Motion Planning
What is Motion Planning in Humanoid Robotics?
The computational process of finding a path for a robot to move from one configuration to another.
Motion planning algorithms must avoid obstacles, maintain balance, and optimize for factors like speed, energy efficiency, or smoothness of movement.
How Motion Planning Works
Motion planning starts with a goal configuration (desired position and orientation) and current state. The planning algorithm searches through possible movement sequences to find a collision-free path. Sampling-based planners like RRT (Rapidly-exploring Random Tree) randomly sample configurations and build a tree of valid paths. Graph-based planners discretize the space and use algorithms like A* to find optimal paths. The planner checks each candidate configuration against environment obstacles using collision detection. For humanoid robots, planning must also consider balance constraints and joint limits. Once a path is found, trajectory optimization smooths it for efficient execution. Real-time replanning handles dynamic obstacles and unexpected situations.
Types of Motion Planning
- Sampling-based: RRT, PRM randomly sample configuration space, good for high-dimensional problems
- Graph-based: A*, Dijkstra discretize space into grid, optimal but computationally expensive
- Potential Fields: Treat goal as attractor and obstacles as repellers, fast but can get stuck in local minima
- Trajectory Optimization: Refine paths for smoothness, energy efficiency, or speed
- Whole-body Planning: Plan coordinated movement of entire robot including arms, legs, and torso
- Reactive Planning: Fast local adjustments without global replanning
Applications in Humanoid Robots
Motion planning enables humanoid robots to navigate from current position to goals while avoiding furniture, walls, and people. In manipulation, planning generates arm motions to reach objects without colliding with environment or self. Whole-body planning coordinates legs, torso, and arms for tasks like reaching objects while walking. Dynamic environments require continuous replanning as people and objects move. Multi-robot scenarios need planning that avoids other robots. Energy-efficient planning extends battery life by optimizing movement paths.
