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Multi Criteria Cooperative Target ObservationAuthor: MUNNANGI BALA SAI KRISHNA REDDY Date: 2020-08-12 Report no: IIIT/TH/2020/72 Advisor:Praveen Paruchuri AbstractThe Cooperative Target Observation (CTO) problem is an optimization problem in which a set of observer agents attempt to maximize the collective time during which each target from among a set of targets, is observed by at least one observer agent within an area of interest. The CTO problem has been well pursued in the multi-agents and robotics literature due to the problem being at the core of a number of applications such as surveillance, inspection, search-and-rescue among others. However, prior works are limited to handling homogeneous target types and the sole criterion of the set of observers is to maximize the observation of targets. In this work, we expand the CTO problem to include heterogeneous target types and constrain observers to incorporate other criteria during decision making while maximizing target observation. To this purpose, we pose Wildlife Monitoring domain as a CTO problem and propose a Multi Criteria Decision Analysis (MCDA) based algorithm named MCDA-CTO, to maximize the observation of different animal species and to effectively handle multiple target types (animal species) and the multiple criteria that arise due to the targets and environmental factors. To demonstrate the working of our algorithm, we present the performance results on two settings: (a) UAV setting i.e., Unmanned Air Vehicles (UAV) are modeled as observers and (b) UGV setting i.e., Unmanned Ground Vehicles (UGV) are modeled as observers. UAVs have uncertainty in observation of targets which makes it challenging to develop a high-quality monitoring strategy while UGVs are affected by the terrain they operate in i.e., UGVs have a different maximum speed in different terrains, thus needing to make suitable trade-offs. We therefore develop monitoring techniques that explicitly take actions to improve belief about the true type of targets being observed for UAV or optimize the ability to observe targets based on terrains for UGV. Furthermore, it is often reasonable to assume that the observers may themselves be a subject of observation by unknown adversaries (poachers). Randomizing the observer’s actions can therefore help to make the target observation strategy less predictable. We provide experimental validation which shows that the techniques we develop provide a higher performance along with better randomization than state of the art approaches. Full thesis: pdf Centre for Data Engineering |
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