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 explores minimum sensing navigation of robots in environmentscluttered with obstacles. The general objective is to find a path plan to agoal region that requires minimal sensing effort. In [1], theinformation-geometric RRT* (IG-RRT*) algorithm was proposed to efficiently findsuch a path. However, like any stochastic sampling-based planner, thecomputational complexity of IG-RRT* grows quickly, impeding its use with alarge number of nodes. To remedy this limitation, we suggest running IG-RRT*with a moderate number of nodes, and then using a smoothing algorithm to adjustthe path obtained. To develop a smoothing algorithm, we explicitly formulatethe minimum sensing path planning problem as an optimization problem. For thisformulation, we introduce a new safety constraint to impose a bound on theprobability of collision with obstacles in continuous-time, in contrast to thecommon discrete-time approach. The problem is amenable to solution via theconvex-concave procedure (CCP). We develop a CCP algorithm for the formulatedoptimization and use this algorithm for path smoothing. We demonstrate theefficacy of the proposed approach through numerical simulations.

A Smoothing Algorithm for Minimum Sensing Path Plans in Gaussian Belief Space