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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Balancing safety and efficiency when planning in crowded scenarios withuncertain dynamics is challenging where it is imperative to accomplish therobot's mission without incurring any safety violations. Typically, chanceconstraints are incorporated into the planning problem to provide probabilisticsafety guarantees by imposing an upper bound on the collision probability ofthe planned trajectory. Yet, this results in overly conservative behavior onthe grounds that the gap between the obtained risk and the specified upperlimit is not explicitly restricted. To address this issue, we propose areal-time capable approach to quantify the risk associated with plannedtrajectories obtained from multiple probabilistic planners, running inparallel, with different upper bounds of the acceptable risk level. Based onthe evaluated risk, the least conservative plan is selected provided that itsassociated risk is below a specified threshold. In such a way, the proposedapproach provides probabilistic safety guarantees by attaining a closer boundto the specified risk, while being applicable to generic uncertainties ofmoving obstacles. We demonstrate the efficiency of our proposed approach, byimproving the performance of a state-of-the-art probabilistic planner, insimulations and experiments using a mobile robot in an environment shared withhumans.

Probabilistic Risk Assessment for Chance-Constrained Collision Avoidance in Uncertain Dynamic Environments