Planning
One of the most fundamental challenges in robotics is, "How can a robot get from where it is to where it needs to be?" In robotics, planning is the brain that enables intelligent navigation and determines the sequence of actions or path that takes a robot from point A to point B. A good path planning algorithm ensures that the generated plan is:
- Safe: Does not collide with obstacles or violate safety constraints
- Efficient: Minimizes time, distance, or energy consumption
- Feasible: Respects the robot's movement constraints (like turning radius, speed limits, or acceleration bounds)
- Adaptable: Can handle dynamic environments and unexpected changes
These are essential for enabling robots to navigate autonomously, whether they're exploring unknown terrain, delivering packages, or performing complex tasks in dynamic environments.
In this section, we will explore the preliminary knowledge behind planning and three powerful approaches:
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How A* (A-star) algorithm finds optimal paths by intelligently searching through possible routes, balancing the cost of the path so far with an estimate of the remaining distance to the goal.
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How Rapidly-exploring Random Trees (RRT) handles complex, high-dimensional planning problems by randomly exploring the space to quickly find feasible paths, especially useful when the environment is cluttered or the robot has many degrees of freedom.
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How Q-learning enables robots to learn optimal policies through trial and error, discovering the best actions to take in different situations without needing a pre-built map of the environment.
To begin, click on the Preliminaries module, or choose another module to explore!
