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

Robotic shepherding is a bio-inspired approach to autonomously guiding aswarm of agents towards a desired location. The research area has earnedincreasing research interest recently due to the efficacy of controlling alarge number of agents in a swarm (sheep) using a smaller number of actuators(sheepdogs). However, shepherding a highly dispersed swarm in anobstacle-cluttered environment remains challenging for existing methods. Toimprove the efficacy of shepherding in complex environments with obstacles anddispersed sheep, this paper proposes a planning-assisted context-sensitiveautonomous shepherding framework with collision avoidance abilities. Theproposed approach models the swarm shepherding problem as a single TravellingSalesperson Problem (TSP), with two sheepdogs\textquoteright\ modes:no-interaction and interaction. An adaptive switching approach is integratedinto the framework to guide real-time path planning for avoiding collisionswith static and dynamic obstacles; the latter representing moving sheep swarms.We then propose an overarching hierarchical mission planning system, which ismade of three sub-systems: a clustering approach to group and distinguish sheepsub-swarms, an Ant Colony Optimisation algorithm as a TSP solver fordetermining the optimal herding sequence of the sub-swarms, and an online pathplanner for calculating optimal paths for both sheepdogs and sheep. Theexperiments on various environments, both with and without obstacles,objectively demonstrate the effectiveness of the proposed shepherding frameworkand planning approaches.

Planning-Assisted Context-Sensitive Autonomous Shepherding of Dispersed Robotic Swarms in Obstacle-Cluttered Environments