Obstacle Avoidance for Kinematically Redundant Manipulators Using a Dual Neural Network

Abstract

One important issue in the motion planning and control of kinematically redundant manipulators is the obstacle avoidance. In this paper, a recurrent neural network is developed and applied for kinematic control of redundant manipulators with obstacle avoidance capability. An improved problem formulation is proposed in the sense that the collision-avoidance requirement is represented by dynamically-updated inequality constraints. In addition, physical constraints such as joint physical limits are also incorporated directly into the formulation. Based on the improved problem formulation, a dual neural network is developed for the online solution to collision-free inverse kinematics problem. The neural network is simulated for motion control of the PA10 robot arm in the presence of point and window-shaped obstacle.

A new variant of the Zhang neural network for solving online time-varying linear inequalities

“…In recent years, the online solution of LMVIs (viewed as a special case of LMIs) has been considered to be an important problem encountered in the science and engineering fields (Xia et al 1999;Zhang & Wang 2004;Zhang 2006) (e.g. online solution of time-varying inequality constraint for obstacle avoidance of redundant robot manipulators).…”

“…For example, an inequality-based criterion/constraint has been recently proposed, introduced and investigated by Zhang et al for obstacle avoidance of redundant robot manipulators (Zhang & Wang 2004), which is used to generate escape velocities of variable magnitude, and then drive the affected links away from obstacles. Thus, robot manipulators can avoid obstacles successfully by solving such linear matrix-vector inequalities (LMVIs) in real time t (Zhang & Wang 2004).…”

“…However, when such a GNN model is applied to solving LIs in the situation of having timevarying coefficients (e.g. the aforementioned time-varying LMVIs for obstacle avoidance of redundant robot manipulators (Zhang & Wang 2004)), a faster convergence rate is often required when compared with the variational rate of time-varying coefficients. This may thus impose stringent restrictions on the GNN's physical realization and/or sacrifice the solution precision.…”

Motion planning with obstacle avoidance for kinematically redundant manipulators based on two recurrent neural networks

“…For obstacle avoidance, it is often demanded to determine the critical point on the manipulator that has the minimum (or near minimum) distance to obstacles. However, in some recent studies [2], [3] the critical points were assumed to be known a priori. This assumption oversimplified real situations.…”

“…Many strategies about the velocity control have been reported. A popular approach is to solve a formulated optimization problem [2], [6], [7]. This approach has several advantages including practical considerations such as joint limits, joint velocity limits and obstacle avoidance into the optimization problem as constraints.…”