Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590408
Title: Towards autonomous surveillance and tracking by multiple UAVs
Author: Oh, Hyondong
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2013
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
This research investigates the use of small and low-cost UAVs (Unmanned Aerial Vehicles) for autonomous aerial surveillance, which aims to identify and continuously track suspicious vehicles and disguised threats in the ground traffic. Since typical ground traffic in an urban environment is quite dense and involves numerous vehicles, achieving this surveillance capability by a single mobile plat¬form is unlikely to be feasible in many aspects. In particular, due to physical constraints, it might be difficult for one UAV to cover large areas simultaneously, which is often critical to mission success in a rapidly changing environment. Be¬sides, in order to obtain accurate information of ground traffic, a single UAV platform will need to rely on sensors which are expensive yet vulnerable to the failure of the platform or sensing block by obstacles. Using multiple UAVs with relatively cheap aboard sensors with information fusion techniques enhancing sensing accuracy could resolve above issues of a single platform without signifi¬cantly increasing an operational cost. Therefore, this thesis deals with the surveillance application of multiple air¬borne sensor platforms endowed with an appropriate level of autonomous de¬cision making to support human operators. A group of UAVs become multiple mobile sensor platforms, and tasks/routes of each UAV need to be efficiently and optimally planned to cooperatively achieve mission objectives. Efficient and sophisticated algorithms for data acquisition/analysis, information fusion, path planning and formation reconfiguration ensuring feasible and safe cooperation, and decision making for cooperative missions are essentially to be developed, in order to take advantage of multiple aerial sensing sources for surveillance. Among various techniques for autonomous surveillance as listed above, this the¬sis seeks to develop and (partly) integrate some of important components: search route planning, behaviour identification/recognition, and moving target tracking, while examining benefits and drawbacks of using multiple UAVs. A particular focus is on multi-sensor management and information fusion in consideration of physical constraints of the platform and strict real-time requirements of the applications in uncertain and dynamic environments. This research investigates the use of small and low-cost UAVs (Unmanned Aerial Vehicles) for autonomous aerial surveillance, which aims to identify and continuously track suspicious vehicles and disguised threats in the ground traffic. Since typical ground traffic in an urban environment is quite dense and involves numerous vehicles, achieving this surveillance capability by a single mobile plat-form is unlikely to be feasible in many aspects. In particular, due to physical constraints, it might be difficult for one UAV to cover large areas simultaneously, which is often critical to mission success in a rapidly changing environment. Be-sides, in order to obtain accurate information of ground traffic, a single UAV platform will need to rely on sensors which are expensive yet vulnerable to the failure of the platform or sensing block by obstacles. Using multiple UAVs with relatively cheap aboard sensors with information fusion techniques enhancing sensing accuracy could resolve above issues of a single platform without signifi-cantly increasing an operational cost. Therefore, this thesis deals with the surveillance application of multiple air-borne sensor platforms endowed with an appropriate level of autonomous de-cision making to support human operators. A group of UAVs become multiple mobile sensor platforms, and tasks/routes of each UAV need to be efficiently and optimally planned to cooperatively achieve mission objectives. Efficient and sophisticated algorithms for data acquisition/analysis, information fusion, path planning and formation reconfiguration ensuring feasible and safe cooperation, and decision making for cooperative missions are essentially to be developed, in order to take advantage of multiple aerial sensing sources for surveillance. Among various techniques for autonomous surveillance as listed above, this the¬sis seeks to develop and (partly) integrate some of important components: search route planning, behaviour identification/recognition, and moving target tracking, while examining benefits and drawbacks of using multiple UAVs. A particular focus is on multi-sensor management and information fusion in consideration of physical constraints of the platform and strict real-time requirements of the applications in uncertain and dynamic environments. This thesis firstly proposes a road-network search planning algorithm by which UAVs are able to efficiently patrol every road identified in the map. A mixed integer linear programming problem (MILP) is formulated to find an optimal so¬lution minimising a total flight time, while accommodating physical constraints of the UAV with the Dubins path. To overcome the computational burden of the MILP, an approximation approach is also proposed. By running Monte Carlo sim¬ulation with the randomly generated maps, an efficient UAV team size and path planning method is examined. Secondly, this thesis proposes a behaviour recog¬nition methodology for ground vehicles moving within road traffic to identify abnormal behaviour. Ground vehicle behaviour is first classified into represen¬tative driving modes, and string pattern matching theory is applied to detect suspicious behaviours in the driving mode history. Moreover, a fuzzy decision making process is developed to systematically exploit all available information obtained from a complex environment considering spatiotemporal environment factors as well as several aspects of behaviours. Lastly, to achieve continuous tracking of detected suspicious vehicles for closer and higher-resolution surveil¬lance data, this thesis proposes several coordinated standoff tracking guidance algorithms using multiple UAVs. The effect of the improved target estimation accuracy on the tracking guidance performance is also examined using roadmap information and sensor fusion techniques. From this thesis, it can be identified that following aspects need to be carefully considered to realise autonomous surveillance using multiple UAVs: i) how many UAVs/sensors would be enough to perform a mission in terms of efficiency, es¬timation accuracy and guidance performance, ii) information gathered by UAVs only is enough, or domain knowledge (local context and past experience) might be additionally required, iii) communication structure between UAVs, and iv) com¬putation time. The proposed autonomous surveillance system utilising multiple UAVs is expected to greatly increase the amount of area that can be continuously monitored, while reducing the number of human operators and their workload required to analyse surveillance data and respond to identified targets.
Supervisor: Tsourdos, A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.590408  DOI: Not available
Share: