Perceptive • Clever • Sleek
Geek+ multiple-model picking robots, with their maximum load of 1200kg and their ability to reduce 50-70% of manual labor and increase picking accuracy to 99.99%, is the preferred choice for businesses looking to reduce costs and raise efficiency using automated warehouse robots.
Geek+ sorting robots are highly efficient, automated and flexible, achieving nearly 10 times the efficiency of traditional sorting systems; their multi-sensor safety obstacle avoidance features also ensure the safety of on-site implementation.
Geek+ RS- Series robots can improve picking efficiency by 2 to 3 times on average and they are flexible in their implementation, allowing for the desired addition or removal of robots according to customer requirements; they require little to no modification to project site and boast a short ROI cycle.
Geek+ M-Series moving robots have a maximum carrying load of 1000kg, and are supported by laser SLAM, QR code navigation and other navigation methods to ensure smooth, accurate and safe robot operations.
Geek+ Smart Forklift Robots save up to 50-80% in manpower costs and are fully functional even in temperatures of negative 10 to 40 degrees; they have an average run time of 6-8 hours and are supported by safety LiDAR and 3D visualization to enable obstacle detection, achieving high safety standards.
The collaborative picking robot can reduce the picking path distance and increase picking efficiency and accuracy, saving manpower by 30%-50%. With SLAM navigation, the robot is safe, flexible and man-robot collaborative.
Technologies like LiDAR (Light Detection and Ranging), visual SLAM (Simultaneous Localization and Mapping), 2D code systems and inertial navigation provide robots with reliable positioning and navigation references through feature maps, without the need for intrusive infrastructure.
• Fast deployment
• Uses existing environment features for map construction
• Highly adaptable to dynamic human/machine manufacturing environment
• High reliability of 2D codes and inertial navigation
The fusion of multi-sensor information such as RGBD camera, LiDAR, and safety sensors, combined with deep learning algorithms to achieve accurate detection of the target and understanding of the environment, enhance the autonomous mobile robot’s adaptability, robustness and operational safety to complex environments.
• Multi sensor fusion, which can adapt to complex human-machine mixed scenarios and has good security
• Use of RGB and 3D cameras to identify small and light obstacles
• Deep learning combined with traditional CV technology for high robustness
The motion control parameters adaptive algorithm, based on the high-efficiency control model, realizes autonomous and efficient movements of different types of intelligent robots (including autonomous mobile robots and automated guided vehicles) and is equipped with soft and agile start-stop, high-speed and stable operation.
• Motion parameters of different robots are adaptive
• Motion safety decisions to avoid collisions
• Smooth trajectory planning: flexible acceleration and deceleration planning of target trajectory are accurately tracked by multi-type controllers to ensure smooth operation