Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

Abstract

The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter.

An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning

“…APF is a mathematical method which causes a robot to be attracted to the goal but repelled by obstacles in the environment [6,34,36,37]. There are notable research works carried out using APF with sensors to plan the path of mobile robots to provide autonomous navigation, taking obstacle avoidance into consideration [12,[37][38][39][40][41][42][43][44][45][46][47]. The idea of applying APF to path planning originated from Khatib [34].…”

An approach for real-time motion planning of an inchworm robot in complex steel bridge environments

“…A VFF algorithm is combined with a FLC and boundary-follow (BF) algorithm on a simulated, circular mobile robot. 28 In normal operation, the VFF algorithm supplies information, such as obstacle positions and direction to the target, for the FLC to modulate the velocity of the robot. If the robot enters a local minima region, the BF algorithm is invoked and supplies information to the FLC instead.…”

A reflexive vehicle control architecture based on a neural model of the cockroach escape response

“…3,6 Tracking or avoiding obstacles moving on unknown or unpredictable paths is not well established. [10][11][12][13] Optimization criteria are complex and often involve multiple objectives; 13 real-time multiobjective path planning is not well established in short time frames without full consideration of other constraints. 8,14,15 Many guidance systems are not capable of generating on-line decisions quickly enough to be of use in time-critical situations, in particular for small multi-degree-of-freedom platforms.…”

“…Tracking or avoiding obstacles moving on unknown or unpredictable paths is not well established. 10–13…”

“…Figure13. Simulation results for three exemplary intercept cases for escape strategies for straight motion (a), random turning (b), and directed turning (c).…”