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Recently, rapidly-exploring random trees(RRT) is widely used in path planning for its nature of single-query. The optimized algorithm RRT∗ extends RRT algorithm to find the optimal path, but it needs to search every state from the initial state to the global scope asymptotically. This method is not only inefficient, but also contrary to the single-query of RRT. In this paper, a new variant of RRT∗-Gb...
Recent progress in randomized motion planning has led to the development of a new class of sampling-based algorithms that provide asymptotic optimality guarantees, almost surely, notably the RRT* algorithm among others. The recently proposed RRT# algorithm utilizes dynamic programming ideas (namely, asynchronous value iteration) and implements them incrementally on randomly generated graphs to improve...
This paper considers the planning of collision-free and energy-optimal trajectories for linear systems with decoupled dynamics for different degrees of freedom. A direct transcription of such a problem generally results in a non-convex problem due to the collision avoidance constraint. In this paper we propose a novel Alternating Quadratic Programming (AQP) algorithm to deal with the non-convex collision...
The RRT* algorithm has recently been proposed as an optimal extension to the standard RRT algorithm [1]. However, like RRT, RRT* is difficult to apply in problems with complicated or underactuated dynamics because it requires the design of a two domain-specific extension heuristics: a distance metric and node extension method. We propose automatically deriving these two heuristics for RRT* by locally...
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