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Title: Agent based mission planning for multiple unmanned autonomous vehicles
Author: Maqbool, Ayesha
ISNI:       0000 0004 2721 245X
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2012
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This thesis presents novel methods for agent-based mission planning sys- tem for multiple unmanned autonomous vehicles (MUAV). MUAV mission planning is the pinnacle of the intelligent systems. The ever-changing ad- verse environment and real-time co-operative decision making adds to the complexity of the system. Due to these inherent complexities, the progress from autonomic to autonomous MUAV system is still in its infancy. The conventional methods of distributed planning and decision making have shown some benefits but are not sufficient to develop the co-operative intelligence in MUAV system. Here we present agent-based approach for developing MUAV mission planning system by in-cooperative intelligent behaviours for self-organisation, self-awareness and intelligent decision making. These co-operative behaviours are aimed to add autonomy to the system. The requirements, interactions, functionalities and the role of these methods in overall system are established by in-depth study of existing control frameworks for MUAV management system. We also present a unified framework for NIUAV mission planning. This functional based framework provides a better yet simple understanding of the otherwise complex system. It serves the purpose of providing a better understanding of the challenges and opportunities in development of MUAV system by providing logical system construction of the MUAV mission planning in detail. To facilitate the self-awareness of the MUAV system, an efficient Advanced Integrated Method (AIM) path planning method is developed. AIM generates optimum obstacle free path from source to destination the consideration of UAV's safety. It combines the existing methods of Artificial potential, Maximum Clearance and Self Organizing Maps (SOM) for guaranteed convergence. We also present a model for the prediction of the future states of moving targets as stochastic processes with associated learned transition probabilities using Discrete Markov Chains (DMC). These predictions are then used for developing interception based target tracking. These predictions also provide fair and effective mean for target allocation among multiple UAV's and for target selection in the presence of multiple targets. The co-operative behaviour for MUAV system is further supported by a new and effective method of self-organisation. Inspired from thermodynamic systems, it introduces co-operative self-allocation of mission space with the objective of sharing surveillance responsibilities. These methods for path planning, prediction based decision making and self-allocation collectively provide the groundwork for building autonomous MUAV system.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available