4 accepted papers
Sampling-based algorithms solve the path planning problem by generating random samples in the searchspace and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-spa…
Anytime sampling-based methods are an attractive technique for solving kino-dynamic motion planning problems. These algorithms scale well to higher dimensions and can efficiently handle state and control constraints. However, an intelligent exploration strategy is required to accelerate their conver
Asymptotically optimal sampling-based planners require an intelligent exploration strategy to accelerate convergence. After an initial solution is found, a necessary condition for improvement is to generate new samples in the so-called "Informed Set". However, Informed Sampling can be ineffective in…
Efficient exploration of the search space is crucial for faster convergence in sampling-based motion planning. An effective sampling method must first concentrate on quickly finding a good initial solution and then focus the search on regions that can potentially improve the current best solution. I…