This work presents results of our work in development of a genetic algorithm based path-
planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a …

The paper develops a hybrid intelligent approach to path planning for high mobility robots
operating in rough environments. Path planning consists of characterization of the …

This study presents an evolutionary computation system that can generate grid robot path
planning problems. An evolvable cellular representation that specifies how to build a PPP is …

This work describes an autonomous navigation system based on a modular neural network.
The environment is unknown and initially the system does not have ability to balance two …

Research in collective robotics is motivated mainly by the possibility of achieving an efficient
solution to multi-objective navigation tasks when multiple robots are employed, instead of a …

Classical reinforcement learning mechanisms and a modular neural network are unified for
conceiving an intelligent autonomous system for mobile robot navigation. The conception …

An autonomous system able to construct its own navigation strategy for mobile robots is
proposed. The navigation strategy is molded from navigation experiences (succeeding as …

This paper presents an autonomous evolutionary system applied to control a mobile robot in
unknown environments. The navigation system learns efficiently to deal with situations …

The paper develops a genetic algorithm approach to path planning for a mobile robot
operating in rough environments. Path planning consists of a description of the environment …

The goal for real-time mobile robots is to travel the shortest path in minimal time while
avoiding obstacles in a navigation environment. Autonomous navigation allows robots to …