Path Planning Of Robot Manipulators Research Articles

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper.

Preface

The 2022 2nd International Conference on Mechanical Automation and Electronic Information Engineering (MAEIE 2022) was successfully held on September 23rd-25th, 2022 in Guizhou, China via virtual format. MAEIE 2022 expanded channels of international academic exchange in science and technology, promoted scientific innovation on the global scale, and strengthen academic cooperation between China and the outside world.In the conference, we were greatly honored to have Prof. Ning Sun from Nankai University, China to serve as our Conference General Chair. 150 individuals around the world took part in the conference. Divided into three parts, the conference agenda covered keynote speeches, oral presentations and online Q&A discussion. Firstly, the keynote speakers were each allocated 30-45 minutes to address their speeches. Then in the oral presentations, the excellent papers we had selected were presented by their authors one by one.Three distinguished professors were invited to hold keynote speeches during the conference. Among them, Prof. Guanglei Wu from Dalian University of Technology addressed a speech on Path Planning of Industrial Robots for Obstacle Avoidance. In this presentation, the path planning for robotic manipulators was introduced, and by combining artificial potential field method and bidirectional rapidly exploring random tree, a BiRRT-APF algorithm was proposed, aiming to handle the problems of highly possible randomness and low search efficiency. Additionally, Prof. Imane Hilal from Faculty of Information Sciences, Morocco addressed a report on Researches on Artificial Intelligence for a Distant Social Learning: Do intelligent Technologies Beat Face-to-Face Learning? The main objective of this study was to present recent research works which focused on the use of AI technics in social distance learning. Then, he discussed the possibility of replacing teachers with technology as already done in many other jobs and concluded with the open challenges in the current distance learning and how AI could make learning more effective and joyful for both students and teachers. Their brilliant keynote speeches had triggered heated discussion in the conference. And every participant praised this conference for disseminating useful and insightful knowledge.We are glad to share with you that we’ve selected a bunch of high-quality papers from the submissions and compiled them into the proceedings after rigorously reviewing them. These papers feature but are not limited to the following topics: Vehicles and Carriages, Factory Modeling and Automation, Radar Engineering, Electronic Design Automation (EDA) Technology, etc. All the papers have been checked through rigorous review and processes to meet the requirements of publication.We would like to acknowledge all of those who supported MAEIE 2022 and made it a great success. In particular, we would like to thank the Journal of Physics: Conference Series, for the endeavor of all its colleagues in publishing this paper volume. We truly believe that all the attendees have had fruitful discussions and gained valuable knowledge, and will enjoy the opportunity for future collaborations.The Committee of MAEIE 2022List of Committee member is available in this pdf

Path planning of a 5-dof robotic arm based on BiRRT-APF algorithm considering obstacle avoidance

In robotic applications, an efficient path planning in a prescribed environment is the central issue, which is a challenging task for multiple degree-of-freedom articulated robotic arms. In this light, the path planning for robotic manipulators based on the combined artificial potential field method and bidirectional rapidly exploring random tree (BiRRT-APF) algorithm is proposed in this paper, aiming to handle the problems of highly possible randomness and low search efficiency. In order to improve the attraction function to speed up the efficiency of searching path, the characteristics of the Bi-RRT algorithm (i.e., bi-directional growth of random trees), is integrated to reduce the search time. Subsequently, a path construction strategy is proposed to remove the redundant nodes of the path, for the increased smoothness of the path and reduced memory storages of the algorithm. The effectiveness of the proposed path planning algorithm is illustrated with a 5-dof robotic arm, and is verified with MATLABTM and CoppeliaSimTM co-simulation, through a comparative study among different algorithms. The results show that the proposed BiRRT-APF algorithm can decrease the randomness of node growth and reduce the number of algorithm iterations, resulting in faster convergence of a smooth path for reduced computational burden.

Reaching Through Latent Space: From Joint Statistics to Path Planning in Manipulation

We present a novel approach to path planning for robotic manipulators, in which paths are produced via iterative optimisation in the latent space of a generative model of robot poses. Constraints are incorporated through the use of constraint satisfaction classifiers operating on the same space. Optimisation leverages gradients through our learned models that provide a simple way to combine goal reaching objectives with constraint satisfaction, even in the presence of otherwise non-differentiable constraints. Our models are trained in a task-agnostic manner on randomly sampled robot poses. In baseline comparisons against a number of widely used planners, we achieve commensurate performance in terms of task success, planning time and path length, performing successful path planning with obstacle avoidance on a real 7-DoF robot arm.

Planning Fail-Safe Trajectories for Space Robotic Arms.

A frequent concern for robot manipulators deployed in dangerous and hazardous environments for humans is the reliability of task executions in the event of a joint failure. A redundant robotic manipulator can be used to mitigate the risk and guarantee a post-failure task completion, which is critical for instance for space applications. This paper describes methods to analyze potential risks due to a joint failure, and introduces tools for fault-tolerant task design and path planning for robotic manipulators. The presented methods are based on off-line precomputed workspace models. The methods are general enough to cope with robots with any type of joint (revolute or prismatic) and any number of degrees of freedom, and might include arbitrarily shaped obstacles in the process, without resorting to simplified models. Application examples illustrate the potential of the approach.

Improved Distorted Configuration Space Path Planning and its Application to Robot Manipulators.

Real-time obstacle avoidance path planning is critically important for a robot when it operates in a crowded or cluttered workspace. At the same time, the computational cost is a big concern once the degree of freedom (DOF) of a robot is high. A novel path planning strategy, the distorted configuration space (DC-space) method, was proposed and proven to outperform the traditional search-based methods in terms of computational efficiency. However, the original DC-space method did not sufficiently consider the demands on automatic planning, convex space preservation, and path optimization, which makes it not practical when applied to the path planning for robot manipulators. The treatments for the problems mentioned above are proposed in this paper, and their applicability is examined on a three DOFs robot. The experiments demonstrate the effectiveness of the proposed improved distorted configuration space (IDCS) method on rapidly finding an obstacle-free path. Besides, the optimized IDCS method is presented to shorten the generated path. The performance of the above algorithms is compared with the classic Rapidly-exploring Random Tree (RRT) searching method in terms of their computation time and path length.

Collision Avoidance of Redundant Robotic Manipulators Using Newton’s Method

This study investigates the application of Newton method to the problems of collision avoidance and path planning for robotic manipulators, especially robots with high Degrees of Freedom (DOF). The proposed algorithm applies to the potential fields method, where the Newton technique is used for performing the optimization. As compared to classical gradient descent method this implementation is mathematically elegant, enhances the performance of motion generation, eliminates oscillations, does not require gains tuning, and gives a faster convergence to the solution. In addition, the paper presents a computationally efficient symbolic formula for calculating the Hessian with respect to joint angles, which is essential for achieving realtime performance of the algorithm in high DOF configuration spaces. The method is validated successfully in simulation environment. Results for different methods (Newton, gradient descent and gradient descent with momentum) are compared in terms of quality of the path generated, oscillations, minimum distance to obstacles and convergence rate.

IoT-Enabled Dual-Arm Motion Capture and Mapping for Telerobotics in Home Care.

With the paradigm shift from hospital-centric healthcare to home-centric healthcare in Healthcare 4.0, healthcare robotics has become one of the fastest growing fields of robotics. The combination of robot capabilities with human intelligence, for example, telerobotics for home care, is gradually showing promising potentials. In this paper, the Home-TeleBot system, a generalized IoT-enabled telerobotic architecture designed to support home-centric healthcare system, is proposed. In particular, the implementation of it is realized by integrating human-motion-capture subsystem with robot-control subsystem. The dual-arm cooperative robot, YuMi, imitates human motion captured by a set of wearable inertial motion capture devices to complete tasks. The proposed approach using workspace mapping and path planning of robot manipulators, facilitates telerobot to execute tasks in a natural and human-like way. Based on the constant of proportionality calculated by comparing the human original workspace with the robot original workspace, the workspace mapping is achieved by making assumptions of the distance between end-effectors (human hands, robot's grippers) and shoulders. Additionally, robot manipulators' path is planned by setting virtual obstacles to constrain robot motion, which aims to improve the performance of robot's human-like motion. As a specific example of application, we apply the proposed architecture to a fetching task based on dual-arm motion capture and mapping for telerobotics in home care.

Continuous Genetic Algorithms for Collision-Free Cartesian Path Planning of Robot Manipulators

A novel continuous genetic algorithm (CGA) along with distance algorithm for solving collisions-free path planning problem for robot manipulators is presented in this paper. Given the desired Cartesian path to be followed by the manipulator, the robot configuration as described by the D-H parameters, and the available stationary obstacles in the workspace of the manipulator, the proposed approach will autonomously select a collision free path for the manipulator that minimizes the deviation between the generated and the desired Cartesian path, satisfy the joints limits of the manipulator, and maximize the minimum distance between the manipulator links and the obstacles. One of the main features of the algorithm is that it avoids the manipulator kinematic singularities due to the inclusion of forward kinematics model in the calculations instead of the inverse kinematics. The new robot path planning approach has been applied to two different robot configurations; 2R and PUMA 560, as non-redundant manipulators. Simulation results show that the proposed CGA will always select the safest path avoiding obstacles within the manipulator workspace regardless of whether there is a unique feasible solution, in terms of joint limits, or there are multiple feasible solutions. In addition to that, the generated path in Cartesian space will be of very minimal deviation from the desired one.

Comparative analysis of collision-free path-planning methods for multi-manipulator systems

Motion planning for manipulators with many degrees of freedom is a complex task. The research in this area has been mostly restricted to static environments. This paper presents a comparative analysis of three reactive on-line path-planning methods for manipulators: the elastic-strip, strategy-based and potential field methods. Both the elastic-strip method [O. Brock and O. Khatib, “Elastic strips: A framework for integrated planning and execution,” Int. Symp. Exp. Robot. 245–254 (1999)] and the potential field method [O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res.5(1), 90–98 (1986)] have been adapted by the authors to the problem at hand related to our multi-manipulator system (MMS) (three manipulators with five degrees of freedom each). Strategy-based method is an original contribution by the authors [M. Mediavilla, J. L. González, J. C. Fraile and J. R. Perán, “Reactive approach to on-line path planning for robot manipulators in dynamic environments,” Robotica20, 375–384 (2002); M. Mediavilla, J. C. Fraile, T. González and I. J. Galindo, “Selection of strategies for collision-free motion in multi-manipulator systems,” J. Intell. Robot Syst38, 85–104 (2003)].The three methods facilitate on-line path planning for our MMS in dynamic environments with collision avoidance, where the three manipulators may move at the same time in their common workspace. We have defined some ‘basic motion problems’ for the MMS, and a series of simulations has been running that will tell us how effective each path-planning method is. The simulations have been performed and the obtained results have been analysed by using a software program developed by the authors.The paper also presents experimental results obtained applying the path-planning methods to our MMS, that perform pick-and-place tasks sharing common working areas.

Neural network approach to collision free path-planning for robotic manipulators

The paper deals with collision free path-planning for industrial robotic manipulators A new efficient approach is proposed that is based on the topologically ordered neural network model. This model describes harmonic potential field of the robot configuration space, sampled by the non-regular grid. The developed path-planning algorithm takes into account highly-irregular shape of the obstacles of welding and assembling robotic cells, and provides reduced number of collision checking. The stability of the topologically ordered neural network is investigated. The algorithm has been successfully applied to the off-line programming of a robotic manufacturing cell for the automotive industry.

Effect of basic numerical parameters on a path planning of robots taking into account actuating energy

This paper presents a study about the effect of numerical parameters on an optimal path planning of robot manipulators taking into account robot actuating energy and grasping forces in manipulator gripper. The mechanical power due to actuators for manipulator motion and energy for gripper action have been considered for the formulation of a multi-objective function. The optimization problem has been formulated as subject to physical constraints, input torque/force constraints and payload limits. In this paper we have reported results of a numerical investigation of the effect of main parameters on the solution characteristics of path planning for robotic manipulators. The study has been focused on a Puma like robot and results have been discussed to compare different numerical solutions of the proposed path planning.

Reactive approach to on-line path planning for robot manipulators in dynamic environments

This paper describes a new approach to path planning of robot manipulators with many degrees of freedom. It is designed for on-line motion in dynamic and unpredictable environments. The robots react to moving obstacles using a local and reactive algorithm restricted to a subset of its configuration space. The lack of a long-term view of local algorithms (local minima problems) is solved using an off-line pre-planning stage that chooses the subset of the configuration space that minimises the probability of not finding collision free paths. The approach is implemented and tested on a system of three Scorbot-er IX five link robots.

An offset error compensation method for improving ANN accuracy when used for position control of precision machinery

Artificial Neural Networks (ANNs) have recently become the focus of considerable attention in many disciplines, including robot control, where they can be used as a general class of nonlinear models to solve highly nonlinear control problems. Feedforward neural networks have been widely applied for modelling and control purposes. One of the ANN applications in robot control is for the solution of the inverse kinematic problem, which is important in path planning of robot manipulators. This paper proposes an iterative approach and an offset error compensation method to improve the accuracy of the inverse kinematic solutions by using an ANN and a forward kinematic model of a robot. The offset error compensation method offers potential to generate accurately the inverse solution for a class of problems which have an easily obtained forward model and a complicated solution.

Path planning for robotic manipulators with redundant degrees of freedom

The problem of path planning for robotic manipulators with redundant and nonredundant degrees of freedom is addressed. It is assumed that the motors for each joint are capable of achieving the commanded velocity within limits. Thus, the dynamic model is simplified and the main complexity is that of the kinematic relationships. Of primary interest is the problem of moving the end effector from point A to point B in an efficient manner, possibly in the presence of obstacles. A suboptimal solution is proposed and discussed. Examples are presented in order to compare the performance of the redundant and the nonredundant manipulators.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>