed by examples of modeling a mobile platform with an onboard manipulator and the results of full-scale experiments with a prototype of the ARAKS reconfigurable mechatronic-modular robotic manipulators (RTU MIREA, Russia). It can be experimentally demonstrated and theoretically substantiated that the final dimension of the exploring tree, and hence the time of its construction up to reaching a given target state, is largely determined by the value of the growth factor.Conclusions. The generalization of the results obtained opens up real prospects for using the RRT method as a constructive basis not only for creating universal means for motion planning mobile robotic systems with an onboard manipulator, but also for solving the problems of automating the docking of autonomous mobile platforms.

Trajectory planning and tracking are crucial tasks in any application using robot manipulators. These tasks become particularly difficult when obstacles are present in the manipulatorpsilas workspace. In this paper it is assumed that the obstacles can be approximated in a conservative way with discs. The goal is to represent the obstacles in the robot configuration space, in order to allow an efficient and accurate trajectory planning and tracking. Moreover, the paper provides the methods for checking the collision between the n-joint manipulator and the obstacles. Trajectory planning depends on tracking accuracy. In this paper an adequate tracking accuracy is guaranteed assuming the use of a suitably designed robust controller.

This paper presents the current state of the art in the adaptive control of single rigid robotic manipulators in the constrained motion tasks. A complete mathematical model of a single rigid robotic manipulator in contact with dynamic environment is presented. The basic approaches in deriving the environment model are given. The significance of the dynamic environment in the scope of the stability problem of the whole system robot-dynamic environment is emphasized. A classification of the adaptive contact control concepts in manipulation robotics is presented. The main characteristics of the most important adaptive strategies in constrained manipulation are given. The advantages and the drawbacks of the presented methods are emphasized. The paper covers results published a few years ago, as well as some recent trends in this field. One important result in the stability analysis of robotic manipulators in the constrained motion tasks is reported. Finally, some concluding remarks are given and possible future investigation trends in adaptive control of robotic manipulators are indicated.

The paper proposes a method of controlling robotic manipulators with use of human gestures and movement. Experiments were performed with the use of 4 degree-of-freedom AX-12 Robotic Arm manipulator with force gripper and ASUS Xtion depth sensor also called motion controller. Depth and video capture has been done via OpenNI library. The infrastructure is based on Windows Communication Foundation (WCF) for remote access, authorization, multimedia streaming and servo control. Control of robotic manipulator is implemented with use of human computer interaction algorithm basing on depth sensor information.

Currently used methods for the analysis of contingency tables with log-linear models and their extensions are essentially a way to subject the parameters of the saturated model to a set of linear constraints. However, in many contexts, expecially when a subset of the variables are ordinal, it would be very useful to be able to consider also linear inequality constraints. In this paper we show how to extend previous models for contingency tables by considering simultaneously both equality constraints. We also give a self contained discussion of the problem of testing equality and inequality constraints in the context of contingency tables analysis.

Position-based visual servo control of autonomous robotic manipulators

Robot manipulators have been extensively used in complex environments to complete diverse tasks. The teleoperation control based on human-like adaptivity in the robot manipulator is a growing and challenging field. This paper developed a disturbance-observer-based fuzzy control framework for a robot manipulator using an electromyography- (EMG-) driven neuromusculoskeletal (NMS) model. The motion intention (desired torque) was estimated by the EMG-driven NMS model with EMG signals and joint angles from the user. The desired torque was transmitted into the desired velocity for the robot manipulator system through an admittance filter. In the robot manipulator system, a fuzzy logic system, utilizing an integral Lyapunov function, was applied for robot manipulator systems subject to model uncertainties and external disturbances. To compensate for the external disturbances, fuzzy approximation errors, and nonlinear dynamics, a disturbance observer was integrated into the controller. The developed control algorithm was validated with a 2-DOFs robot manipulator in simulation. The results indicate the proposed control framework is effective and crucial for the applications in robot manipulator control.

Adaptive prescribed settling time periodic event-triggered control for uncertain robotic manipulators with state constraints

Robotic manipulators are highly coupled multi-input multi-output (MIMO) nonlinear systems with uncertainties and highly time-varying dynamic capabilities. These characteristics make the trajectory control of a robotic manipulator very challenging. This paper presents the modeling and trajectory tracking control of a 3-DOF robotic manipulator using self-tuning fuzzy sliding mode controller (ST-FSMC). The stability of the system was investigated using the Lyapunov direct method. The controller was implemented using MATLAB/Simulink and its performance was evaluated. The simulation results show that the proposed controller removed chattering phenomena from the control effort and reduced the tracking error (average steady-state error) to 0.0036 rad. However, in the case of the conventional controllers, the average steady-state e rror increased to 0.0413 rad, 0.0044 rad, and 0.0053 rad for the Proportional-integral-derivative (PID) controller, Sliding mode controller (SMC), and Fuzzy sliding mode controller (FSMC), respectively. The conventional controllers (PID, SMC, and FSMC) were designed for the purpose of comparison with the proposed ST-FSMC. The simulation results show that the designed controller (ST-FSMC) has a superior tracking performance, is robust and is not sensitive to applied model parameter variations as compared to other conventional controllers.