الفهرس 
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Abstract
Coverage path planning is the problem of constructing a path which navigates through allfreepoints foragivenenvironment. Suchaproblemcan besolvedusingbothonline ando inecoveragealgorithms, wherethemaindierencebetweenthoseapproachesis prior knowledge availability. Many real-time applications bene t from coverage path planning algorithms like automated harvesters, search-and-rescue, complex structures inspections, or even vacuum cleaning. However, coverage path planning algorithms are developed employing a single robotic agent or multiple robotic agents. Therefore, this thesis aims to present a novel online coverage path planning approach in unknown environments, which utilizes dynamic programming with a rolling horizon limited look-ahead policy while using a multi-objective optimization genetic algorithm for optimizing a multi-objective tness function to ensure reaching an optimized solution, in addition to proposing a multi-agent scheme for parallelizing traditional coverage path planning along with presenting a new state space representation for multi-agent planning.