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Path Planning Algorithms for Autonomous Mobile Robots

Title:

Path Planning Algorithms for Autonomous Mobile Robots

AskariHemmat, Mohammad Ali (2018) Path Planning Algorithms for Autonomous Mobile Robots. Masters thesis, Concordia University.

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Abstract

This thesis work proposes the development and implementation of multiple different
path planning algorithms for autonomous mobile robots, with a focus on differentially
driven robots. Then, it continues to propose a real-time path planner that is capable of finding
the optimal, collision-free path for a nonholonomic Unmanned Ground Vehicle (UGV)
in an unstructured environment. First, a hybrid A* path planner is designed and implemented
to find the optimal path; connecting the current position of the UGV to the target
in real-time while avoiding any obstacles in the vicinity of the UGV. The advantages of
this path planner are that, using the potential field techniques and by excluding the nodes
surrounding every obstacles, it significantly reduces the search space of the traditional A*
approach; it is also capable of distinguishing different types of obstacles by giving them
distinct priorities based on their natures and safety concerns. Such an approach is essential
to guarantee a safe navigation in the environment where humans are in close contact with
autonomous vehicles. Then, with consideration of the kinematic constraints of the UGV,
a smooth and drivable geometric path is generated. Throughout the whole thesis, extensive
practical experiments are conducted to verify the effectiveness of the proposed path
planning methodologies.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Thesis (Masters)
Authors:AskariHemmat, Mohammad Ali
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Mechanical Engineering
Date:19 July 2018
Thesis Supervisor(s):Zhang, Youmin
ID Code:984157
Deposited By: MOHAMMAD ALI ASKARI HEMMAT
Deposited On:16 Nov 2018 16:24
Last Modified:16 Nov 2018 16:24
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