Vision-based interception of a moving target with a nonholonomic mobile robot
Robotics and Autonomous Systems
Short and long-range visual navigation using warped panoramic images
Robotics and Autonomous Systems
Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
Hybrid intelligent vision-based car-like vehicle backing systems design
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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The evolutionary learning algorithm called particle swarm optimization (PSO) is developed in this paper. The image model of the embedded mobile robot is automatically generated with the omni-directional image concept to approach toward the behavior of the embedded mobile robot. The circumvolutory environment is dynamically captured from the head of the mobile robot, which will directly be transformed into the Cartesian coordinate system. The required parameters of fuzzy rules are automatically extracted with the guide of the flexible fitness function, which is efficiently approach toward the multiple objectives of avoiding obstacles, selecting favorable fuzzy rules to drive the desired targets at the same time. Three illustrated examples with various initial positions for the discussed environment map containing different blocks size and locations are illustrated the efficiency of the PSO leaning algorithm. Simulations demonstrate that the proposed mobile robot with the selected fuzzy rules can avoid the obstacles and achieve the targets as soon as possible.