A biologically inspired neural net for trajectory formation and obstacle avoidance

Abstract

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

Biologically inspired modelling for the control of the upper limb movements: from concept studies to future applications

“…The movements executed by an 11 Degrees Of Freedom (DOF) human model driven by this controller are similar to the real ones. Sensory information for movement planning was studied also in Glasius et al (1996), where the issues of trajectory formation and obstacle avoidance are solved using a two-layer biologically inspired ANN, trained in an unsupervised way. The layers respectively represent the sensory map, which builds up the activity patterns from sensory information, and the time-evolving motor map, transforming activity patterns into motor commands.…”

“…Most studies aimed indeed at proposing a novel model generally validated by comparing real movements with the artificial ones executed by a virtual arm. Among the few works successfully applied to real contexts, the control scheme proposed in Kawato et al (1988), was used to control an industrial manipulator (Miyamoto et al, 1987) and the study by Glasius et al (1996) was tested to drive both a mechanical manipulator and an artificial arm.…”

“…Special attention will be instead devoted to those examples where the application tackles the motor impairment as an effect of neuromuscular disorders. Then the authors will concentrate on future applications of the biologically inspired modelling as envisaged by some proposals ranging from robotics (Miyamoto et al, 1987; Glasius et al, 1996), to rehabilitation (Lan et al, 1994; Goffredo et al, 2008). …”

Scale-Free Navigational Planning by Neuronal Traveling Waves

“…In some form such a 2-step backspread–forwardtrack procedure is present in all of today’s graph search algorithms, and it is difficult to imagine solutions of the planning problem which do not involve this core idea. Accordingly, various neuronal planning models have considered the backpropagation of activity from the goal across a topological map of the environment towards the start position [ 7 – 11 ]. However, these models suffer from an exponential decay of activity with distance from the goal.…”

Routing Optimization of Intelligent Vehicle in Automated Warehouse

“…There are some problems in the neural network system, such as networks large-scale, common performance, and easy to make a robot into an infinite loop. Glasius et al [6] proposed a neural network model based on Hopfield network with dynamically avoiding obstacles. The model can avoid local minimum point, but it is difficult to adapt to the dynamic and high speed environment.…”