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1	Probability Based Path Planning of Unmanned Ground Vehicles for Autonomous Surveillance : Through World Decomposition and Modelling of Target Distribution Liljeström, Per January 2022 (has links) The interest in autonomous surveillance has increased due to advances in autonomous systems and sensor theory. This thesis is a preliminary study of the cooperation between UGVs and stationary sensors when monitoring a dedicated area. The primary focus is the path planning of a UGV for different initial intrusion alarms. Cell decomposition, i.e., spatial partitioning, of the area of surveillance was utilized, and the objective function is based on the probability of a present intruder in each cell. These probabilities were modeled through two different methods: ExpPlanner, utilizing an exponential decay function. Markov planner, utilizing a Markov chain to propagate the probabilities. The performance of both methods improves when a confident alarm system is utilized. By prioritizing the direction of the planned paths, the performances improved further. The Markov planner outperforms the ExpPlanner in finding a randomly walking intruder. The ExpPlanner is suitable for passive surveillance, and the Markov planner is suitable for ”aggressive target hunting”. Probability based path planning Unmanned ground vehicles UGV Autonomous surveillance Cell decomposition Spatial partitioning Target distribution modeling Decay function Last-seen function Stochastic processes Markov chain Markov process Markov planner Robotics Robotteknik och automation

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Probability Based Path Planning of Unmanned Ground Vehicles for Autonomous Surveillance : Through World Decomposition and Modelling of Target Distribution