AStar Algorithm in Path Planning: Implementation and Applications
This article explains the AStar algorithm's application in path planning, including implementation details with code examples to demonstrate practical usage.
Genetic Algorithm-based Route Planning and Path Planning Algorithms
Complete source code for genetic algorithm implementations in route planning and path planning applications, featuring comprehensive algorithm explanations and key function descriptions for collaborative learning
Trajectory Planning
Trajectory planning for aircraft is a key technology in achieving autonomous navigation, representing an important research direction in artificial intelligence and guidance/navigation fields. This provides a systematic and in-depth introduction to the concepts, theories, and methods of unmanned aerial vehicle trajectory planning, with code implementation insights.
Path Planning Algorithms: Traditional and Modern Approaches
Traditional deterministic methods for path planning include intelligent search algorithms (A* and D*), steepest descent method, visibility graph approach, artificial potential field method, cell decomposition, optimal control methods, simulated annealing, and genetic algorithms. These methods face challenges such as combinatorial explosions in high-dimensional spaces, local optima, high computational complexity, sensitivity to noise, and convergence issues. Modern approaches like deep learning, swarm intelligence, and hybrid methods offer solutions to overcome these limitations by capturing complex data structures, simulating collective behaviors, and leveraging combined algorithmic strengths.
Particle Swarm Optimization for Path Planning
Particle Swarm Optimization (PSO) originated from Complex Adaptive System (CAS) theory, which was formally proposed in 1994. In CAS, members are called agents - for example, in studying bird flock systems, each bird represents an agent. Agents possess adaptability, enabling them to interact with their environment and other agents, while "learning" or "accumulating experience" through these interactions to modify their structure and behavior. The evolution of the entire system includes the emergence of new levels (birth of birds), differentiation and diversity (birds splitting into smaller subgroups), and new themes (discovery of new food sources during foraging). PSO mimics this behavior through mathematical models where particles (agents) iteratively update their positions based on personal and group best solutions.
MATLAB Code Implementation for Creating 3D Maps
MATLAB Code Implementation for Creating 3D Maps with Technical Descriptions
Authentic Flight Trajectory Simulation
Modeling Realistic Aircraft Flight Paths with Kinematic Constraints