Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Fuzzy logic-based real-time robot navigation in unknown environment with dead ends
Robotics and Autonomous Systems
The virtual wall approach to limit cycle avoidance for unmanned ground vehicles
Robotics and Autonomous Systems
DYNAMIC ENVIRONMENT ROBOT PATH PLANNING USING HIERARCHICAL EVOLUTIONARY ALGORITHMS
Cybernetics and Systems
Switching control approach for stable navigation of mobile robots in unknown environments
Robotics and Computer-Integrated Manufacturing
Modified Newton's method applied to potential field-based navigation for mobile robots
IEEE Transactions on Robotics
A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fuzzy-Logic-Based Approach for Mobile Robot Path Tracking
IEEE Transactions on Fuzzy Systems
Evolution of a Negative-Rule Fuzzy Obstacle Avoidance Controller for an Autonomous Vehicle
IEEE Transactions on Fuzzy Systems
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This paper introduces a new behavior-based collision avoidance approach for mobile robot navigation in unknown and dynamic environments, which called Nearest Virtual-Target NVT. The NVT approach was developed based on a modeling-planning-reaction configuration. In modeling module, sensory information is integrated to construct a local model of environment which represents obstacles distribution and free obstacle areas in a part of robot's work space. The planning module uses the “actual-virtual target switching strategy” to compute obstacle free paths towards the target. The robot motion generation is handled by the reaction module. The reaction module applies a fuzzy controller to control the robot's rotational and translational velocities. The contribution of this approach is solving navigation difficulties presented in previous approaches for successful motion of the robot towards the target in troublesome scenarios such as narrow passages, very dense, cluttered and dynamic environments. Feasibility and effectiveness of the proposed approach are verified through simulation and real robot experiments. Eventually, advantages and limitations of this approach are discussed.