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School Logo Cleveland State University Logo Title Evolutionary Optimization For Safe Navigation Of An Autonomous Robot In Cluttered Dynamic Unknown Environments, Arash Roshanineshat
School Logo Cleveland State University Logo Title Evolutionary Optimization For Safe Navigation Of An Autonomous Robot In Cluttered Dynamic Unknown Environments, Arash Roshanineshat
ETD Archive
We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to …
Evolutionary Optimization For Safe Navigation Of An Autonomous Robot In Cluttered Dynamic Unknown Environments, Arash Roshanineshat
Evolutionary Optimization For Safe Navigation Of An Autonomous Robot In Cluttered Dynamic Unknown Environments, Arash Roshanineshat
ETD Archive
We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to …
Oppositional Biogeography-Based Optimization, Mehmet Ergezer
Oppositional Biogeography-Based Optimization, Mehmet Ergezer
ETD Archive
This dissertation outlines a novel variation of biogeography-based optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs opposition-based learning (OBL) alongside BBO migration to create oppositional BBO (OB BO). Additionally, a new opposition method named quasi-reflection is introduced. Quasireflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods that we explore. Performance of quasi-opposition is validated by mathematical analysis for a single-dimensional problem and by simulations for higher dimensions. Experiments are performed on benchmark …