Robotic locomotion refers to the various ways robots move from one place to another. It combines concepts from mechanics, control systems, and artificial intelligence (AI) to enable efficient and adaptive motion. Depending on their function and operating environment, robots use different locomotion strategies, with legged and wheeled locomotion being the most widely adopted.1-3
Image Credit: Leo Pakhomov/Shutterstock.com
An Overview of Robotic Locomotion
In many cases, robotic movement is inspired by nature. For example, six-legged robots mimic the walking patterns of stick insects, making them well-suited for navigating extreme environments. However, replicating such natural mechanisms isn’t straightforward. Challenges like mechanical complexity, energy demands, and stability often arise.
Three key factors influence locomotion strategies:
- Environment – Whether the terrain is flat, rough, hard, or soft.
- Ground Contact – The type and angle of contact points and surface friction.
- Stability – Factors like center of gravity, inclination, and the number of contact points.
These considerations are particularly significant for legged and wheeled robots, as each excels in different environments.1-3
Types of Locomotion
Wheeled Locomotion
Wheeled locomotion is the go-to choice in mobile robotics, thanks to its straightforward mechanical design, stability without complex balance control (for robots with at least three wheels), and high energy efficiency—even at faster speeds.
Unlike legged robots, wheeled robots rarely face stability challenges. Instead, the focus is on optimizing control, maneuverability, and traction, particularly on rough terrains. While two wheels are enough to achieve static stability, most wheeled robots are designed with at least three wheels to maintain consistent ground contact and enhance stability during operation.
There are four main types of wheels commonly used in wheeled robots:
- Castor Wheels – These have two degrees of freedom (DOF) and are simple to implement. They handle loads well and tolerate uneven ground but are not omnidirectional.
- Standard Wheels – Also with two DOF, these wheels are durable and reliable for straightforward motion, but like castor wheels, they lack omnidirectional capability and require steering adjustments, which can create friction and reduce precision.
- Swedish Wheels – These wheels, available in 45° and 90° configurations, feature passive rollers that allow smooth, omnidirectional movement. They enable holonomic motion, giving robots the ability to move in any direction without complex steering.
- Spherical Wheels – True omnidirectional wheels, these allow unrestricted movement in any direction but are challenging to implement due to their complexity.
Omnidirectional wheel designs, while highly maneuverable, come with trade-offs. Even small errors in wheel speed can disrupt the robot’s path, making precise control essential. Robots like Tribolo demonstrate how spherical wheels can be used effectively. In this design, three spherical wheels are each powered by a motor and secured with three contact points to the chassis—two spherical bearings and one directly linked to the motor axis.
The choice of wheel type depends on the robot’s intended use, balancing simplicity, efficiency, and advanced maneuverability to meet the demands of its operating environment.
Legged Locomotion
Legged robots are built to tackle tough terrains where wheels fall short. They can cross wide gaps, climb steps, and handle irregular surfaces with ease. To make this possible, each leg needs at least two degrees of freedom (DOF), which typically means one servo motor per joint. For instance, a four-legged robot would require a minimum of eight servos to move effectively.
While legged robots shine on uneven ground, they use much more energy than wheeled robots on flat surfaces—sometimes up to 100 times more. Wheeled robots, needing fewer motors, are generally more energy-efficient on smooth terrain. However, when the surface is soft or loose, wheels struggle with higher rolling resistance, giving legged robots the advantage.
For stable walking, a robot usually needs at least four legs to keep constant ground contact. Many designs opt for six legs, allowing one leg to lift while the others maintain balance. This approach, called statically stable walking, ensures the robot stays balanced throughout its gait cycle.
Bipedal robots, or two-legged designs, rely on dynamic stability—a more complex balancing act. Engineering these robots is a significant challenge, and their use is often limited to controlled scenarios where stability can be consistently managed.
Adding more DOFs to a robot improves its ability to navigate rough terrain and perform intricate maneuvers. However, this comes with trade-offs: increased power demands, added weight, and more complex controls. Coordinating multiple motors requires precise engineering to maintain smooth and reliable movement.
This balance of capability, efficiency, and complexity continues to drive innovation in legged robot design.2,3
Other Locomotion Strategies
While wheeled and legged locomotion dominate the field, other innovative approaches have been developed to address specific challenges in rough terrains and hybrid environments.
Tracked Slip/Skid Locomotion
Wheeled robots, particularly those with omnidirectional capabilities like spherical or Swedish wheels, face significant power inefficiencies on rough terrains due to increased rolling friction. In such cases, tracked vehicles offer a compelling alternative.
Robots like the Nanokhod use tracks to achieve superior traction on loose surfaces and can cross larger gaps with ease. However, tracked systems have their limitations—they require more space for turning because of skidding and experience high friction on hard surfaces. This not only increases power consumption but also makes it harder to predict movement and orientation changes accurately.3
Walking Wheels
Walking wheels blend the advantages of both legged and wheeled locomotion to overcome the limitations of each. While legged robots excel at climbing stairs and navigating rugged terrains but are inefficient on flat surfaces, and wheeled robots are energy-efficient on hard surfaces but struggle with obstacles, walking wheels offer a middle ground.
The Shrimp robot is a prime example of this hybrid approach. It features six motorized wheels that can climb barriers twice their wheel diameter and execute precise maneuvers with synchronized steering. Remarkably, the Shrimp adapts to obstacles passively, relying on its flexible mechanical structure rather than sensors, allowing it to tackle uneven environments with impressive efficiency.3
Recent Developments in the Field
As robotics continues to evolve, artificial intelligence (AI) is proving to be a game-changer in locomotion technology. Traditional methods of controlling robot movement are being replaced by AI-driven approaches that simplify control systems, improve adaptability, and open up new possibilities for how robots navigate their environments. Two recent advancements—one in quadrupedal robots and another in bipedal designs—showcase the exciting potential of these innovations.
Smarter Quadruped Control
In a study published in the Journal of Mechanical Science and Technology, researchers introduced an AI-based control algorithm designed for self-balancing quadruped robots. This algorithm moves away from traditional methods that rely on complex equations, replacing them with artificial neural networks (ANN) and reinforcement learning (RL).
The process works like this: RL generates training data, which is then used to train the ANN model. Once trained, the ANN efficiently manages the robot’s balance and movement. Testing with a customized quadruped robot demonstrated that this approach could streamline control systems while maintaining performance, marking a significant shift in how robotic locomotion is handled.4
AI-Powered Bipedal Walking Mechanisms
Meanwhile, another study published in the Grenze International Journal of Engineering & Technology (GIJET) focused on improving bipedal locomotion using AI. This project tackled the challenges of two-legged robots, optimizing their gait for better adaptability and efficiency.5
Through machine learning, the robot was able to adjust its walking patterns based on terrain, speed, and energy requirements. Sensors integrated with AI provided enhanced balance and stability, while predictive analytics enabled the robot to avoid obstacles and prevent collisions. These advancements bring bipedal robots closer to operating effectively in real-world environments.
Future Outlook
As robotics advances, future locomotion systems will integrate AI to help robots navigate diverse terrains and complex environments with greater autonomy. Techniques like machine learning and reinforcement learning are set to enhance movement, making robots more agile, efficient, and precise.
While challenges such as stability, energy use, and control remain, progress in AI-driven methods offers practical solutions. These innovations bring us closer to developing robots capable of operating effectively in real-world conditions.
Want to Learn More About Robotic Locomotion?
Robotics is reshaping how machines interact with the world, enabling smoother navigation across challenging terrains and opening doors to new applications in industry, research, and daily life. From improving agility to optimizing energy use, advancements in locomotion are making robots more capable and versatile than ever.
Ready to explore more? Check out these articles:
References and Further Reading
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.