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A heuristic-based algorithm that finds the shortest path from a start node to an end node by maintaining a tree of paths and choosing the path that minimizes the combined cost and heuristic.

A probabilistic algorithm that incrementally builds a space-filling tree to search high-dimensional spaces efficiently by randomly exploring the space.

An algorithm that finds the shortest paths from a single source node to all other nodes in a graph by progressively expanding outward from the source.

A path planning approach that uses virtual forces, treating the goal as an attractor and obstacles as repulsors, to navigate around obstacles.

A motion planning algorithm particularly useful for robots with many degrees of freedom. It randomly samples the configuration space and connects samples to form a graph (roadmap) used for path finding.

A partitioning of a plane into regions based on distance to a specific set of points, used in path planning to maximize clearance from obstacles.

A simple robotic navigation algorithm that involves the robot 'hugging' the contour of the obstacles in its environment to reach its goal.

A graph of inter-visible locations, used in path planning where the shortest path is computed as a series of straight-line segments connecting waypoints.

A path planning strategy that breaks down the environment into manageable cells and solves the path by traversing through adjacent, non-obstacle cells.

A search algorithm that expands the node that appears to be closest to the goal, as determined by a heuristic, often trading off completeness and optimality for speed.

A real-time reactive path planning method that selects velocity samples based on the robot's current state and dynamic limitations.

A pathfinding algorithm that is like A*, but capable of dynamic re-planning in response to changes in the environment, suitable for robots in dynamic scenarios.

A method that treats the robot as a point in a potential field where the goal emits an attracting force and the obstacles emit repelling forces.

A form of path planning that uses fuzzy logic to navigate under uncertainty by applying rules that deal with approximate rather than fixed values.

An algorithm that propagates a wavefront from the goal to the start, assigning each cell a value representing its distance from the goal and then tracing a path back from the start to the goal following decreasing values.

Path planning algorithms that incrementally build a roadmap by sampling a robot's configuration space and connecting these samples with feasible paths until reaching the goal.

A path planning algorithm that is a variant of A*, which allows any-angle paths by looking ahead to determine the visibility between non-adjacent nodes.

A technique that uses different layers of costmaps (e.g. static, inflation, dynamic) to represent different levels of obstacle information for robust robot navigation.

A probabilistic approach for mobile robot localization using random sampling, often incorporated into path planning to update the robot's belief about its position.

An optimization technique that simulates biological evolution, which can be applied to path planning by evolving a population of path solutions over time.

A motion planning approach that discretizes the state space into a lattice and then searches for the optimal sequence of moves on this lattice.

A path planning strategy that uses a set of behaviors, like obstacle avoidance or wall-following, which are blended together to achieve a goal.

A pathfinding algorithm that runs two simultaneous searches, one forward from the start and the other backward from the goal, until they meet to form a complete path.

A probabilistic technique for approximating the global optimum of a given function, which can be used in path planning to escape local minima and find better paths.