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Go to Editorial ManagerIn this paper, a new algorithm for mobile robot navigation and polygonal obstacles avoidance in dynamic target environment is introduced. In the dynamic target path planning the agent (robot) trying to reach a moving target in minimum path cost. The introduced algorithm which called Prediction-based path planning with obstacle avoidance in dynamic target environ- ment planning a path to a moving target by predicting the next target location, then computing a path from the robot current lo- cation to the predicted target location representing each visible obstacle by the smallest circle that enclosing the polygon obstacle, then determine the visible tangents between the robot and the cir- cular obstacle that intersect its shortest path and compute the shortest path. Three target movement scenarios were suggested and tested in different environment conditions. The results show that the target was reached in all scenarios and under all environ- ment conditions with good path cost.
Obstacle avoidance and path planning are from the most important problems in mobile robots, especially in unknown environment . In this paper, we proposed an approach for mobile robot navigation combining path planning and obstacle avoidance. Methods such as obstacle avoidance are inspired from the nature, and have been developed by fuzzy logic to train an intelligent robot in unknown environment. The model of the robot has two driving wheels and the linear velocity and azimuth of the two wheels are independently controlled using PID controller. Inputs are obtained from ultrasonic sensors mounted on it.
Formation control is a critical task in the coordination of multi-mobile robot systems operating in structured environments with limited local knowledge and low-cost hardware. Achieving reliable formations requires effective localization, path planning, and obstacle avoidance capabilities. This study presents a static strategy for forming polygon-shaped configurations using multiple mobile robots. The proposed strategy improves formation efficiency by employing a cluster matching algorithm instead of the conventional triangulation approach to complete the formation process. In addition, the visibility binary tree algorithm and the reciprocal orientation algorithm are integrated to enhance robot coordination and spatial awareness. Simulation results demonstrate that the proposed strategy achieves superior performance in multi-robot formation tasks, offering improved efficiency and robustness compared with traditional triangulation-based methods.