Chapter 5: Motion Planning and Path Optimization
27 sections
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Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Deterministic algorithms like A and D form the foundation of path planning.
- Sampling-based methods like RRT and PRM are scalable to high-dimensional spaces.
- Trajectory optimization enhances path smoothness and dynamic feasibility.
- Dynamic obstacle avoidance integrates perception with reactive control strategies.
- Real-time planning in unknown terrain demands adaptability and robustness.
Key Concepts
- -- A* Algorithm
- A best-first search algorithm that uses the cost-to-come and cost-to-go to find optimal paths in graph-based environments.
- -- RapidlyExploring Random Tree (RRT)
- An algorithm designed for pathfinding in high-dimensional spaces by incrementally building a tree rooted at the start configuration.
- -- Dynamic Window Approach (DWA)
- A method that samples velocities to choose paths that avoid obstacles and progress toward the goal.
- -- Trajectory Optimization
- The process of generating trajectories that minimize costs related to smoothness and collisions, respecting physical constraints.
- -- FrontierBased Exploration
- A technique that directs robots towards the boundaries between known and unknown areas to enhance mapping and exploration.
Additional Learning Materials
Supplementary resources to enhance your learning experience.