A Layered Goal-Oriented Fuzzy Motion Planning Strategy for Mobile Robot Navigation

Abstract

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.

An adaptive genetic algorithm for robot motion planning in 2D complex environments

“…The B-spline technique is one of the efficient curve interpolating methods (i.e., curve fitting methods) which has many applications in signal processing, computer graphics and robotics. Other methods which are based on cell decomposition [1], Voronoi diagram [1], dynamic programming [10], rapidly exploring random trees (RRTs) [1], randomized and approximation algorithms [11], cellular automata [12], neural networks [13], fuzzy logic [14], fast marching algorithm [15], metaheuristics [16], etc. have been proposed each of which has its own advantages and disadvantages [17,18].…”

A Reactive Anticipation for Autonomous Robot Navigation

“…Then, it repeats the process until it reaches the goal. It can coordinate multiple goals and constraints within a complex environment (Huq et al, 2008;Yang et al, 2006). However, in deliberative navigation, accurate model of environment is needed to plan a globally feasible path.…”

Fuzzy Logic Based Navigation of Mobile Robots

“…Rosenblatt et al (Rosenblatt et al 1989, 1994, 1995, presented DAMN architecture in which a centralized arbitration of votes provided by independent behaviors combines into a "voted" output. Others (Saffiotti, 1997), (Seraji et al, 2001(Seraji et al, , 2002, (Yang et al 2004(Yang et al , 2005Selekwa et al, 2005 andAguirre &Gonzales, 2006) used fuzzy logic system to represent and coordinate behaviors.…”