TRON08017 2020 Robotic Path Planning

General Details

Full Title
Robotic Path Planning
Transcript Title
Robotic Path Planning
Code
TRON08017
Attendance
N/A %
Subject Area
TRON - Electronics
Department
COEL - Computing & Electronic Eng
Level
08 - NFQ Level 8
Credit
05 - 05 Credits
Duration
Semester
Fee
Start Term
2020 - Full Academic Year 2020-21
End Term
9999 - The End of Time
Author(s)
Saritha Unnikrishnan
Programme Membership
SG_EELEC_H08 202000 Bachelor of Engineering (Honours) in Electronics and Self Driving Technologies
Description

This module introduces the objectives and the application of path planning in robotics. The module introduces vehicle kinematics for autonomous navigation. The learner will gain knowledge in localisation, mapping, SLAM and path planning techniques. Python will be used as the programming language. Programming knowledge in Python and a good base in Algebra are the prerequisites for this module.

Learning Outcomes

On completion of this module the learner will/should be able to;

1.

Choose and apply the appropriate localisation and mapping algorithms for a given problem.

2.

Understand the application of the learned techniques in vehicle kinematics.

3.

Apply and synthesise simultaneous Localisation and Mapping (SLAM) algorithms such as Iterative Closest Point and Extended Kalman Filter (EKF SLAM)

4.

Evaluate path planning approaches such as Grid Based Search, Model Predictive Trajectory Generator and Probabilistic Road-Map planning.

5.

Identify the best solution for the problem at hand through guided research and team work.

Teaching and Learning Strategies

The theory of the key topics will be delivered through lectures every week. 

The code demonstrations of the algorithms will be performed using Python Jupyter notebooks on a weekly basis.

Online resources and book lists will be provided through Moodle to guide the students in the best way.

Assignments will challenge and assess the student's problem solving and logical reasoning abilities.

Module Assessment Strategies

A final written exam and continuous assessment in the form of group project work and classroom assignment will be used to assess the module.

To reinforce the theoretical principles covered in lectures, learners will participate in project work.

The learner will complete a final exam at the end of the semester.

The learner is required to pass both the project and the final examination element of this module.

Repeat Assessments

Repeat Exams will be set for Autumn of each year.

Repeat a given project work, which can be submitted at the repeat exam sitting.

Indicative Syllabus

Introduction to Vehicle Kinematics

  • Mechanics, Planning and Control in Robotics
  • Importance of Localisation, Mapping and Path Planning for autonomous navigation

Localisation

  • Kalman Filter Localisation
  • Particle Filter Localisation
  • Histogram Filter Localisation

Mapping

  • Gaussian grid map
  • Ray casting grid map
  • k-means object clustering
  • Object shape recognition

SLAM

  • Iterative Closest Point (ICP) matching
  • EKF SLAM
  • Fast SLAM
  • Graph based SLAM

Path Planning

  • Dynamic Window Approach
  • Grid based search
  • Model Predictive Trajectory Generator
  • Probabilistic Road-Map (PRM) planning

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
60 %
End of Semester / Year Formal Exam
40 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Programming assignment Continuous Assessment Open Book Exam 30 % OnGoing 1,2
2 Project Continuous Assessment Group Project 30 % Week 13 3,4,5
             

End of Semester / Year Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Final written Exam Final Exam Closed Book Exam 40 % End of Term 1,2,3,4
             
             

Full Time Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 1 Weekly 1.00
Laboratory Practical Computer Laboratory Practical 2 Weekly 2.00
Independent Learning Not Specified Independent Learning 4 Weekly 4.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Online Learning Mode Workload


Type Location Description Hours Frequency Avg Workload
Lecture Not Specified Lecture 1 Weekly 1.00
Laboratory Practical Online Online Lab 1 Weekly 1.00
Independent Learning Not Specified Independent Learning 5 Weekly 5.00
Total Online Learning Average Weekly Learner Contact Time 2.00 Hours

Required & Recommended Book List

Required Reading
2005-08-19 Probabilistic Robotics MIT Press
ISBN 9780262303804 ISBN-13 0262303809

Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

Module Resources