Motion Planning Of Redundant Robots Research Articles

A sparsity-based method for fault-tolerant manipulation of a redundant robot

AbstractAs an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots’ joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity.

Predefined-Time Convergent Neural Solution to Cyclical Motion Planning of Redundant Robots Under Physical Constraints

In industrial activities, robot arms are usually requested to perform repetitive tasks. As a special recurrent neural network, zeroing neural network (ZNN) has been successfully applied to cyclical motion planning (CMP) of redundant robots. However, most of the existing ZNN models for CMP have not taken into account physical limits of robot arms. Unfortunately, if physical limits are violated, the robot will fail to complete the task and even be damaged in practice. To overcome this problem, this article proposes a novel ZNN model for CMP of physically constrained redundant robots. The ZNN model is endowed with predefined-time convergence by using various designed activation functions. Rigorous theoretical analysis is conducted to prove the predefined-time convergence of the accelerated ZNN model. Numerical experiments are then comparatively performed, showing that the ZNN model delivers superior convergence and it is applicable to CMP of redundant robots with physical constraints considered.

Optimal Robot Motion Planning of Redundant Robots in Machining and Additive Manufacturing Applications

The paper deals with the generation of optimal trajectories for industrial robots in machining and additive manufacturing applications. The proposed method uses an Ant Colony algorithm to solve a kinodynamic motion planning problem. It exploits the kinematic redundancy that is often present in these applications to optimize the execution of trajectory. At the same time, the robot kinematics and dynamics constraints are respected and robot collisions are avoided. To reduce the computational burden, the task workspace is discretized enabling the use of efficient network solver based on Ant Colony theory. The proposed method is validated in robotic milling and additive manufacturing real-world scenarios.

Task-Constrained Optimal Motion Planning of Redundant Robots Via Sequential Expanded Lagrangian Homotopy

Real-time motion planning of robots in a dynamic environment requires a continuous evaluation of the determined trajectory so as to avoid moving obstacles. This is even more challenging when the robot also needs to perform a task optimally while avoiding the obstacles due to the limited time available for generating a new collision-free path. In this paper, we propose the sequential expanded Lagrangian homotopy (SELH) approach, which is capable of determining the globally optimal robot's motion sequentially while satisfying the task constraints. Through numerical simulations, we demonstrate the capabilities of the approach by planning an optimal motion of a redundant mobile manipulator performing a complex trajectory. Comparison against existing optimal motion planning approaches, such as genetic algorithm (GA) and neural network (NN), shows that SELH is able to perform the planning at a faster rate. The considerably short computational time opens up an opportunity to apply this method in real time; and since the robot's motion is planned sequentially, it can also be adjusted to accommodate for dynamically changing constraints such as moving obstacles.

Equivalence of Different-level Schemes for Repetitive Motion Planning of Redundant Robots

摘要: 探讨冗余机械臂之重复运动规划(Repetitive motion planning, RMP), 对应速度层重复运动规划方案, 提出了加速度层重复运动规划方案, 并进一步发现和探讨了速度层与加速度层RMP方案的等效性关系. 基于神经动力学方法的理论分析和基于PUMA560机械臂的计算机仿真结果均证实了本文所提出的加速度层RMP方案在冗余度解析问题上的有效性, 以及速度层和加速度层RMP方案的等效性. 关键词: 等效性 / 重复运动规划 / 神经动力学方法 / 机械臂 / 二次规划

Equivalence of Different-level Schemes for Repetitive Motion Planning of Redundant Robots

A repetitive-motion-planning (RMP) scheme at the acceleration-level of redundant robot manipulators is proposed, and its equivalence to the RMP scheme at velocity-level is discovered and investigated. Both theoretical analysis via neural-dynamic method and computer simulations performed on PUMA 560 robot manipulator demonstrate the efficacy of the proposed acceleration-level RMP scheme, and validate the equivalence of the RMP schemes at two different levels.

Motion planning of redundant robots by perturbation method

A redundant robot for the specific applications to trajectory tracking, singularity avoidance, and obstacle avoidance is considered in this paper. This paper also presents an optimal path planning approach to minimize the cost of moving a redundant robot manipulator along a specified geometric path subject to angle change constraints. The proposed motion planning approach using the forward kinematics and the perturbation method can be applied to solve the path planning problem with geometrical singularity and task priority requirements, such as obstacle avoidance and singularity avoidance. The solution is obtained according to the design objective using an optimization procedure. The experiments using the RV-M2 robot of MITSUBISHI are also presented.

Motion planning of redundant robots

Motion planning of robot manipulators is one of the most challenging problems encountered in the field of robotics. This article concentrates on those problems that require geometrical singularity and task priority. An alternative to the conventional approach is proposed, using a forward kinematics and optimization technique. The solution can be obtained corresponding to the design objective using an optimization procedure. The robustness of the proposed method to geometrical singularity is demonstrated. The concept of the permissible region is proposed for the motion planning problem with obstacle avoidance. Illustrated examples are also given.© 1997 John Wiley & Sons, Inc.

Motion planning of redundant robots

Motion planning of redundant robots