How MATLAB R2019b Transformed Robotics Assignment Development

Dr. Alan Whitmore

United Kingdom

Robotics

Dr. Alan Whitmore has over 12 years of experience in robotics and control systems. He completed his Ph.D. in Electrical Engineering from Midlands Technical University, UK.

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Simulation lies at the core of modern robotics development, offering a safe and efficient way to test ideas before moving to real-world prototypes. MATLAB’s Robotics System Toolbox has steadily grown into a trusted environment for modeling and simulating robots, and the R2019b release introduces even more valuable features. One of the highlights is the availability of specialized low-fidelity robot models. These simplified models are not burdened with the complexities of precise hardware details but instead emphasize the control and motion planning aspects of robotics systems.

This is particularly important during the initial stages of design, where the focus is on developing and testing high-level algorithms such as motion planning, task scheduling, and trajectory generation. With low-fidelity models, you can quickly experiment, identify errors, and refine your strategies before progressing to high-fidelity physics simulations or physical robots. In addition, R2019b strengthens support for low-level control design workflows. Once motion plans are defined, the next step is to link them with actuators and motors, ensuring the robot can execute the commands accurately. MATLAB provides tools for detailed analysis, enabling designers to verify system behavior effectively. These advancements also make it easier to complete your MATLAB assignment with confidence and precision.

Manipulators and Robotic Arms

Robotic manipulators—commonly seen in factories, warehouses, and even medical devices—are an essential category in robotics. MATLAB R2019b introduces a library of commercial robot models that gives students and engineers the ability to experiment with widely used manipulator designs without needing physical hardware.

In addition, this release supports joint-space and task-space motion models. These models allow developers to explore how robots move either in terms of joint angles or the position of the end effector in physical space. By combining these models with collision detection tools, you can design realistic applications such as pick-and-place tasks or automated assembly operations.

A particularly useful aspect is that these new features integrate seamlessly with MATLAB’s existing kinematics, dynamics, and trajectory planning tools. For example, you can use inverse kinematics to calculate joint positions for a desired end-effector location and then test collision-free trajectories.

The release also includes new manipulator examples, which are invaluable for students. These examples range from simple trajectory planning exercises to full pick-and-place workflows. For a learner, these examples serve as hands-on templates to understand both theory and application.

Ground Vehicles and Mobile Robots

Mobile robotics is rapidly growing, particularly with the rise of autonomous delivery systems, warehouse automation, and self-driving cars. MATLAB R2019b introduces kinematic motion models for mobile robots, such as differential drive and car-like platforms.

These models provide a simplified yet realistic representation of how mobile robots move in an environment. With them, you can experiment with designing navigation algorithms, testing controllers, and integrating sensors.

For example, a student working on an autonomous warehouse robot can now simulate path planning in a cluttered environment, ensuring the robot can move between shelves without collisions. On a larger scale, these same tools can be applied to research in autonomous vehicles, where kinematic constraints like turning radius are critical.

R2019b also introduces new warehouse robot examples, which go beyond single-robot navigation to include multi-robot coordination. This is especially valuable in studying swarm robotics, where multiple robots must work together to achieve a common goal.

Gazebo Cosimulation

One of the standout features in MATLAB R2019b is the direct interface between Simulink and the Gazebo simulator. While previous versions allowed Gazebo integration through ROS, this direct interface synchronizes MATLAB’s Simulink models with Gazebo’s dynamic environment in real time.

This is a huge step for robotics simulation. With this setup, you can design your control algorithms in Simulink while simultaneously receiving realistic sensor feedback from Gazebo, including images, lidar scans, and depth data.

For reinforcement learning (RL) applications, this feature is transformative. Training RL agents requires tight feedback loops between actions and observations. By combining Simulink’s RL frameworks with Gazebo’s visual and physical environments, you can train intelligent agents for tasks like object manipulation or navigation.

For example, in one application, a robotic arm can be trained using convolutional neural networks (CNNs) to identify and pick up objects. Gazebo provides the camera images, while MATLAB handles the training algorithms. This approach offers a safe, scalable, and flexible way to train advanced robotic intelligence without risking physical equipment.

Navigation and Motion Planning

Autonomous navigation is at the core of robotics. Whether it’s a robotic arm, a drone, or a self-driving car, navigation involves mapping, planning, and following paths in an environment.

Egocentric Occupancy Maps

A major update in R2019b is the introduction of egocentric occupancy maps, or local maps. Unlike global maps, which cover an entire environment, local maps focus on the area immediately around the robot. These maps move with the robot, providing a more efficient representation for path correction and obstacle avoidance.

This improvement is crucial for real-world robotics because environments are rarely static. For instance, a delivery robot navigating a busy campus cannot rely solely on a static global map—it must adapt in real time to people walking by, doors opening, or objects moving. Egocentric maps make this possible.

SLAM and 3D Mapping

Simultaneous Localization and Mapping (SLAM) remains a cornerstone of navigation research. In R2019b, MATLAB enhances its SLAM capabilities by allowing the creation of 3D occupancy maps from lidar point clouds. These maps can be combined with pose graph optimization to improve accuracy, making it easier to represent large, complex environments in detail.

Students can experiment with 3D SLAM using readily available datasets or their own lidar data, gaining practical experience with one of the most important technologies in modern robotics.

Advanced Path Planners

Navigation Toolbox now includes Hybrid A* and Rapidly-Exploring Random Tree (RRT) planners. Unlike the older Probabilistic Roadmap (PRM) planner, these new planners can handle nonholonomic constraints—meaning they take into account the real-world limitations of how robots move.

For example, a car-like robot cannot simply move sideways; it must respect its turning radius. Hybrid A* and RRT ensure that generated paths are feasible, not just theoretically possible.

These planners are customizable, allowing advanced users to define new state spaces, collision validators, and planning strategies. At the same time, beginners benefit from built-in utilities and ready-to-use examples. This flexibility makes Navigation Toolbox suitable for both academic learning and advanced research.

ROS Connectivity

Perhaps one of the most impactful updates in R2019b is the introduction of the ROS Toolbox. All ROS-related functionality is now consolidated into this single toolbox, simplifying workflows for students and researchers working with ROS-based robots.

ROS and ROS 2 Support

ROS Toolbox supports both ROS and ROS 2, the latter being increasingly important in modern robotics due to its improved communication and multi-platform support. This means students can now experiment with both versions, preparing them for industry demands.

The toolbox allows you to work with ROS bag logfiles, prototype algorithms through desktop simulation, and eventually deploy distributed networks of standalone ROS nodes. This end-to-end support is crucial for students working on projects that need to move from simulation to physical hardware.

Application Examples

MATLAB R2019b includes new examples such as sign-following robots and automated parking valet systems, showcasing practical applications of ROS and ROS 2. These examples are particularly valuable for learners because they demonstrate not only how to use the tools but also how to apply them to real-world robotic challenges.

Why These Updates Matter for Students

For university students, MATLAB R2019b is not just about new features—it’s about opportunities for learning and innovation. Here’s why:

1. Hands-on Learning – The new manipulator and mobile robot examples provide ready-made projects for coursework and assignments.
2. Bridging Theory and Practice – Low-fidelity models let you test concepts quickly, while high-fidelity integration with Gazebo bridges the gap to real-world applications.
3. Research-Ready Tools – Advanced path planning, SLAM, and reinforcement learning frameworks provide a foundation for academic research and thesis work.
4. Industry Relevance – ROS 2 support ensures that students are learning skills aligned with the robotics industry’s future.

Conclusion

MATLAB R2019b is a major step forward for robotics development. With updates in simulation, manipulation, navigation, and ROS connectivity, it empowers students and researchers to design, test, and deploy robotic systems more efficiently than ever before. From training reinforcement learning agents with Gazebo to experimenting with Hybrid A* planners for autonomous cars, the opportunities are vast.

For students tackling robotics assignments or working on advanced projects, these tools make it easier to bring ideas to life. Whether you are just beginning your journey in robotics or diving deep into research, MATLAB R2019b provides the foundation to innovate, experiment, and succeed.