Motion Planning Of Robot Manipulators Research Articles

Kinematic Graph for Motion Planning of Robotic Manipulators

We introduce a kinematic graph in this article. A kinematic graph results from structuring the data obtained from the sampling method for sampling-based motion planning algorithms in robotics with the motivation to adapt the method to the positioning problem of robotic manipulators. The term kinematic graph emphasises the fact that any path computed by sampling-based motion planning algorithms using a kinematic graph is guaranteed to correspond to a feasible motion for the positioning of the robotic manipulator. We propose methods to combine the information from the configuration and task spaces of the robotic manipulators to cluster the samples. The kinematic graph is the result of this systematic clustering and a tremendous reduction in the size of the problem. Hence, using a kinematic graph, it is possible to effectively employ sampling-based motion planning algorithms for robotic manipulators, where the problem is defined in higher dimensions than those for which these algorithms were developed. Other barriers that hindered adequate utilisation of such algorithms for robotic manipulators with articulated arms, such as the non-injective surjection of the forward kinematic function, are also addressed in the structure of the kinematic graph.

Repetitive Motion Planning of Robotic Manipulators With Guaranteed Precision

Repetitive motion planning (RMP) plays a remarkable role in the operation of robotic manipulators. In this article, the RMP of robotic manipulators wherein the high precision of joint angle repeatability and end-effector motion is guaranteed is investigated. In particular, a novel pseudoinverse-based (P-based) RMP scheme is designed and proposed for robotic manipulators by applying a special difference rule to discretize the existing RMP scheme with P-based formulation. Such scheme is theoretically analyzed and proven to simultaneously guarantee joint angle repetitive precision and end-effector motion precision. Comparative simulation results of a five-link robotic manipulator and a universal robotic manipulator are provided to verify the effective performance of the proposed P-based RMP scheme. The physical realizability of the proposed scheme is further substantiated by implementing the scheme on a practical EPSON robotic manipulator.

Motion Planning of Robot Manipulators for a Smoother Path Using a Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay

In order to enhance performance of robot systems in the manufacturing industry, it is essential to develop motion and task planning algorithms. Especially, it is important for the motion plan to be generated automatically in order to deal with various working environments. Although PRM (Probabilistic Roadmap) provides feasible paths when the starting and goal positions of a robot manipulator are given, the path might not be smooth enough, which can lead to inefficient performance of the robot system. This paper proposes a motion planning algorithm for robot manipulators using a twin delayed deep deterministic policy gradient (TD3) which is a reinforcement learning algorithm tailored to MDP with continuous action. Besides, hindsight experience replay (HER) is employed in the TD3 to enhance sample efficiency. Since path planning for a robot manipulator is an MDP (Markov Decision Process) with sparse reward and HER can deal with such a problem, this paper proposes a motion planning algorithm using TD3 with HER. The proposed algorithm is applied to 2-DOF and 3-DOF manipulators and it is shown that the designed paths are smoother and shorter than those designed by PRM.

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

Different from general linear inequality or equality, the problem of future different-level linear inequality and equality (FDLLIE) is investigated, which is much more interesting and challenging. In order to solve this difficult FDLLIE, continuous different-level linear inequality and equality (CDLLIE) is first considered. A zeroing equivalency theorem is proposed based on the zeroing neural network method, and then a continuous solution model is, thus, obtained for CDLLIE solving. Furthermore, a new discrete-solution (NDS) model is developed for FDLLIE solving by using a proposed new 7-instant Zhang et al. discretization (ZeaD) formula to discretize the continuous solution model. Meanwhile, theoretical analyses and results are presented to show the excellent properties of the NDS model. Numerical results illustrate the effectiveness and superiority of the NDS model for solving FDLLIE. Furthermore, application experiments for motion planning of robot manipulator are conducted to substantiate the efficacy of the NDS model for FDLLIE solving.

Motion Planning of Robot Manipulator Based on Improved NSGA-II

In this paper, the trajectory of a robot manipulator is planned using the non-dominated sorting genetic algorithm II (NSGA-II). Moreover, consumed time, Cartesian trajectory length, and smooth movement are used as the multi-objective to be optimized [1, 2]. The Pareto optimal solution set is obtained through NSGA-II, and simulation is used to obtain and verify the results. In an actual engineering case, the optimal solution of the Pareto solution set can be selected as the optimal path of a robot manipulator. Results show that the relationship between consumed time and joint jerk is a priority solution to practical engineering selection. Moreover, the spatial distribution of the optimal solution set is improved by enhancing the proposed crowding distance mechanism in the conventional NSGA-II algorithm.

Motion planning for robotic manipulators using robust constrained control

Since their first appearance in the 1970's, industrial robotic manipulators have considerably extended their application fields, allowing end-users to adopt this technology in previously unexplored scenarios. Correspondingly, the way robot motion can be specified has become more and more complex, requiring new capabilities to the robot, such as reactivity and adaptability. For an even enhanced and widespread use of industrial manipulators, including the newly introduced collaborative robots, it is necessary to simplify robot programming, thus allowing this activity to be handled by non-expert users. Next generation robot controllers should intelligently and autonomously interpret production constraints, specified by an application expert, and transform them into motion commands only at a lower and real-time level, where updated sensor information or other kind of events can be handled consistently with the higher level specifications. The availability of several execution strategies could be then effectively exploited in order to further enhance the flexibility of the resulting robot motion, especially during collaboration with humans.This paper presents a novel methodology for motion specification and robust reactive execution. Traditional trajectory generation techniques and optimisation-based control strategies are merged into a unified framework for simultaneous motion planning and control. An experimental case study demonstrates the effectiveness and the robustness of this approach, as applied to an image-guided grasping task.

Motion planning of robot manipulator for dynamic and constrained space with posture enumeration

Motion planning of robot manipulator for dynamic and constrained space with posture enumeration

Safety-oriented path planning for articulated robots

SUMMARYThis work presents an approach to motion planning for robotic manipulators that aims at improving path quality in terms of safety. Safety is explicitly assessed using the quantity calleddanger field. The measure of safety can easily be embedded into a heuristic function that guides the exploration of the free configuration space. As a result, the resulting path is likely to have substantially higher safety margin when compared to one obtained by regular planning algorithms. To this end, four planning algorithms have been proposed. The first planner is based on volume trees comprised of bubbles of free configuration space, while the remaining ones represent modifications of classical sampling-based algorithms. Several numerical case studies are carried out to validate and compare the performance of the presented algorithms with respect to classical planners. The results indicate significantly lower danger metric for paths obtained by safety-oriented planners even with some decrease in running time.

Fault tolerant motion planning of robotic manipulators based on a nested RRT algorithm

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.

Building and navigating a network of local minima

AbstractWe present a novel method that constructs and navigates a network of local minima of potential fields defined over multidimensional spaces. Though motivated by problems of motion planning for robotic manipulators, similar techniques have been proposed for use in other domains such as molecular chemistry and drug design. The method is based on building a roadmap of paths connecting local minima of a potential function. The novel approach consists of an up‐hill search strategy used to climb out of local minima and find new nearby local minima, without doubling back on previous local minima. With this up‐hill search strategy, one can find local minima otherwise difficult to encounter, and one can focus the search to specific local minima and specific directions from those local minima. The construction of the roadmap can be done in parallel with very little communication. We present extensive simulation results. © 2001 John Wiley & Sons, Inc.

Optimal Motion Planning of Robotic Manipulators Removing Mobile Objects Grasped in Motion

The following study deals with motion optimization of robot arms having to transfer mobile objects grasped when moving. This approach is aimed at performing repetitive transfer tasks at a rapid rate without interrupting the dynamics of both the manipulator and the moving object. The junction location of the robot gripper with the object, together with grasp conditions, are partly defined by a set of local constraints. Thus, optimizing the robot motion in the approach phase of the transfer task leads to the statement of an optimal junction problem between the robot and the moving object. This optimal control problem is characterized by constrained final state and unknown traveling time. In such a case, Pontryagin"s maximum principle is a powerful mathematical tool for solving this optimization problem. Three simulated results of removing a mobile object on a conveyor belt are presented; the object is grasped in motion by a planar three-link manipulator.

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.

Evolutionary Optimisation for Robot Motion

This paper presents a detailed analysis of a motion planner based on genetic algorithms for collision free motion planning of robotic manipulators through simulation. The problem is formulated for a 2-DOF planar manipulator moving in the presence of a static circular obstacle in its operational space. The algorithm is then extended to 3-DOF planar manipulator moving among multiple static obstacles.

関節空間における障害物回避パス探索の一方法

関節空間における障害物回避パス探索の一方法

Collision-free motion of an articulated kinematic chain in a dynamic environment

A method for the collision avoidance of an articulated kinematic chain in a dynamic environment is presented. The inverse kinematic solution is used to get the configuration of the chain for a given position of any part of the chain. The calculation of collision-free motion is based on the local information about the motion. Intermediate positions are used to get the new motion path of the chain. The movement of environmental objects during the collision detection and the generation of new motions are considered. This generalizes the method for arbitrarily configured kinematic chains and for a dynamic environment. Its implementation for the motion planning of robot manipulators in an offline programming system is briefly described. >