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

Consider a robot operating in an uncertain environment with stochastic,dynamic obstacles. Despite the clear benefits for trajectory optimization, itis often hard to keep track of each obstacle at every time step due to sensingand hardware limitations. We introduce the Safely motion planner, areceding-horizon control framework, that simultaneously synthesizes both atrajectory for the robot to follow as well as a sensor selection strategy thatprescribes trajectory-relevant obstacles to measure at each time step whilerespecting the sensing constraints of the robot. We perform the motion planningusing sequential quadratic programming, and prescribe obstacles to sense basedon the duality information associated with the convex subproblems. We guaranteesafety by ensuring that the probability of the robot colliding with any of theobstacles is below a prescribed threshold at every time step of the plannedrobot trajectory. We demonstrate the efficacy of the Safely motion plannerthrough software and hardware experiments.

Safely: Safe Stochastic Motion Planning Under Constrained Sensing via Duality