English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fast, autonomous flight in unstructured, cluttered environments such asforests is challenging because it requires the robot to compute new plans inrealtime on a computationally-constrained platform. In this paper, we enablethis capability with a search-based planning framework that adapts samplingdensity in realtime to find dynamically-feasible plans while remainingcomputationally tractable. A paramount challenge in search-based planning isthat dense obstacles both necessitate large graphs (to guarantee completeness)and reduce the efficiency of graph search (as heuristics become less accurate).To address this, we develop a planning framework with two parts: one thatmaximizes planner completeness for a given graph size, and a second thatdynamically maximizes graph size subject to computational constraints. Thisframework is enabled by motion planning graphs that are defined by a singleparameter, dispersion, which quantifies the maximum trajectory cost to reach anarbitrary state from the graph. We show through real and simulated experimentshow the dispersion can be adapted to different environments in realtime,allowing operation in environments with varying density. The simulatedexperiment demonstrates improved performance over a baseline search-basedplanning algorithm. We also demonstrate flight speeds of up to 2.5m/s inreal-world cluttered pine forests.

Experiments in Adaptive Replanning for Fast Autonomous Flight in Forests