Robotic Path Planning and Task Execution

Robotic Path Planning and Task Execution

This course is part of Introduction to Robotics with Webots Specialization

Instructor: Nikolaus Correll

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5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

14 reviews

Intermediate level

Recommended experience

4 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Use discrete planning techniques such as Dijkstra and A* to compute optimal robot trajectories.
Implement complex sequences of behaviors using behavior trees.
Plan and implement a complex robotic controller for autonomous mobile manipulation behavior.

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

10 assignments

Taught in English

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This course is part of the Introduction to Robotics with Webots Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 5 modules in this course

This course, which is the last and final course in the Introduction to Robotics with Webots specialization, will teach you basic approaches for planning robot trajectories and sequence their task execution. In "Robotic Path Planning and Task Execution", you will develop standard algorithms such as Breadth-First Search, Dijkstra's, A* and Rapidly Exploring Random Trees through guided exercises. You will implement Behavior Trees for task sequencing and experiment with a mobile manipulation robot "Tiago Steel".

It is recommended that you complete the first and second courses of this specialization, “Introduction to Robotics: Basic Behaviors” and "Robotic Mapping and Trajectory Generation" , before beginning this one. This course can be taken for academic credit as part of CU Boulder’s MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

Welcome to Robotic Path Planning and Task Execution. The first week in this course provides an introduction to path planning and presents a series of optimal algorithms for finding the shortest path on a graph that increase in complexity and efficiency. You will be introduced to various algorithms, including Bread-First Search to Dijkstra's and A*.

What's included

8 videos13 readings2 assignments1 discussion prompt

8 videos Total 71 minutes

Introduction to the Specialization 2 minutes
Meet the Instructor 2 minutes
Introduction to "Robotic Path Planning and Task Execution" 1 minute
Path Planning 29 minutes
Graph Traversal using Breadth-First Search (BFS) 8 minutes
Shortest Path on a Graph with BFS 9 minutes
Shortest Path on a Grid with BFS 9 minutes
From BFS to Dijkstra's on a Graph 13 minutes

13 readings Total 151 minutes

Course Updates and Accessibility Support 1 minute
Earn Academic Credit for your Work! 10 minutes
Course Support 10 minutes
Assessment Expectations 10 minutes
AI Citation and Acknowledgement 10 minutes
Course Activities 5 minutes
Why Planning? 5 minutes
More on Graphs to Represent Computational Problems 10 minutes
The Shortest Path Problem 10 minutes
Graph Traversal 15 minutes
Dijkstra's Algorithm 25 minutes
A* - Dijkstra's with Heuristic 15 minutes
Decompression - The Micro Mouse Competition 25 minutes

2 assignments Total 250 minutes

Planning on Graphs 10 minutes
Implementing a Planner 240 minutes

1 discussion prompt Total 10 minutes

Introduce Yourself! 10 minutes

In this week, you will learn how to efficiently plan in non-grid worlds and in high-dimensional spaces.

What's included

1 video4 readings4 assignments

1 video Total 23 minutes

Randomized Planning and Collision Avoidance 23 minutes

4 readings Total 60 minutes

Introduction to RRT 10 minutes
The RRT Algorithm 20 minutes
Collision Checking 10 minutes
Improving Optimality and Speed of RRT 20 minutes

4 assignments Total 380 minutes

AI Policy Quiz 5 minutes
Improving RRT 5 minutes
Hands-On: Implementing RRT 220 minutes
Hands-On: Implement Line-Based Collision Checking 150 minutes

This week introduces you to a new programming abstraction known as "Behavior Trees", which offers solutions to shortcomings of Finite State Machines and simple reactive controllers.

What's included

1 video4 readings2 assignments1 peer review

1 video Total 31 minutes

Behavior Trees 31 minutes

4 readings Total 55 minutes

Introduction 20 minutes
Parallel Nodes, Decorators and Blackboards 15 minutes
Exchanging Data Within BTs - Blackboards 10 minutes
Behavior Tree Libraries 10 minutes

2 assignments Total 190 minutes

Sequence and Selector nodes 10 minutes
Hands-On: Implementing Behavior Trees 180 minutes

1 peer review Total 240 minutes

Mapping and Navigation with BTs 240 minutes

This week you will learn how to extend a mobile base with a robotic arm and trigger the ability to grasp objects.

What's included

1 video2 readings2 assignments

In this last week, you will complete a final project to demonstrate your knowledge of both this course and the entire specialization. It consists of implementing a complete mobile manipulation solution.

What's included

1 video1 peer review

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Build toward a degree

This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹