A comprehensive autonomous navigation strategy for mobile robots using the grey wolf optimizer with a fuzzy logic controller and a sense-to-avoid system based on optical flow

This thesis develops an autonomous navigation strategy for wheeled mobile robots in static anddynamic settings by integrating global and local path planning with motion control. Global pathplanning utilizes the Grey Wolf Optimization (GWO) algorithm, while local path planning employsOptical Flow and ultrasonic sensors for obstacle detection, and Fuzzy Logic Controllers (FLCs)manage motion control. The GWO algorithm effectively identifies paths in environments with upto 80% obstacle density. FLCs ensure accurate path following, and the Optical Flow-based systemefficiently evades obstacles

Introduction -- 1 Problem Statement -- 2 Hypothesis -- 3 General Objective -- 4 Thesis Organization -- 1 Theoretical Framework & Literature Review -- 1.1 Robotics Navigation Problem Overview -- 1.1.1 Localization and Mapping -- 1.1.2 Path and Motion Planning -- 1.2 Path Planning -- 1.2.1 Configuration Space -- 1.2.2 Planning in Dynamic Environments -- 1.2.3 Global & Local Path Planning -- 1.3 Path Planning Related Work -- 1.3.1 Classical Path Planning -- 1.3.2 Heuristic Path Planning -- 1.3.3 Analysis and Discussion -- 1.3.4 Path Planning Comprehensive Overview -- 1.4 Motion Planning -- 1.4.1 Kinematics System Overview -- 1.4.2 Perception System Overview -- 1.4.3 Motion Planning System Integration -- 1.5 Key Insights -- 2 Proposal Description & Methodology -- 2.1 Problem Definition -- 2.1.1 Simulation Environment -- 2.1.2 Pioneer 3-DX -- 2.2 Methods and Techniques -- 2.2.1 Grey Wolf Optimization -- 2.2.2 Optical Flow -- 2.2.3 Fuzzy Logic Controller -- 2.3 Navigation Strategy General Description -- 2.3.1 Global Planning Subsystem -- 2.3.2 Local Planning Subsystem -- 3 Results & Analysis -- 3.1 Global Path Planning Test -- 3.1.1 Experiment on GWO Position Update Equations -- 3.1.2 Experiment on the Static Obstacles Density in the Scene -- 3.1.3 Experiment on Scene Size -- 3.2 Global Path Following Test -- 3.2.1 Experiment on the Effectiveness of Path Following -- 3.3 Local Path Planning Test -- 3.3.1 Experiment on the Texture and Shape of Static Obstacles -- 3.3.2 Experiment on the Navigation Skills with Unknown Static Obstacles -- 3.3.3 Experiment on Reactive Capacity against Dynamic Obstacles -- 3.4 Comprehensive Autonomous Navigation Strategy Test -- 3.4.1 Experiment on Scenario 1: Hospital Room -- 3.4.2 Experiment on Scenario 2: Factory Workstation -- 3.5 General Analysis -- 4 Conclusions & Future Work