Fast-BIT: Modified heuristic for sampling-based optimal planning with a faster first solution and convergence in implicit random geometric graphs

Abstract

This paper presents Fast Batch Informed Trees (Fast-BIT), a modification to the asymptotically optimal path planner Batch Informed Trees (BIT). Fast-BIT modifies the underlying heuristic that dictates the expansion and processing of vertex and edge queues. BIT uses heuristics to guide the search of implicit Random Geometric Graphs (RGGs) to generate an explicit solutions, while minimizing highly computational tasks such as collision checking. Fast-BIT builds on BIT by biasing the search heuristic towards the goal, in a solution analogous to depth-first search, finding an initial solution of the implicit RGG at a faster rate, at the cost of decreasing initial optimality. Fast-BIT requires additional procedures to reset expansion variables of affected areas in the graph, ensuring the bias is not lasting in the graph expansion. An earlier initial solution leads to a faster initial upper bound for use in informed graph pruning, allowing convergence of path cost to begin earlier in the planning procedure. We show that Fast-BIT finds a first solution faster than BIT as well as the commonly used RRT-Connect and similar methods, along with a faster overall convergence rate. This paper shows various random-world test examples, showing the benefits of similar algorithms, along with a robot path planning simulation.