4 papers
Non-Trivial Query Sampling For Efficient Learning To Plan
Sagar Suhas Joshi, Panagiotis Tsiotras
In recent years, learning-based approaches have revolutionized motion planning. The data generation process for these methods involves caching a large number of high quality paths…
LES: Locally Exploitative Sampling for Robot Path Planning
Sagar Suhas Joshi, Seth Hutchinson, Panagiotis Tsiotras
Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree. Conventionally,…
TIE: Time-Informed Exploration For Robot Motion Planning
Sagar Suhas Joshi, Seth Hutchinson, Panagiotis Tsiotras
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 h…
Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning
Sagar Suhas Joshi, Panagiotis Tsiotras
Asymptotically optimal sampling-based planners require an intelligent exploration strategy to accelerate convergence. After an initial solution is found, a necessary condition for…