Motion planning algorithms for autonomous robots in static and dynamic environments

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This thesis analyzes the ways a robot could tackle the problem of motion planning, among which is understood the movement from point A to B while avoiding obstacles. The author compares two approaches to the motion problem; classical approach and obstacle-avoidance approach. Each of the approaches has a range of different techniques. Voronoi, Visibility graph, Cell decomposition and Potential field for the classical approaches. Neural network, Bug Algorithms, Dynamic windowing, Vector field histogram, Bubble Band and Curvature velocity technique for the obstacle-avoidance approaches.

This thesis analyzes the ways a robot could tackle the problem of motion planning, among which is understood the movement from point A to B while avoiding obstacles. The author compares two approaches to the motion problem; classical approach and obstacle-avoidance approach. Each of the approaches has a range of different techniques. Voronoi, Visibility graph, Cell decomposition and Potential field for the classical approaches. Neural network, Bug Algorithms, Dynamic windowing, Vector field histogram, Bubble Band and Curvature velocity technique for the obstacle-avoidance approaches.

To test the applicability of the theoretical techniques and approaches, simulations and experiments have been conducted and the results have been presented. The approaches which have been tested resulted in a graph, which is necessary for path-finding. With the help of the graph search algorithm the optimal path could be found. The classical approaches suffered from problems as trapped in local minima, high time complexity in high dimensions and object concavity. However solutions to these problems were also presented for example local object avoidance algorithms. Through the simulations the author showed that the approaches used are effective and can be used to solve motion planning for real robots. The experiments proved that the Dynamic window approach, which was a technique for the obstacle-avoidance approach, can be used to avoid obstacles in real time.

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