Why Obstacle Avoidance Is a Core Capability for Mobile Robots
As mobile robots are increasingly deployed in factories, warehouses, hotels, shopping malls, restaurants, and public spaces, their ability to move safely and efficiently has become a primary concern.
Among all motion-related capabilities, obstacle avoidance is one of the most critical. A robot must be able to perceive static and dynamic obstacles in real time, assess their spatial relationship, and adjust its motion accordingly to reach its target safely.
Effective obstacle avoidance is not a single algorithm or sensor feature—it is the result of environment perception, sensor selection, and system-level design.
Environmental Perception: The Foundation of Obstacle Avoidance
Obstacle avoidance and autonomous navigation both rely on one fundamental prerequisite: environment perception.
In unknown or partially known environments, robots must use onboard sensors to collect information such as:
This makes sensor technology a decisive factor in mobile robot obstacle avoidance performance.
Common Sensors Used for Robot Obstacle Avoidance
Modern mobile robots typically rely on a combination of the following sensors:
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Depth or RGB cameras
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Ultrasonic sensors
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Bump or contact sensors
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Cliff or drop sensors
Among these, LiDAR has become the most widely used primary sensor due to its accuracy, reliability, and independence from ambient lighting conditions.
However, LiDAR alone is often insufficient in complex real-world environments.
Why LiDAR Alone Is Not Always Enough
While LiDAR provides precise distance and geometric information, certain materials and scenarios remain challenging:
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Transparent or semi-transparent objects (e.g. glass doors)
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Highly reflective surfaces
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Small or irregularly shaped obstacles
To address these edge cases, many robots supplement LiDAR with additional low-cost sensors, most commonly ultrasonic sensors.
Ultrasonic sensors can detect transparent objects such as glass and mirrors at close range. They are inexpensive and easy to integrate, but they also have clear limitations:
As a result, ultrasonic sensors are best used as complementary sensors, rather than primary perception devices.
Is More Sensors Always Better?
A common misconception is that adding more sensors will automatically improve obstacle avoidance performance.
In practice, this is not the case.
An excessive or poorly designed sensor combination can:
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Increase system cost
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Complicate data processing
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Introduce sensor interference
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Degrade overall reliability
Each sensor type has its own error characteristics and noise model. For example, ultrasonic sensors typically provide much lower distance and angular accuracy than LiDAR. Without proper fusion, conflicting data may reduce rather than improve perception quality.
Key Factors in Multi-Sensor Fusion Design
To build an effective obstacle avoidance system, sensor selection and fusion must be carefully designed. Several constraints must be considered.
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Robot Form Factor and Mechanical Design
The physical design of the robot directly affects sensor choice. Compact service robots or humanoid educational robots often have limited space.
Installing a large or bulky sensor may negatively impact:
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Industrial design
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Weight distribution
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Mechanical stability
In such cases, compact, low-profile LiDAR sensors are often preferred.
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Suitability for the Operating Environment
Each sensor has specific operational limits.
For example:
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A LiDAR with a 10-meter range may be insufficient for large, open factory floors
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Vision-based sensors may struggle in low-light or dark environments
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Strong ambient light can degrade certain perception systems
Sensor specifications must be matched to real deployment scenarios, not just laboratory conditions.
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Cost and Scalability
Once technical requirements are met, cost becomes a decisive factor—especially for large-scale commercial deployment.
Historically, the high cost of LiDAR limited its use in mass-produced robots. This has driven two major industry trends:
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The development of low-cost, high-performance 2D LiDAR
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Increased research into vision-only navigation systems to reduce hardware cost
Today, affordable LiDAR solutions have made robust obstacle avoidance and navigation accessible to a much wider range of robot platforms.
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Multi-sensor Fusion Robot Platfrom
In practical robot deployment, sensor fusion is not only an algorithmic challenge, but also a system-level design problem that depends heavily on the robot platform itself.
The
Apollo 2.0 robot platform is designed as a multi-sensor fusion–ready mobile base, providing a stable hardware foundation for advanced perception and navigation. It features a
dual-LiDAR configuration, consisting of a primary LiDAR for global environment perception and mapping, and a secondary low-mounted LiDAR dedicated to detecting
low-profile obstacles that are easily missed by standard sensor layouts.
In addition, Apollo 2.0 integrates automotive-grade ultrasonic sensors and industrial-grade safety bumpers, enabling reliable detection of transparent objects, close-range obstacles, and emergency collision scenarios. By fusing data from LiDAR, ultrasonic sensing, and physical safety interfaces, the system achieves robust perception of both static and dynamic obstacles across complex environments.
This rich sensor fusion architecture allows Apollo 2.0 to support large-scale, high-precision mapping, while maintaining stable and reliable localization, navigation, and obstacle avoidance performance. As a result, it is well suited for service robots operating in factories, warehouses, commercial spaces, and other demanding real-world scenarios where environmental complexity and safety requirements are high.
From Algorithms to Real-World Reliability
With advances in computing power, sensor technology, and artificial intelligence, mobile robot obstacle avoidance and autonomous navigation have made significant progress.
However, application requirements have evolved. Robots are no longer expected to simply function—they must operate with:
No single sensor or algorithm can guarantee perfect obstacle avoidance in all environments. Overcoming the limitations of individual methods through careful system design and sensor fusion remains an active area of development.
Conclusion
Obstacle avoidance is a core capability that defines whether a mobile robot can operate safely and effectively in real environments.
Successful solutions depend not on maximizing sensor count, but on selecting the right sensors, understanding their limitations, and integrating them intelligently. LiDAR remains the backbone of most mobile robot obstacle avoidance systems, while complementary sensors enhance robustness in challenging scenarios.
As mobile robots continue to expand into more complex environments, obstacle avoidance technologies will continue to evolve—driven by real-world deployment experience rather than theoretical perfection.
How Mobile Robots Avoid Obstacles: Sensor Choices and Fusion Techniques
How Mobile Robots Avoid Obstacles: Sensor Choices and Fusion Techniques
