Optimised Informed RRTs for Mobile Robot Path Planning

Path planners based on basic rapidly-exploring random trees (RRTs) are quick and efficient, thus favourable for real-time robot path planning, but almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to solution, may be expensive in practice. Recent work has focused accelerating RRT*'s convergence rate. The most successful strategies informed sampling, optimisation, a combination thereof. However, sampling its with optimisation have not been applied RRT. Moreover, while number of optimisers can used accelerate rate, comparison their effectiveness is lacking. This paper investigates use both RRT*, resulting family algorithms known as optimised RRTs. We apply different compare effectiveness. goal ascertain if applying help quick, though suboptimal, attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based RRTs do counterparts, when planning time limited there more time.