Decentralised indoor smart camera mapping and hierarchical navigation for autonomous ground vehicles. Issue 7 (21st October 2020)

• Record Type: Journal Article
• Authors: Whitaker, Taylor J.L.; Cunningham, Samantha‐Jo; Bobda, Christophe
• Is Part Of: IET computer vision. Volume 14:Issue 7(2020)
• Page Start: 462
• Page End: 470
• Publication Date: 2020-10-21
• Subjects: mobile robots -- optimisation -- path planning -- greedy algorithms -- graph theory -- cameras -- navigation; Decentralised indoor smart camera mapping -- hierarchical navigation -- autonomous ground vehicles -- novel decentralised coordination scheme -- map building -- path planning -- smart overhead cameras -- waypoint graphs -- growing neural gas algorithm -- dynamic waypoint graph -- authors -- decentralised fashion -- hierarchical fashion -- waypoint generation -- generated environment -- randomised hierarchical graphs -- Decentralised‐A* -- smart camera network; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2019.0949
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : In this work, the authors propose a novel decentralised coordination scheme for autonomous ground vehicles to enable map building and path planning with a network of smart overhead cameras. Decentralised indoor smart camera mapping and hierarchical navigation supports the automatic generation of waypoint graphs for each camera in an environment and allows path planning through the environment across multiple camera fields of view, or subviews. The proposed solution utilises the growing neural gas algorithm to learn the topology of unoccupied working space in each subview for maintaining a dynamic waypoint graph on each camera. The authors' pathing solution leverages a modified version of the A* algorithm to compute paths in a decentralised and hierarchical fashion. Waypoint generation was simulated and analysed on a generated environment to ensure it is both effective and efficient, while path planning was simulated on various randomised hierarchical graphs to effectively compare the proposed Decentralised‐A* (D‐A*) algorithm against standard greedy search. The proposed method efficiently handles the cases where other robot navigation methods are otherwise weak and ineffective, while still providing avenues for further optimisation of resource overhead for both the smart camera network as well as the robots themselves.