Path Planning Algorithms: Traditional and Modern Approaches

In the realm of deterministic methods, traditional approaches encompass intelligent search algorithms (e.g., A* and D*), steepest descent method, visibility graph technique, artificial potential field method, cell decomposition, optimal control methods, simulated annealing, and genetic algorithms. However, these methods exhibit certain limitations. For instance, intelligent search algorithms are prone to combinatorial explosions and local optima in high-dimensional spaces. The steepest descent method requires extensive iterative computations without optimality guarantees. The visibility graph approach suffers from high computational complexity, sensitivity to environmental noise, and difficulties in handling high-dimensional problems. The artificial potential field method struggles with pathfinding near closely spaced obstacles, exhibits oscillations in narrow passages, and faces local optima issues. Optimal control methods involve complex model parameter tuning, are prone to divergence, and require continuous second-order partial derivatives of the terrain. Cell decomposition, simulated annealing, and genetic algorithms also entail high computational complexity.

Consequently, recent years have seen the emergence of novel deterministic methods such as deep learning techniques, swarm intelligence algorithms, and hybrid approaches that overcome some limitations of traditional methods. Deep learning methods can capture complex data structures in high-dimensional spaces through neural network architectures like convolutional or recurrent layers. Swarm intelligence algorithms simulate collective biological behaviors (e.g., particle swarm optimization or ant colony optimization) to effectively avoid combinatorial explosions. Hybrid methods integrate multiple superior algorithms, such as combining A* with genetic algorithms for initial path generation and refinement, leveraging their respective advantages. These new approaches significantly expand the research domain of deterministic methods, providing enhanced possibilities for solving practical problems.