NEO: A Novel Expeditious Optimisation Algorithm for Reactive Motion Control of Manipulators

We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadratic programme which, when considering obstacles, joint limits, and manipulability on a 7 degree-of-freedom robot, is generally solved in a few ms. While NEO is not intended to replace state-of-the-art motion planners, our experiments show that it is a viable alternative for scenes with moderate complexity while also being capable of reactive control. For more complex scenes, NEO is better suited as a reactive local controller, in conjunction with a global motion planner. We compare NEO to motion planners on a standard benchmark in simulation and additionally illustrate and verify its operation on a physical robot in a dynamic environment. We provide an open-source library which implements our controller.Comment: IEEE Robotics and Automation Letters (RA-L). Preprint Version. Accepted January, 2021. The code and videos can be found at https://jhavl.github.io/neo

SELF-COLLISION AVOIDANCE OF ARM ROBOT USING GENERATIVE ADVERSARIAL NETWORK AND PARTICLES SWARM OPTIMIZATION (GAN-PSO)

Collision avoidance of Arm Robot is designed for the robot to collide objects, colliding environment, and colliding its body. Self-collision avoidance was successfully trained using Generative Adversarial Networks (GANs) and Particle Swarm Optimization (PSO). The Inverse Kinematics (IK) with 96K motion data was extracted as the dataset to train data distribution of  3.6K samples and 7.2K samples. The proposed method GANs-PSO can solve the common GAN problem such as Mode Collapse or Helvetica Scenario that occurs when the generator  always gets the same output point which mapped to different input  values. The discriminator  produces the random samples' data distribution in which present the real data distribution (generated by Inverse Kinematic analysis).  The PSO was successfully reduced the number of training epochs of the generator  only with 5000 iterations. The result of our proposed method (GANs-PSO) with 50 particles was 5000 training epochs executed in 0.028ms per single prediction and 0.027474% Generator Mean Square Error (GMSE)

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

Increasing the Automation Level of Serial Robotic Manipulators with Optimal Design and Collision-free Path Control

The current hydraulic robotic manipulator mechanisms for heavy-duty machines are a mature technology, and their kinematics has been developed with a focus on the human operator maneuvering a hydraulically controlled system without numerical control input. As the trend in heavy-duty manipulators is increased automation, computer control systems are increasingly being widely used, and the requirements for robotic manipulator kinematics are different. Computer control enables a different kind of robotic manipulator kinematics, which is not optimum for direct control by a human operator, because the joint motions related to the different trajectories are not native for the human mind. Numerically controlled robotic manipulators can accept kinematics that is more efficient at doing the job expected by the customer.To increase the autonomous level of robotic manipulator, the optimal structure is not enough, but it is a part of the solution toward a fully autonomous manipulator. The control system of the manipulator is the main part of computer-controlled manipulators. A collision avoidance system plays an important role in the field of autonomous robotics. Without collision avoidance functionality, it is quite obvious that only very simple movements and tasks can be carried out automatically. With more complicated movement and manipulators, some kind of collision avoidance system is required. An unknown or changing environment increases the need for an intelligent collision avoidance system that can find a collision-free path in a dynamic environment.This thesis deals with these fundamental challenges by optimizing the serial manipulator structure for the desired task and proposing a collision avoidance control system. The basic requirement in the design of such a robotic manipulator is to make sure that all the desired task points can be achieved without singularities. These properties are difficult to achieve with the general shape and type of robotic manipulators. In this research work, a task-based kinematic synthesis approach with the proper optimization method ensures that the desired requirements can be fulfilled.To enable autonomous task execution for robotic manipulators, the control systems must have a collision avoidance system that can prevent different kinds of collisions. These collisions include self-collisions, collisions with other manipulators, collisions with obstacles, and collisions with the environment. Furthermore, there can be multiple simultaneous possible collisions that need to prevented, and the collision system must be able to handle all these collisions in real-time. In this research work, a real-time collision avoidance control approach is proposed to handle these issues. Overall, both topics, covered in this thesis, are believed to be key elements for increasing the automation of serial robotic manipulators

Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

Manipulator-based grasping pose selection by means of task-objective optimisation

This paper presents an alternative to inverse kinematics for mobile manipulator grasp pose selection which integrates obstacle avoidance and joint limit checking into the pose selection process. Given the Cartesian coordinates of an object in 3D space and its normal vector, end-effector pose objectives including collision checking and joint limit checks are used to create a series of cost functions based on sigmoid functions. These functions are optimised using Levenberg-Marquardt's algorithm to determine a valid pose for a given object. The proposed method has been shown to extend the workspace of the manipulator, eliminating the need for precomputed grasp sets and post pose selection collision checking and joint limit checks. This method has been successfully used on a 6 DOF manipulator both in simulation and in the real world environment

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research