How Walking Robot Assignments are Modeled and Simulated Using Simulink and Simscape

Amit Khurana

United Kingdom

Simulation

Amit Khurana has over 9 years of experience in robotics simulation and control systems. He completed his Master’s degree from the University of Central Lancashire, UK.

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Simulation plays a critical role in modern robotics for two main reasons: safety and efficiency.

Safety

When working with hardware, robots are prone to falls and damage—especially during early testing phases. Simulation allows you to validate algorithms and mechanical designs before moving to physical prototypes. This reduces the risk of damaging expensive hardware and makes it easier to identify design flaws early. You can test your robot under extreme or unsafe conditions in a virtual environment, which would be difficult or even dangerous to replicate in real life.

Efficiency

Physical experiments are time-consuming and often require manual resets between tests. In contrast, simulations can be run repeatedly, and even automated. For example, testing a walking gait with varying terrain types becomes easy with simulation, as you don’t need to rebuild or reprogram hardware every time. This speeds up the development cycle significantly.

When your robot is controlled by embedded systems, simulation lets you iterate quickly on algorithms before porting them to hardware. This separation helps pinpoint whether issues are due to software changes or hardware constraints.

Components of a Robot Simulation

A complete robot simulation consists of multiple layers. Depending on your goal, you might only need a few of these components. Here’s a breakdown of the essential parts.

Robot Mechanics

The mechanical model of the robot is built using Simscape Multibody. There are two main approaches to modeling the 3D structure of a robot:

• Build from Scratch — This method allows full control over design parameters such as length, mass, inertia, and joint placement. It’s ideal during the conceptual phase of a project because you can easily change physical properties to explore multiple design options.
• Import from CAD — If a robot has already been designed in a CAD tool, you can import the entire model into Simscape Multibody. This preserves geometric accuracy and real-world properties. Any changes made to the CAD design can be updated in the simulation with minimal effort.

Adding Dynamics

Once the mechanical model is built or imported, it needs to be dynamic—able to move under forces and interact with its environment.

Internal Dynamics

Each joint, whether translational or rotational, can be given physical properties such as stiffness and damping. These parameters affect how the robot responds to forces and how it moves.

External Dynamics

Gravity must be set up correctly, especially for legged robots. One of the key aspects of walking robots is the interaction with the ground. Starting with R2019b, Simscape Multibody includes the Spatial Contact Force block, which makes it easier to simulate foot-ground contact. For older versions, external libraries like the Simscape Multibody Contact Forces Library can be used.

Actuators and Control

Actuators connect your control algorithms with the physical robot. They are essential in bringing the robot to life.

Actuator Control

In Simscape, motion can be prescribed to actuator models. This is useful for determining what kind of actuator (torque, power, size) is required to perform a particular motion. Once the actuator model is ready, Simulink can be used to design and test controllers.

Actuator Dynamics

For more accurate simulations, actuator models can include physical domains like electricity (for motors) or fluids (for hydraulic or pneumatic actuators). This multi-domain modeling allows you to test complete systems, including power supply and response times.

Modeling Detail vs. Simulation Speed

The level of detail in the model affects simulation speed. For example, a high-level motion planning algorithm may require simulating several minutes of walking, whereas testing a low-level motor controller with PWM may only require milliseconds of simulation. It’s useful to build modular and scalable models using features like variants, block libraries, and model references in Simulink.

This way, you can reuse components across simulations with different fidelity needs.

Motion Planning for Walking Robots

Motion planning can either be open-loop or closed-loop. Both have their pros and cons and serve different purposes.

Open-Loop Planning

In this approach, predefined motions are executed regardless of the environment. For instance, you can develop a walking pattern that works under ideal conditions. These are relatively simple to implement and useful for early testing.

Closed-Loop Planning

Here, the robot adapts based on real-time feedback. Closed-loop planning is more robust to environmental changes, like uneven terrain or external disturbances. It also relies on sensor data to determine corrective actions.

Sensors used in walking robots typically include:

• Joint encoders
• Accelerometers and gyroscopes
• Force sensors in the feet
• Cameras and LiDAR for terrain mapping

The sensor data feeds into the control logic, which could be model-based (like PID or internal model control) or learned using machine learning methods like reinforcement learning.

Optimization in Simulation

Optimization tools help fine-tune various parts of the robot and its control system.

Optimizing the Mechanical Design

Simulation lets you experiment with different sizes, weights, joint positions, and stiffness to see how changes affect the robot's walking ability. For example, reducing leg weight may improve stability but reduce torque output.

Tuning Controllers

Controllers often need gains, thresholds, or rate limits adjusted. These parameters can be optimized in simulation to achieve better performance, safety, or energy efficiency.

Planning Efficient Gaits

Walking trajectories can also be optimized. A popular approach is to use genetic algorithms or other heuristic methods to evolve walking patterns that meet stability and speed goals. While this doesn’t guarantee the most elegant solution, it provides a good starting point for further refinement.

Importance of Closed-Loop Feedback

Open-loop control can be limiting in dynamic environments. A better solution is to incorporate sensors and feedback systems to create a reactive walking robot.

Example: Stability Control

Suppose the robot slips on a low-friction surface. An open-loop controller might not recover, leading to a fall. A closed-loop controller can detect instability and adjust gait or foot placement to avoid falling.

Machine Learning Approaches

Machine learning techniques like reinforcement learning are increasingly used for control and planning. These systems learn from repeated simulations to improve their behavior. A reinforcement learning agent can optimize walking patterns over thousands of iterations in a simulation environment before ever touching real hardware.

Running Batch Simulations

One of the major advantages of simulation is the ability to automate and parallelize runs. For example, you might want to evaluate:

• How different terrains affect walking stability
• How actuator delays influence balance
• Which control parameters minimize power consumption

With batch simulations, you can run hundreds or even thousands of test cases using tools like MATLAB scripts or Simulink Test. This large-scale testing provides insight into performance and robustness that would be difficult to achieve physically.

Final Thoughts on Walking Robot Simulation

Simulating walking robots involves the integration of mechanical modeling, actuator dynamics, motion planning, control algorithms, and optimization. Simulink and Simscape provide a powerful platform to bring all these components together.

With proper simulation, engineers and researchers can:

• Safely test risky scenarios
• Rapidly iterate designs
• Optimize performance
• Develop robust control systems

Walking robots are inherently complex, but simulation helps break this complexity into manageable components. By modeling the robot, its environment, and its controller in simulation, you can refine and validate your design before it ever walks in the real world.

Whether you're a student working on a robotics project or part of a research team developing advanced legged systems, understanding and leveraging simulation tools is a crucial skill.

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