Fuzzy logic based speed planning for autonomous navigation under Velocity Field Control

Abstract

Velocity fields techniques are often used in the path planning and control problem in autonomous navigation. Velocity fields are compounded by vectors that can be expressed in polar form, i.e. Vx = iVl . cos(n) and Vy = IVI . sin(n). In the proposed technique, the problem is divided into two separate problems: the calculation of the orientation angle n, which was treated in a previous work, and the calculation of the linear velocity IVI. This paper addresses the calculation of IVI given a trajectory vector field that encodes n, thus generating the speed reference for a Velocity Field Controller. The linear velocity I V I is generated through a Mamdani-type Fuzzy Inference System, that considers the curl of the trajectory vector field, and an extension of the Voronoi Diagram. Results obtained in both simulations and tests with a real robot show that an adequate intelligent velocity behavior, and a smooth obstacle avoidance are achieved.

A Software System for Planning Collision-Free Motion for Multi-Agent Systems Using Control Barrier Functions

“…While obstacle avoidance with known information could be seamlessly realized, those with scarce information about any obstacles face various limitations. Some of the obstacle avoidance algorithms are bug algorithms, potential field algorithms, vector field algorithms, genetic algorithms, fuzzy logic algorithms, and neural network algorithms [5], [6], [7], [8], [9], [10], [11]. The bug algorithm, the potential field algorithm, and the vector field algorithm are called traditional obstacle avoidance algorithm methods.…”

“…The vector field histogram does not apply to a dynamic environment [8]. The genetic algorithm is slow to search in terms of speed [9]. The core of the fuzzy logic method is the fuzzy controller.…”

Tracking Control for Quad-Rotor Using Velocity Field and Obstacle Avoidance Based on Hydrodynamics

“…In Reference [4] this technique is used to control multiple three-wheeled robots that cooperate to achieve a task. The use of probabilistic algorithms altogether with the velocity field is developed in Reference [5] where the fuzzy logic defines the velocity to be used in the tracking control. Another application is to generate a trajectory for an unmanned aerial vehicle (UAV) to collect information from a terrain after a natural disaster [6].…”

Design and implementation of a new speed planner for semiautonomous systems

“…In most cases, speed planners determine the speed reference according to the curvature of the road and the vehicle dynamics [13,14]. On the other hand, the conditions of the environmental factors, such as obstacle density, minimum distance values to obstacles, etc., are very important for autonomous and semiautonomous vehicles' speed reference determination.…”