Return to search

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

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”.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-185544
Date January 2022
CreatorsLiljeström, Per
PublisherLinköpings universitet, Reglerteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

Page generated in 0.0027 seconds