1© 2017 The MathWorks, Inc.

Designing Autonomous Robotic

Systems with MATLAB and Simulink

Carlos Santacruz-Rosero, PhD

Sr Application Engineer – Robotics

Pulkit Kapur

Sr Industry Manager – Robotics

Trudy Rippel

Account Management Team

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Robotics Applications using MATLAB and Simulink

Autonomous Ground Robots Manipulator Arms

Visual Odometry Advanced Perception and Planning

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Some challenges….

Applying Multidomain Expertise

Technical Depth and System Stability

IP Protection

End-to-End workflows

Complexity of Algorithms

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Agenda

▪ Introduction

▪ Demo

▪ Conclusion

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DemoPlatform

Perception

Control

Planning

Sense

Connect

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C++ ROS node

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Ground Vehicle Co-simulation with Gazebo

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Modeling: Import CAD and URDF

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MATLAB, Simulink, and ROS

MATLAB Code

SM Models

Built-in

algorithms

Robot

ROS node

Simulation

environment

Networking

Code Generation

ROS Bag import

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Synthetic

data

Fusion & Tracking Algorithm Development Workflow

Logged

multi-sensor

data

Algorithm

Create new scenario or refine sensor model

Collect lab data

Refine algorithm

yes

no

Expected

Behavior

C Code

Generate code

Integrate

with embedded

environment

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Humanoid Simulation

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Planning and Control for Ground Robots

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Integrate with common robotics frameworks

• Import and filter ROS

bags

• Connect MATLAB and

Simulink to ROS

• Automatically generate

C++ for ROS nodes

Co-simulation with Gazebo (CAT Vehicle Simulator) Clear separation between your algorithm

and the middleware/hardware

17Path Tracking for Unmanned Ground Vehicle

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Robot SimulationLow-Level ControlRobotics Algorithm

Pure Pursuit with UGV: Multi-domain simulation

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Underwater Vehicle Co-simulation with UWsim

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UAV Co-simulation with Gazebo

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Integrate Robotics Algorithms

MATLAB

Runtime

C, C++ HDL PLC

Embedded Hardware

C/C++ ++ExcelAdd-in Java

Hadoop/Spark

.NETMATLAB

ProductionServer

StandaloneApplication

Enterprise Systems

Python

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Apps: Ground Truth Labeler

Why?

1. Verifying Algorithms

2. Training Classifiers

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Perception with MATLAB

New App for

Ground Truth

Labeling

Label pixels

and regions for

semantic

segmentation

Caffe model

importer

LSTM (time series, text)

DAG Networks

Library of

pretrained

models

Multi-GPUs in

parallel

Optimized GPU

code

Training plots

NEW

PRODUCT:

GPU Coder-

Convert to

NVIDIA CUDA

code

Deploy / ShareTrain / PredictModelsData

“How do I label

my data?”

“How do I access

the latest models?”

“How do I make training

and prediction faster?”

“How do I deploy

my new model?”

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Key Takeaways

▪ 1. Productivity increased through an interactive environment to

prototype, develop, debug, and implement algorithms in robotics.

▪ 2. Model-Based Design for motion control systems allows early

verification of requirements, production code generation, and

continuous testing.

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Takeaways

▪ Workflows in robotics design and simulation, perception, planning, and controls.

▪ Meet us in San Jose during MATLAB EXPO (November 7th)

▪ ROS, ROS 2.0 and DDS support

▪ Image Processing and Computer Vision

▪ Machine Learning

▪ Deep Learning

▪ Path Planning

▪ SLAM

▪ Sensor Models and Sensor Fusion

▪ FDA Software Validation

▪ Ground truth labeling with LiDAR and Vision

▪ 3D Simulation with Physics

▪ Real-time support

▪ Code-generation for C/C++/CUDA/HDL/PLC

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% Thank you