An adaptive, run-time navigation system for haul trucks in surface mines. Issue 5 (4th July 2017)

• Record Type: Journal Article
• Authors: Kavuri, Ajay; Prakash, Bisleshana; Sabniveesu, Venkataraghavasivanagashashank; Nimbarte, Ashish; Kulathumani, Vinod; Kecojevic, Vladislav
• Is Part Of: International journal of mining, reclamation and environment. Volume 31:Issue 5(2017)
• Page Start: 364
• Page End: 374
• Publication Date: 2017-07-04
• Subjects: Navigation -- proximity -- GPS -- Ad hoc network -- earth-view maps; Mining engineering -- Periodicals; Mineral industries -- Environmental aspects -- Periodicals; Abandoned mined lands reclamation -- Periodicals; 622.292
• DOI: 10.1080/17480930.2016.1148566
• Languages: English
• ISSNs: 1748-0930

Abstract

Abstract: This paper presents a navigation system for haul trucks in surface mining. The main features of this system include the following: (1) realistic earth-view maps; (2) run-time customisation of the maps; and a (3) unified graphical user interface (GUI). The system implementation comprised of three parts: map pre-processing, sensor communication system and unified GUI development. Map pre-processing involved development of a new method for incorporating accurate earth-view maps in the navigation system for a frequently updating environment like surface mines. An IEEE 802.11b wireless network in ad hoc mode was used to achieve communication between sensors (global position system (GPS), proximity) and vehicles. A unified GUI was developed to display the data acquired from different sensors on the customised maps. The GUI also features a dynamic layering tool for run-time customisation of maps. This tool generates custom warnings based on the changed environmental, geographical conditions or hazards. The prototype of the system was deployed in a surface coal mine in the southern United States. Result of the testing showed that the GUI was able to accurately display the GPS data on the earth-view map developed using the map pre-processing method. The sensor system used for the zone-based proximity warning was able to identify correct zone 90% of the time and about 10% times, the zones were classified as the directly neighbouring zone. Thus, the initial testing proved that the system was feasible and is expected to improve the users' awareness of their surroundings and overall safety.