As 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.

The main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.

In this paper, we present an on-line task modification method (OTMM) to realize singularity avoidance for nonredundant and redundant manipulators at the velocity level. The method introduces a correction vector, constructed from the task velocity and the singular vector corresponding to the minimum singular value, into the task velocity. The performance is simply affected by the choice of the lower limit of the minimum singular value and a scalar adjusting function, which is monotone with respect to the minimum singular value. The method makes unnecessary avoiding the singularity point by off-line path planning for nonredundant or redundant manipulators, and the effort to check whether the singularity is escapable for redundant manipulators. The simulation results show the effectiveness of the OTMM for on-line singularity avoidance in manipulator motion control.

The robotic motion planning criteria has evolved from kinematics to dynamics in recent years. Many research achievements have been made in dynamic motion planning, but the externally applied loads are usually limited to the gravity force. Due to the increasing demand for generic tasks, the motion should be generated for various functions such as pulling, pushing, twisting, and bending. In this paper, a comprehensive form of equations of motion, which includes the general external loads applied at any point of branched tree structures, is implemented. An optimization-based algorithm is then developed to generate load-effective motions of redundant tree-structured systems for generic tasks. A highly articulated dual-arm human model is used to generate different effective motions to sustain different external load magnitudes. The results also provide a new scientific insight of human motion.