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

This paper aims to improve the path quality and computational efficiency ofkinodynamic planners used for vehicular systems. It proposes a learningframework for identifying promising controls during the expansion process ofsampling-based motion planners for systems with dynamics. Offline, the learningprocess is trained to return the highest-quality control that reaches a localgoal state (i.e., a waypoint) in the absence of obstacles from an inputdifference vector between its current state and a local goal state. The datageneration scheme provides bounds on the target dispersion and uses state spacepruning to ensure high-quality controls. By focusing on the system's dynamics,this process is data efficient and takes place once for a dynamical system, sothat it can be used for different environments with modular expansionfunctions. This work integrates the proposed learning process with a) anexploratory expansion function that generates waypoints with biased coverageover the reachable space, and b) proposes an exploitative expansion functionfor mobile robots, which generates waypoints using medial axis information.This paper evaluates the learning process and the corresponding planners for afirst and second-order differential drive systems. The results show that theproposed integration of learning and planning can produce better quality pathsthan kinodynamic planning with random controls in fewer iterations andcomputation time.

Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation