Motion Planning and Control of Redundant Manipulators for Dynamical Obstacle Avoidance

Abstract

This paper presents a framework for the motion planning and control of redundant manipulators with the added task of collision avoidance. The algorithms that were previously studied and tested by the authors for planar cases are here extended to full mobility redundant manipulators operating in a three-dimensional workspace. The control strategy consists of a combination of off-line path planning algorithms with on-line motion control. The path planning algorithm is used to generate trajectories able to avoid fixed obstacles detected before the robot starts to move; this is based on the potential fields method combined with a smoothing interpolation that exploits Bézier curves. The on-line motion control is designed to compensate for the motion of the obstacles and to avoid collisions along the kinematic chain of the manipulator; this is realized using a velocity control law based on the null space method for redundancy control. Furthermore, an additional term of the control law is introduced which takes into account the speed of the obstacles, as well as their position. In order to test the algorithms, a set of simulations are presented: the redundant collaborative robot KUKA LBR iiwa is controlled in different cases, where fixed or dynamic obstacles interfere with its motion. The simulated data show that the proposed method for the smoothing of the trajectory can give a reduction of the angular accelerations of the motors of the order of 90%, with an increase of less than 15% of the calculation time. Furthermore, the dependence of the on-line control law on the speed of the obstacle can lead to reductions in the maximum speed and acceleration of the joints of approximately 50% and 80%, respectively, without significantly increasing the computational effort that is compatible for transferability to a real system.

Research on Kinematics Analysis and Trajectory Planning of Novel EOD Manipulator

“…In the field of engineering, inverse kinematics has a higher application value and is the basis of robot motion planning and trajectory planning [15]. To obtain this closed-form solution, two sufficient conditions need to be followed [16][17][18]:…”

An Efficient Stochastic Constrained Path Planner for Redundant Manipulators

“…The method also confers the robot with the ability to avoid collisions, by accounting for the velocity of dynamic obstacles and previewing its next position in order to plan the optimal correction of the trajectory. This method was further extended to the case where redundant manipulators operate in a spatial workspace, with the added capability of avoiding sphere-like obstacles [9]. Finally, [10] deals with an optimization problem in the case where the robot is redundant along the end effector's tool axis.…”

Analysis of Obstacle Avoidance Strategy for Dual-Arm Robot Based on Speed Field with Improved Artificial Potential Field Algorithm

“…According to the application of robot in production and in life, it can be known that the obstacles include two types, namely static obstacles and dynamic obstacles, and the collision avoidance problem is essentially a robot path planning problem. Lots of researchers have spent a lot of time and energy on the above issue for deep study and have put forward many classic algorithms, such as artificial potential field method (APF) [8][9][10], rapidly exploring random tree (RRT) [11][12][13], C-space [14,15], grid-based algorithms [16,17], and a new novel approach for the application of dynamic safety zones based on the requirements of safety standards for collaborative robotics [18]. Among the above methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods.…”