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

An autonomous navigation with proven collision avoidance in unknown anddynamic environments is still a challenge, particularly when there are movingobstacles. A popular approach to collision avoidance in the face of movingobstacles is based on model predictive algorithms, which, however, may becomputationally expensive. Hence, we adopt a reactive potential field approachhere. At every cycle, the proposed approach requires only current robot statesrelative to the closest obstacle point to find the potential field in thecurrent position; thus, it is more computationally efficient and more suitableto scale up for multiple agent scenarios. Our main contribution here is towrite the reactive potential field based motion controller as a hybridautomaton, and then formally verify its safety using differential dynamiclogic. In particular, we can guarantee a passive safety property, which meansthat collisions cannot occur if the robot is to blame, namely a collision canoccur only if the robot is at rest. The proposed controller and verificationresults are demonstrated via simulations and implementation on a Pioneer P3-ATrobot.

SafeGuardPF: Safety Guaranteed Reactive Potential Fields for Mobile Robots in Unknown and Dynamic Environments