Simulation and Path Planning Techniques for Static Obstacle Avoidance in Autonomous Robots
This book provides a comprehensive introduction to the computational principles, mathematical models, and simulation techniques used for robot navigation in environments containing static obstacles. Beginning with the fundamentals of autonomous robot motion, coordinate systems, geometric modeling, and robot kinematics, the book establishes the theoretical foundation required for understanding modern path planning algorithms.
Readers explore both two-dimensional and three-dimensional obstacle avoidance strategies, including analytical methods, computational geometry, trajectory generation, minimum-distance calculations, collision detection, and path optimization techniques. The text explains how robots identify collision-free paths while maintaining efficiency, stability, and navigation accuracy under different environmental constraints.
A major emphasis is placed on simulation-based validation using modern robotics simulation environments. The book demonstrates how autonomous robotic systems can be modeled, tested, and analyzed before deployment in real-world applications. Topics include simulation environment configuration, virtual robot modeling, normalization of simulation results, algorithm verification, performance comparison, and engineering evaluation techniques that support reliable autonomous navigation.
The book further examines graph-based search methods, optimization algorithms, workspace analysis, trajectory smoothing, obstacle representation, and computational performance metrics including path length, execution time, obstacle clearance, computational efficiency, navigation accuracy, and algorithm robustness. Practical engineering examples illustrate how these techniques are applied in warehouse automation, mobile robotics, unmanned ground vehicles, inspection robots, service robots, manufacturing automation, and intelligent transportation systems.
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