This document describes a modular pick and place simulator developed using the ROS framework. The simulator was designed to address challenges in robotics like uncertainty in scheduling tasks, irregular environments, and the need for safe and efficient systems. It uses a three-tier architecture for scene recognition, path planning and movement, and feedback control. ROS allows each tier to be developed as an independent node for modular and flexible design. The simulator was effective for teaching students about robotics challenges in an accessible way.

1. Modular Pick and Place Simulator using ROS
Framework
PEDRO TAVARES
JOSÉ LIMA; PEDRO COSTA; ANTÓNIO PAULO MOREIRA
ADAPTIVE PICK AND PLACE APPROACH USING ROS FRAMEWORK 1

2. Table of Contents
Introduction
Problem
Objectives
System Architecture
◦ Simulator in the Industrial Robotics Course
◦ Scene Recognition
◦ Configuration Space and Kinematics
◦ Robot Movement
◦ Control Tier
◦ ROS Framework
Discussion and Future Perspectives
ADAPTIVE PICK AND PLACE APPROACH USING ROS FRAMEWORK 2

3. 1. The field of Robotics has become one of the most rapidly growing fields in
the research and technological world.
2. Intelligent robots present key characteristics that enable the streamlining of
automated processes associated to industry.
3. Pick and Place operations have attracted considerable interest from the
research and industrial community as they present one of the most
effective solutions to typical problems such as handling or transportation.
Introduction
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Introduction
Problem

4. 1. Robotic manipulators allows to
maximize the efficiency in several
industrial processes.
2. The development of flexible robots
represents the possibility of them
becoming a highly efficient
operator.
3. Using a generic framework
promotes the development of
modular and simple software that
together fulfill the state-of-art
requests of the industry.
Introduction
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Introduction
Problem

5. 1. Develop a simulator that captures the main challenges in robotics:
i. Uncertainty in the scheduling of a robotic task.
1. Irregular and non structured environment.
2. Existence of static and dynamic structures.
ii. Difficulty in conciliation between Time and Complexity.
iii. Lacking of codification in a standard way.
iv. Need to develop a safe and efficiently system.
Problem
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Introduction
Problem
Objectives

6. Objectives
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Problem
Objectives
System Architecture
1. Development of a flexible system architecture.
i. Intelligent scene recognition.
ii. Generic Path planner.
iii. Low execution time.
iv. Modular development.
v. Easily adaptable.

7. System Architecture
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Objectives
System Architecture
Simulator in the Industrial Robotics Course
Tier ID Tier Specifications Examples
I
Recognition of the
environment.
Strategy Planning.
Image Processing, Camera-
Laser Triangulation…
II
Movement of the robot
Grabbing or Placement
A*, Dijkstra Algorithm,
Heuristics…
Joint Control, Strength
Control on Gripper, Close
Identification
III
Control of changes and
responses
Feedback Control Loops,
Adaptive Strategies

8. System Architecture
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Objectives
System Architecture
Simulator in the Industrial Robotics Course

9. Simulator in the Industrial
Robotics Course
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System Architecture
Simulator in the Industrial Robotics Course
Scene Recognition

10. 1. Image Processing;
2. 3D Model;
3. Object Classification;
4. Decision Tree;
5. Objects’ Dictionary / Class /
Instance;
Scene Recognition
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Simulator in the Industrial Robotics Course
Scene Recognition
Configuration Space and Kinematics

11. Configuration Space and
Kinematics
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Scene Recognition
Configuration Space and Kinematics
Robot Movement
1. Configuration Space.
i. Spacial Discretization.
ii. Structure containing the all the
possible robot configurations and
respective properties.
iii. Definition of each configuration
cell.

12. 2. Kinematics:
 Transformation of cartesian poses into robot states and vice-versa.
Configuration Space and
Kinematics
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Scene Recognition
Configuration Space and Kinematics
Robot Movement
DH Parameters
Appliance of Linear
Transformations
Transformations
Equations

13. 1. Determination of Final Pose.
2. Quadrant Consideration.
3. Approach Path Planning.
4. Joint Control.
Robot Movement
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Configuration Space and Kinematics
Robot Movement
Control Tier

14. 1. Feedback Loop.
2. Auto Corrective Algorithm.
3. Minimization of Errors.
Control Tier
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Robot Movement
Control Tier
ROS Framework

15. 1. ROS allows the
decomplexation of problems.
2. Each Tier of the proposed
system architecture can be
associated with a node which
runs separately from the
others.
3. The communication between
nodes is assure by topics,
services and messages.
ROS Framework
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Control Tier
ROS Framework
Discussion and Future Perspectives

16. 1. This simulator shown results in term of motivating students and it
allowed those students to understand in a simple way how the
robotics field is evolving.
2. Moreover, during the tasks they were confronted with a range of
situation commonly found in industrial environment surrounding
robotic manipulator arms.
3. The real problem of EuRoC shown to be useful in terms of
understanding the robotics situation and potential.
Discussion and Future
Perspectives
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ROS Framework
Discussion and Future Perspectives

17. ADAPTIVE PICK AND PLACE APPROACH USING ROS FRAMEWORK 17
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