Recent progress on sampling based dynamic motion planning algorithms

Abstract

This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored. Review of Sampling Based Dynamic Motion Planning AlgorithmsAndrew Short, Zengxi Pan, Nathan Larkin and Stephen van DuinAbstract-This paper reviews recent developments extending sampling based motion planning algorithms to operate in dynamic environments. Sampling based planners provide an effective approach for solving high degree of freedom robot motion planning problems. The two most common algorithms are the Probabilistic Roadmap Method and Rapidly Exploring Random Trees. These standard techniques are well established, however they assume a fully known environment and generate paths ahead of time. For realistic applications a robot may be required to update its path in real-time as information is gained or obstacles change position. Variants of these standard algorithms designed for dynamic environments are categorically presented and common implementation strategies are explored.

Comparison of A* and RRT in real–time 3D path planning of UAVs

“…42,43 Advancements in sampling-based methods have proposed these algorithms for real-time path planning in dynamic and unknown environments. 29 Re-planning is essential in these situations as the environment is only partially known at one point in time, revealing more detail in the direction of the goal in every vehicle movement. This can also create situations, in which a previous non-colliding path may lead to a collision with more details in the environment.…”

“…al. 29 a realistic path planner must react in synchronisation with information update from the sensory systems. Furthermore, such path planner must generate a path even with restricted global information.…”

3D real-time path planning of UAVs in dynamic environments in the presence of uncertainty

“…Sampling-based algorithms construct a path between start and goal positions by connecting unevenly selected obstacle-free points in the configuration space. 70,73 Opposite to graph-based methods, these algorithms offer a guarantee of solution within an infinite time as opposed to graph-based methods provided that a path exists. 70 The standard Rapidly-Exploring Random Tree (RRT) constructs a unidirectional tree by randomly planting seeds in obstacle-free points.…”

Advancements for A* and RRT in 3D path planning of UAVs

“…14,15 Sampling-based methods unevenly selects a set of points from the configuration space, creating a path by connecting these points. 15,16 Oppositely to graph-based methods which offer no guarantee of solution, 17 sampling-based methods are probabilistically-complete. 15…”