Jueying Lite

Name: Jueying Lite
Brand: Deep Robotics

Affordable Quadruped Robot for Education and Research

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Jueying Lite is a compact quadruped robot developed by Deep Robotics for educational institutions, research labs, and developers. It features 12 degrees of freedom, robust locomotion capabilities, and comprehensive SDK support for ROS-based development. Designed as an accessible entry point into legged robotics, it offers impressive mobility and payload capacity at a more economical price point than industrial models.

Released: 2020

Overview

The Jueying Lite represents Deep Robotics' commitment to making advanced quadruped robotics accessible to educational institutions and research laboratories worldwide. As a lighter, more affordable version of the company's flagship Jueying series, the Lite model maintains the core locomotion capabilities that have made Deep Robotics a leader in legged robotics while reducing complexity and cost for academic applications.

Built on Deep Robotics' proven motion control algorithms and mechanical design principles, Jueying Lite features 12 degrees of freedom distributed across four legs, enabling dynamic gaits including trotting, walking, and basic jumping. The robot's compact form factor and 32.7 kg weight make it manageable for laboratory environments while still offering a 14 kg payload capacity for sensors, computing units, and experimental equipment.

Designed specifically for the research and education market, Jueying Lite includes comprehensive software development tools, ROS compatibility, and detailed documentation to support teaching and experimentation. The platform allows students and researchers to explore locomotion control, SLAM algorithms, autonomous navigation, and multi-robot coordination without the high costs associated with industrial-grade quadruped systems.

Key Features

12 DOF Actuation: Three actuators per leg providing natural quadruped locomotion and dynamic gait control
14 kg Payload Capacity: Sufficient capacity for mounting cameras, LiDAR, computing hardware, and experimental payloads
90-Minute Runtime: Extended battery life supports full research sessions and outdoor demonstrations
IP54 Protection: Dust and splash resistance enables outdoor testing and varied environmental conditions
ROS Integration: Native ROS support with SDK and API for rapid prototyping and algorithm development
Multiple Locomotion Modes: Walk, trot, and stair climbing capabilities with adjustable gait parameters
Onboard Sensing: Integrated stereo cameras and IMU for perception and state estimation
Developer-Friendly Platform: Comprehensive documentation, simulation tools, and community support for education

Applications

Jueying Lite serves primarily educational and research institutions where students and researchers need hands-on experience with legged robotics. Universities use the platform for teaching courses in robotics, control theory, and artificial intelligence, while research labs employ it for developing and testing new locomotion algorithms, perception systems, and multi-agent coordination strategies. The robot's accessibility makes it ideal for undergraduate and graduate programs looking to provide practical experience with state-of-the-art robotic platforms.

Beyond academia, Jueying Lite also finds applications in lightweight inspection tasks, robotics competitions, and technology demonstrations. Its combination of mobility, payload capacity, and open development environment makes it suitable for prototype development and algorithm validation before scaling to industrial platforms. Research groups use it to explore human-robot interaction, outdoor navigation in natural terrain, and collaborative robotics scenarios.

Technical Highlights

Jueying Lite leverages Deep Robotics' advanced motion control algorithms that enable stable locomotion across varied terrain. The robot's proprietary controller manages coordination between all 12 actuators to maintain balance during dynamic movements, automatically adjusting gait parameters based on terrain conditions and commanded velocity. The system's force control capabilities allow for compliant interaction with the environment, essential for traversing obstacles and uneven surfaces.

The platform's software architecture emphasizes modularity and extensibility, with clear API boundaries between perception, planning, and control layers. This separation allows researchers to modify individual components without disrupting the entire system. The included simulation environment enables algorithm development and testing in virtual scenarios before deployment on hardware, accelerating the research cycle and reducing the risk of physical damage during experimental trials.