rapidexploring

Rapidly-exploring Random Trees, often abbreviated as RRT, are a class of algorithms used for pathfinding and motion planning in continuous spaces, particularly in robotics. They are a probabilistic, sampling-based approach that explores the configuration space by growing a tree of reachable states. The core idea is to efficiently search a potentially large and complex space without needing an explicit model of the environment's geometry.

The algorithm starts with an initial state, which forms the root of the tree. It then iteratively samples a random point in the configuration space. A new node is added to the tree by connecting the closest existing node in the tree to the sampled point, typically by extending a short, straight path towards the sample. This extension is constrained by the robot's kinematic and dynamic capabilities, as well as any obstacles in the environment. If the path segment between the two nodes is collision-free, the new node is added to the tree.

RRTs are well-suited for high-dimensional spaces where traditional grid-based methods become computationally intractable. The probabilistic nature means that RRTs do not guarantee finding the optimal path, but they are generally effective at finding a feasible path quickly. Variations of the RRT algorithm exist, such as RRT*, which attempts to improve path optimality over time by rewiring the tree as more samples are considered. RRTs have found applications in areas like robot arm manipulation, autonomous vehicle navigation, and even in molecular dynamics simulations.