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Real-Time Path Planning is a term used in robotics that consists of motion planning methods that can adapt to real time changes in the environment. This includes everything from primitive algorithms that stop a robot when it approaches an obstacle to more complex algorithms that continuously takes in information from the surroundings and creates a plan to avoid obstacles.
Exact motion planning for high-dimensional systems under complex constraints is computationally intractable. Potential-field algorithms are efficient, but fall prey to local minima (an exception is the harmonic potential fields). Sampling-based algorithms avoid the problem of local minima, and solve many problems quite quickly.
Path planning is realized with propagating wavefronts. The wavefront expansion algorithm is a specialized potential field path planner with breadth-first search to avoid local minima. [1] [2] It uses a growing circle around the robot. The nearest neighbors are analyzed first and then the radius of the circle is extended to distant regions. [3]
The probabilistic roadmap [1] planner is a motion planning algorithm in robotics, which solves the problem of determining a path between a starting configuration of the robot and a goal configuration while avoiding collisions. An example of a probabilistic random map algorithm exploring feasible paths around a number of polygonal obstacles
In a MAPD setting, agents have to complete a stream of tasks, where each task is composed by a pick-up a location and a delivery location. When planning for the completion of a task, the path has to start from the current position of the robot and to end in the delivery position of the task, passing through the pick-up point.
D* a family of incremental heuristic search algorithms for problems in which constraints vary over time or are not completely known when the agent first plans its path; Any-angle path planning algorithms, a family of algorithms for planning paths that are not restricted to move along the edges in the search graph, designed to be able to take on ...
The k shortest path routing is a good alternative for: Geographic path planning; Network routing, especially in optical mesh network where there are additional constraints that cannot be solved by using ordinary shortest path algorithms. Hypothesis generation in computational linguistics; Sequence alignment and metabolic pathway finding in ...
Any-angle path planning algorithms are pathfinding algorithms that search for a Euclidean shortest path between two points on a grid map while allowing the turns in the path to have any angle. The result is a path that cuts directly through open areas and has relatively few turns. [ 1 ]