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@@ -5,7 +5,7 @@ A collection of trajectory planning algorithms for mobile robots. I developed th
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## Requirements
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- Matlab
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- Peter Corke's Robotics, Vision and Control toolbox
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- CoppeliaSim simulator (earlier known as V-REP)
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-[CoppeliaSim simulator](https://coppeliarobotics.com/) (earlier known as V-REP)
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- Matlab bindings for CoppeliaSim
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## Overview
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Inputs to the MATLAB program consist of the position and orientation of the robot and data from the LIDAR sensor. Outputs from the program controls the linear and angular velocities of the simulated robot base wheels to perform a specified task. A Finite State Machine (FSM) manages the sequence of robot behaviours, e.g. initial, moving forward, stopping, moving backward.
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<!-- - Project 1 : Trajectory generation -->
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<!-- - Project 2 : Wall following and Bug2 algorithms using LIDAR data -->
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<!-- - Project 3 : Robot localization using particle filter, implementation of wavefront planner -->
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## Project 1
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Inputs to the MATLAB program consist of the position and orientation of the robot and data from the LIDAR sensor. Outputs from the program controls the linear and angular velocities of the simulated robot base wheels to perform a specified task. A Finite State Machine (FSM) manages the sequence of robot behaviours, e.g. initial, moving forward, stopping, moving backward.
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<!-- - Project 1 : Trajectory generation -->
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<!-- - Project 2 : Wall following and Bug2 algorithms using LIDAR data -->
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<!-- - Project 3 : Robot localization using particle filter, implementation of wavefront planner -->
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## Project 1
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- Straight-line trajectory generation
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- Moving along a circle trajectory with constant orientation
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- Moving along a circular trajectory with constant orientation
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The robot motion is generated by using proportional regulators with limited output.
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