Abstract
In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs’ application is how to improve the efficiency of updating the distance values when cell states are changed (i.e., changes caused by newly inserted or removed obstacles). To this end, this paper presents a novel algorithm to speed up the construction of DMs on planar, eight-connected grids. The novelty of our algorithm, Canonical Ordering Dynamic Brushfire (CODB), lies in two aspects: firstly, it only updates those cells which are affected by the changes; secondly, it employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update; therefore, the construction requires much fewer cell visits and less computation costs compared to previous algorithms. Furthermore, we propose algorithms to compute DM-based subgoal graphs. Such a spatial representation can be used to provide high-level, collision-free roadmaps for agents with certain safety radius to engage fast and rational path planning tasks. We present our algorithm both intuitively and through pseudocode, compare it to competing algorithms in simulated scenarios, and demonstrate its usefulness for real-time path planning tasks.
Highlights
In the context of collision check and path planning in robotics and game AI, the Distance Map (DM) has been widely used as a consistent model to encode the search space [1,2,3,4,5]
We demonstrate the usefulness of the DM-based subgoal methods on certain simulated scenarios
We demonstrate the usefulness of the DM-based graphs to real-time path planning tasks
Summary
In the context of collision check and path planning in robotics and game AI, the Distance Map (DM) has been widely used as a consistent model to encode the search space [1,2,3,4,5]. To make use of this localized feature, existing algorithms such as Dynamic Brushfire [6] and its subsequent variants [7,8] aim to speed up the reconstruction by launching a wavefront from the source of the state changes to incrementally repair the distance values, rather than reconstructing the whole DM from scratch. With such a localized mechanism, only those cells that are affected by the wavefront need to be handled; in most cases, the computation costs can be efficiently reduced.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have