Limo
Multi-modal mobile robotics development platform
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The Limo is an advanced mobile robotics development platform designed for autonomous driving research and education. It features four steering modes (Ackermann, differential, omnidirectional, and tracked) that can be switched mechanically, making it highly versatile for testing various navigation algorithms. Targeted at universities, research institutions, and robotics developers, it provides a comprehensive solution with integrated sensors and full ROS support.
Released: 2020
RoboZaps evaluation note
Buyer-side view of Limo
RoboZaps reviews Limo as part of a broader robot shortlist, not as a single-vendor pitch. Buyers should compare availability, deployment maturity, support terms, integration work, and total cost before committing.
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Best fit
Teams that need to compare Limo against othermobile robots options with deployment readiness, commercial terms, and support assumptions made explicit.
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Buyers who need a guaranteed off-the-shelf deployment before confirming current availability, integrator coverage, site readiness, and post-sale support ownership.
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Overview
The AgileX Limo represents a breakthrough in mobile robotics education and research by combining four distinct steering modes in a single compact platform. This unique capability allows researchers and students to experiment with Ackermann steering (like a car), differential drive (like traditional robots), omnidirectional movement (allowing lateral motion), and tracked locomotion (for enhanced traction) - all mechanically switchable without software reconfiguration.
Designed with both educational institutions and professional researchers in mind, the Limo comes fully equipped with essential sensors including a 360-degree LiDAR, depth camera, and IMU. The platform's modular design and comprehensive ROS integration make it an ideal testbed for developing and validating autonomous navigation algorithms across different motion paradigms.
With its compact footprint measuring 460mm in length and weighing 22kg, the Limo is portable enough for classroom demonstrations yet robust enough for serious research applications. The platform's open architecture and extensive SDK support enable users to quickly prototype and deploy custom autonomous driving solutions.
Key Features
- Four-in-One Steering Modes: Mechanically switchable between Ackermann, differential, omnidirectional, and tracked configurations
- Comprehensive Sensor Suite: Integrated 360° LiDAR, depth camera, and IMU for full environmental perception
- Full ROS Support: Native ROS/ROS2 compatibility with complete software packages and simulation environments
- Modular Design: Expandable platform with multiple mounting points for custom sensors and payloads up to 10kg
- Extended Runtime: 120-minute battery life enables extended testing and demonstration sessions
- Industrial-Grade Construction: Durable aluminum alloy chassis designed for repeated use in educational and research settings
Applications
The Limo platform serves as an essential tool in robotics education, providing universities and research institutions with a hands-on platform for teaching autonomous navigation, SLAM algorithms, path planning, and multi-modal locomotion strategies. Its ability to switch between different steering modes makes it invaluable for comparative studies in mobile robotics, allowing students to understand the trade-offs between different locomotion approaches.
In research and development contexts, the Limo is widely used for prototyping autonomous delivery systems, indoor navigation solutions, and warehouse automation concepts. The platform's compact size and sensor array make it suitable for testing algorithms in constrained environments, while its ROS compatibility ensures seamless integration with existing robotics software ecosystems and simulation tools like Gazebo.
Technical Highlights
The Limo's standout innovation is its mechanical steering mode switching system, which allows users to physically reconfigure the robot's drivetrain to achieve four distinct locomotion patterns. This mechanical approach ensures authentic performance characteristics for each mode, unlike purely software-based simulations, providing researchers with real-world data on how different steering geometries affect navigation performance and energy efficiency.
The platform's sensor fusion capabilities combine data from the 360-degree LiDAR, depth camera, and IMU to provide robust localization and mapping capabilities. The complete ROS integration includes pre-configured packages for navigation, SLAM, and autonomous driving, significantly reducing the setup time for new projects. The open SDK and well-documented APIs enable developers to access low-level control while also providing high-level interfaces for rapid prototyping.
Videos
Specifications
Connectivity
| Communication | WiFi, Ethernet |
Mechanics
| Locomotion | Wheeled |
Performance
| Max Speed | 1.5 m/s |
| Payload Capacity | 4 kg |
Physical
| Weight | 4.8 kg |
| Height | 220 mm |
Power
| Battery Life | 2 hours |
Sensing
| Sensors | LiDAR, depth camera, IMU |
Software
| SDK Support | Yes |
| ROS Compatible | Yes |
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