Mobile Robot Control 2021 Group 8

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Group information

Group members

Name: Student ID: Email:
Rick Peeters 1021754 r.peeters@student.tue.nl
Rinse Hobma 1022328 r.hobma@student.tue.nl
Bram Odrosslij 1016625 b.odrosslij@student.tue.nl
Abdullah Cosgun 1020574 a.cosgun@student.tue.nl
Ivo Mix 1011609 c.g.mix@student.tue.nl

Tutor

Cesar Lopez Martinez c.a.lopez.martinez@tue.nl

General planning

Week 1 (19 April - 25 April)

  • Start with tutorials.
  • Prepare the software (Ubuntu, qtcreator, .. ).
  • Design document.
  • Project planning.

Week 2 (26 April - 2 May)

  • Introduction meeting with the tutor.
  • Watch tutorials 1.1, 1.2 & 1.3.

Week 3 (3 May - 9 May)

  • Deadline: Design document (4 May)
  • Finish the tutorials.
  • Watch tutorials 2.1, 2.2 & 2.3.
  • Orientating the escape room challenge.

Week 4 (10 May - 16 May)

  • Finish the script for the escape room competition.
  • Escape room competition (12 May)
  • Evaluating the result of the escape room competition.

Week 5 (17 May - 23 May)

  • Start oriëntating for the hospital competition.

Week 6 (24 May - 30 May)

Week 7 (31 May - 6 June)

  • Presentations (2 June)

Week 8 (7 June - 13 June)

  • Final Competition (9 June)

Week 9 (14 June - 20 June)

Week 10 (21 June - 23 June)

  • Deadline: Wiki pages (23 June)

Course description

Introduction

PICO

Assignment

Design document

The design document describes the initial idea for the solutation on the basis of the requirements, functions, components, specifications and interfaces.

The design document can be found here.

Escape Room Challenge

Strategy

The basis of the used strategy for the escape room challenge lies in the Artificial potential field algorithm. Together with the Finite state machine, that is defined in the design document, and the exit detection algorithm, forms this the strategy that is used in the escape room challenge.

Finite state machine (FSM)

Figure 1: Finite state machine for the Escape Room Challenge.

Artificial potential field algorithm / navigation module

The basis of the navigation module lies on the artificial potential field algorithm. This algorithm uses two vectors that determine the motion of the PICO robot. The first vector is the attractive vector, which is determined by the difference in the current position of the robot and the target position that is determined by the exit detection algorithm.

The other vector is the repulsive vector, which makes the PICO robot move away from the wall and is proportional to the distance to the wall. Thus the magnitude of the vector increases when the PICO robot is closer to the wall.

The path of the PICO robot is then determined by taking the sum of attractive vector and the repulsive vector. By updating these vectors with a frequency of 20 [Hz], the PICO robot will move in the direction of the target point while at the same time getting pushed away from the wall.

While implementing this algorithm, the group ran into difficulties and the navigation module was changed. The beginning of the algorithm stayed the same, the difference was made after the PICO robot is approaching the wall near the exit. When this happens the PICO robot moves, parallel to the wall at a fixed distance in the direction of the target point. This procedure happens until the PICO robot is located in the corridor.

..

Exit detection

Testing

A big part in the process of making the algorithm for the escape room challenge, is testing. This is therefore done thoroughly with multiple maps with each different properties to tackle problems that can occur during the actual escape room challenge.

To help understand what the robot sees, a visualization is made with the tools available in OpenCV. In these visualisations multiple objects can be identified. The blue lines represent the walls of the room, the green circles represent the end of line segments and corner points. The red dot is PICO robot itself and the pink dot is the target whereto the robot is driving.

First test map

Figure 2: Testing the algorithm in a map with a small exit on the top.

Second test map

Figure 3: Testing the algorithm in a map with a small exit on the bottom.

Third test map

Figure 4: Testing the algorithm in a map with a small exit on the left.

Fourth test map

Figure 5: Testing the algorithm in a map with a small exit on the left.

Result

Figure 5: The result of the escape room challenge.

Hospital Challenge

Strategy

Finite State Machine