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/

Zendy Plus

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

Zendy Tools

Zendy Open

An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree ofautonomy for any particular underwater mission to fulfill the missionobjectives successfully and ensure its safety in all stages of the mission in alarge scale operating filed. In this paper, a novel combinatorialconflict-free-task assignment strategy consisting an interactive engagement ofa local path planner and an adaptive global route planner, is introduced. Themethod is established upon the heuristic search potency of the Particle SwarmOptimisation (PSO) algorithm to address the discrete nature of routing-taskassignment approach and the complexity of NP-hard path planning problem. Theproposed hybrid method is highly efficient for having a reactive guidanceframework that guarantees successful completion of missions specifically incluttered environments. To examine the performance of the method in a contextof mission productivity, mission time management and vehicle safety, a seriesof simulation studies are undertaken. The results of simulations declare thatthe proposed method is reliable and robust, particularly in dealing withuncertainties, and it can significantly enhance the level of vehicle's autonomyby relying on its reactive nature and capability of providing fast feasiblesolutions.

Toward Efficient Task Assignment and Motion Planning for Large Scale Underwater Mission