What Are Mobile Robots?

By Ankit SinghReviewed by Susha Cheriyedath, M.Sc.Sep 26 2024

Mobile robots represent a pivotal advancement in automation and robotics, characterized by their ability to move and navigate their environment autonomously or semi-autonomously. These machines have revolutionized industries ranging from manufacturing to healthcare, playing critical roles in operations that require mobility. By leveraging sophisticated hardware, control algorithms, and artificial intelligence (AI), mobile robots can perform complex tasks such as exploration, transportation, and surveillance.

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The Building Blocks of Mobile Robots

Mobile robots rely on a combination of hardware, software, and advanced algorithms to navigate, perceive, and interact with their environment. This section explores the key technical aspects that enable mobile robots to operate autonomously and perform complex tasks efficiently.

Sensing and Perception

Sensing and perception allow mobile robots to gather and interpret data from their surroundings. One of the prominent technologies employed is light detection and ranging (LIDAR), which utilizes laser beams to measure the time it takes for the light to reflect back, enabling the construction of a 3D map of the environment. LIDAR systems, with their 360-degree field of view, are highly effective for obstacle detection and distance measurement. This is critical in dynamic environments, where constant adjustments are needed for navigation.¹

Mobile robots also use cameras and stereo-vision systems to capture visual information. Paired with computer vision algorithms, robots can recognize objects, track movement, and even analyze visual changes over time. RGB-D cameras, which combine standard RGB imaging with depth information, provide further detail about object shapes and sizes, enhancing spatial awareness.¹

Localization and Mapping

Localization is the process by which a robot determines its position within an environment, while mapping involves creating a representation of that environment. The leading technology that can accomplish these tasks concurrently is simultaneous localization and mapping (SLAM).

SLAM uses sensor data from LIDAR, cameras, and inertial measurement units (IMUs) to generate real-time maps of the robot's surroundings while continuously updating the robot's location within that map. However, managing sensor noise and errors is a significant challenge in SLAM, often handled using extended Kalman filters (EKF) or particle filters, which mitigate uncertainty in sensor data.^1,2

To improve robustness, techniques like loop closure detection are employed, where the robot recognizes previously visited areas and corrects its map accordingly. This process is essential for reducing cumulative localization errors, particularly in large or complex environments. Advances in graph-based SLAM further optimize this process by minimizing the computational load, making it viable for real-time applications.^1,2

Path Planning and Navigation

Once localized and mapped, a robot must find the optimal path to reach its destination while avoiding obstacles. Path planning algorithms calculate the most efficient route based on environmental data. Classical algorithms like A^* and Dijkstra work well in structured environments, evaluating costs to find the shortest path. However, these are less suitable for dynamic environments where obstacles move or change.³

For more complex scenarios, rapidly exploring random trees (RRTs) and probabilistic roadmaps (PRMs) are used to explore the environment and generate feasible paths in real-time. These algorithms excel in unstructured or cluttered environments as they dynamically adapt to changing conditions.³

In dynamic settings, the dynamic window approach (DWA) is used for real-time navigation and collision avoidance. The DWA continuously recalculates possible paths based on sensor data, allowing the robot to make quick adjustments.

In outdoor and rough terrains, robots use adaptive path planning, where they adjust their speed and movement based on terrain type. For example, legged robots modify their gait to navigate uneven surfaces, while wheeled robots slow down in rough conditions.³

Control Systems and Actuation

Control systems ensure that mobile robots follow their planned paths with precision. These systems are divided into low-level control, which governs the robot’s mechanical actuators, and high-level control, which handles decision-making and task execution.⁴

In low-level control, Proportional-Integral-Derivative (PID) controllers are commonly used to regulate movement by adjusting motor speed and direction. PID controllers operate by minimizing the discrepancy between the robot's actual position and the desired position, thereby facilitating smooth and accurate movements. These controllers are especially important for maintaining stability in complex tasks such as object manipulation or navigating uneven surfaces.⁴

For high-level control, Model Predictive Control (MPC) is a widely used technique. MPC optimizes the robot's movements by predicting future states and adjusting control actions in real-time. This strategy proves particularly effective in dynamic environments, where the robot must continually adapt its behavior to changing surroundings.⁴

Robots also employ various types of actuators, including electric motors, hydraulic systems, and pneumatic actuators, to generate movement. Advanced systems may incorporate multi-degree-of-freedom actuators, enabling robots to perform tasks demanding fine motor control, like grasping objects or manipulating tools. These actuators are crucial for robots operating in complex environments or performing intricate tasks like surgery or maintenance.⁴

Power Systems

Mobile robots rely on efficient power systems to sustain their operations, with lithium-ion batteries being the most common power source due to their high energy density and long lifespan. Nonetheless, energy consumption remains a crucial challenge, especially for robots that necessitate continuous operation over prolonged durations.

Robots engaged in energy-intensive tasks, such as heavy-duty transportation or prolonged navigation, often face limited operational time due to battery constraints.⁵ In addition to conventional batteries, some mobile robots utilize fuel cells or solar power as alternative energy sources. To manage energy consumption efficiently, battery management systems (BMS) are integrated into mobile robots. These systems monitor battery health, temperature, and charge cycles, optimizing power usage and extending battery life.⁵

Robot Design and Architecture: Key Principles and Practices

Applications of Mobile Robots

Mobile robots have diverse applications across multiple industries. Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) are widely used in industrial settings to transport goods, optimize inventory management, and reduce labor costs. Companies like Amazon and DHL heavily utilize mobile robots for logistics and fulfillment.⁶

Additionally, mobile robots assist in tasks such as patient transport, medication delivery, and even surgery. For instance, robotic systems like the da Vinci Surgical System enable remote, precise surgical procedures. Autonomous systems like drones also monitor crops and livestock and perform soil analysis, planting, irrigation, and harvesting.⁷

Mobile robots play a crucial role in space exploration, underwater research, and environmental monitoring. Mobile robots are also used for surveillance, reconnaissance, and bomb disposal. Their autonomy and durability allow them to operate in hazardous environments where human intervention is too risky.⁸

Latest Developments in Mobile Robots

Recent breakthroughs in mobile robotics have focused on improving autonomy, agility, and adaptability. A recent study published in Electronics explored self-assembly and self-repair in modular robots, highlighting how multiple robot modules can autonomously form larger structures and repair damaged components while in motion.

Unlike previous methods, this dynamic approach allows continuous task completion, even during repairs, enhancing reliability and efficiency. Tested on physical hardware, this approach demonstrated the practicality of performing self-repair and assembly without interrupting group tasks, showing potential for applications like disaster recovery, space exploration, and infrastructure monitoring, where robots must adapt to unpredictable environments.⁹

Another breakthrough research published in Advanced Intelligent Systems introduced Connect-R, a modular robotic system designed to operate in extreme environments like nuclear facilities. This system self-assembles into cubic structures, enabling other robots to perform complex tasks in hazardous settings.

Connect-R addresses the challenge of informational and physical uncertainty in nuclear zones by providing a simplified, structured environment for robots to navigate. By maximizing operational time in high-radiation environments, Connect-R significantly improves safety and efficiency in nuclear decommissioning and other high-risk applications​.¹⁰

Key Industry Players

Several key players are leading advancements in mobile robotics. Boston Dynamics is renowned for its legged robots used in logistics and inspection, while iRobot dominates the home robotics market with its autonomous cleaning systems. Clearpath Robotics specializes in industrial automation, offering autonomous material handling solutions, and Autonomous Solutions, Inc. (ASI) focuses on driverless technology for agriculture, mining, and military sectors.

What to Expect from the Robotics Industry by 2030

Future Prospects and Conclusion

The future of mobile robots is on the brink of rapid change, driven by advances in AI, machine learning, and more energy-efficient technologies. With greater autonomy and real-time decision-making made possible by integration into communication networks, these robots will soon take on bigger roles in industries like logistics, healthcare, and autonomous transportation, reshaping how these sectors operate worldwide.

In conclusion, mobile robots have come a long way from being simple machines. Today, they are complex systems capable of performing intricate tasks in various environments. Thanks to breakthroughs in AI, sensors, and control systems, they are transforming industries from manufacturing to healthcare. However, obstacles like energy efficiency and navigation still need to be addressed. As the field grows, mobile robots will become an even more important part of our daily lives, making automation smarter and more adaptive.

References and Further Reading

1. Zhu, J. et al. (2024). Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey. Tsinghua Science and Technology, 29(2), 415–429. DOI:10.26599/tst.2023.9010010. https://ieeexplore.ieee.org/abstract/document/10258154
2. Panigrahi, P. K. et al. (2022). Localization strategies for autonomous mobile robots: A review. Journal of King Saud University - Computer and Information Sciences, 34(8), 6019-6039. DOI:10.1016/j.jksuci.2021.02.015. https://www.sciencedirect.com/science/article/pii/S1319157821000550
3. Liu, L. et al. (2023). Path planning techniques for mobile robots: Review and prospect. Expert Systems With Applications, 227, 120254. DOI:10.1016/j.eswa.2023.120254. https://www.sciencedirect.com/science/article/pii/S095741742300756X
4. Abdallaoui, S. et al. (2023). Comparative Study of MPC and PID Controllers in Autonomous Vehicle Application. Mechanisms and Machine Science, vol 121. Springer, Cham. DOI:10.1007/978-3-031-09909-0_10. https://link.springer.com/chapter/10.1007/978-3-031-09909-0_10
5. Mikołajczyk, T. et al. (2022). Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency. Applied Sciences, 13(13), 7547. DOI:10.3390/app13137547. https://www.mdpi.com/2076-3417/13/13/7547
6. Plakantara, S.P. et al. (2024). Managing Risks in Smart Warehouses from the Perspective of Industry 4.0. Springer Optimization and Its Applications, vol 214. Springer, Cham. DOI:10.1007/978-3-031-58919-5_1. https://link.springer.com/chapter/10.1007/978-3-031-58919-5_1
7. Omisore, O. M. et al. (2020). A Review on Flexible Robotic Systems for Minimally Invasive Surgery. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1–14. DOI:10.1109/tsmc.2020.3026174 https://ieeexplore.ieee.org/abstract/document/9226520
8. Raj, R. et al. (2021). A Comprehensive Study of Mobile Robot: History, Developments, Applications, and Future Research Perspectives. Applied Sciences, 12(14), 6951. DOI:10.3390/app12146951. https://www.mdpi.com/2076-3417/12/14/6951
9. Peck, R. H. et al. (2021). Self-Assembly and Self-Repair during Motion with Modular Robots. Electronics, 11(10), 1595. DOI:10.3390/electronics11101595. https://www.mdpi.com/2079-9292/11/10/1595
10. Sayed, M. E. et al. (2022). Modular Robots for Enabling Operations in Unstructured Extreme Environments. Advanced Intelligent Systems, 4(5), 2000227. DOI:10.1002/aisy.202000227. https://onlinelibrary.wiley.com/doi/full/10.1002/aisy.202000227

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