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

Provably safe and scalable multi-vehicle path planning is an important andurgent problem due to the expected increase of automation in civilian airspacein the near future. Hamilton-Jacobi (HJ) reachability is an ideal tool foranalyzing such safety-critical systems and has been successfully applied toseveral small-scale problems. However, a direct application of HJ reachabilityto large scale systems is often intractable because of itsexponentially-scaling computation complexity with respect to system dimension,also known as the "curse of dimensionality". To overcome this problem, thesequential path planning (SPP) method, which assigns strict priorities tovehicles, was previously proposed; SPP allows multi-vehicle path planning to bedone with a linearly-scaling computation complexity. In this work, wedemonstrate the potential of SPP algorithm for large-scale systems. Inparticular, we simulate large-scale multi-vehicle systems in two differenturban environments, a city environment and a multi-city environment, and usethe SPP algorithm for trajectory planning. SPP is able to efficiently designcollision-free trajectories in both environments despite the presence ofdisturbances in vehicles' dynamics. To ensure a safe transition of vehicles totheir destinations, our method automatically allocates space-time reservationsto vehicles while accounting for the magnitude of disturbances such as wind ina provably safe way. Our simulation results show an intuitive multi-lanestructure in airspace, where the number of lanes and the distance between thelanes depend on the size of disturbances and other problem parameters.

Provably Safe and Robust Drone Routing via Sequential Path Planning: A Case Study in San Francisco and the Bay Area