Hiwonder LanderPi: Multimodal AI Robot Takes Innovation to the Next Level!

The "Perception-Decision-Execution" loop of Hiwonder LanderPi begins with a solid hardware foundation: a Raspberry Pi 5 paired with an STM32 dual-core controller forms the computational backbone. Meanwhile, a 3D depth camera, AI voice interaction box, high-torque motor-driven robot arm, and TOF LiDAR collectively create a multisensory collaboration system, granting the AI robot full awareness of the physical world.

However, the advanced intelligent experience comes from the integration of embodied AI models. To achieve this, LanderPi raspi car integrates advanced embodied AI models and provides API access, enabling seamless integration with cutting-edge models like OpenAI. It also supports flexible switching between various models, such as ChatGPT. This architecture creates a "super brain" capable of deeply integrating visual, voice, and environmental data, endowing the product with unprecedented environmental perception and human robot interaction capabilities.

By combining the AI voice interaction box with multisensor data, LanderPi raspi robot goes beyond merely hearing commands — it understands intent, judges situations, and plans actions. This enables advanced embodied intelligence applications, such as intelligent navigation and transportation, accurate voice interaction, dynamic scene understanding, and adaptive color tracking.

Hiwonder LanderPi supports 3 types of chassis: Mecanum wheels, Ackermann chassis, and Tank chassis, catering to omnidirectional movement, precise turning, and complex terrain adaptation, respectively, enabling seamless operation across all scenarios.

Equipped with a high-performance MS200 LiDAR and integrating high-precision encoders and IMU data developed in-house, LanderPi car builds a complete intelligent navigation system. Through 360° panoramic scanning and SLAM mapping technology, it achieves centimeter-level environmental modeling and supports various navigation modes, including point-to-point navigation, multi-point continuous path navigation, and loop navigation.

LanderPi robot car integrates global planning algorithms such as A* and Dijkstra with local dynamic planning strategies like DWA and TEB, giving the robot real-time perception, dynamic obstacle avoidance, and path replanning capabilities in complex environments. Even in dynamic and challenging scenarios, it can handle navigation, transportation, and sorting tasks with stability and efficiency.

Traditional robotic arms often rely on preset action groups, leaving them helpless when dealing with irregularly placed objects. However, LanderPi revolutionizes robotic arm operation logic with its "deep vision + self-developed inverse kinematics algorithm": a 3D structured light depth camera captures real-time point cloud data of objects, accurately identifying the target's position, size, tilt angle, and 3D shape. Coupled with the fully self-developed inverse kinematics algorithm, simply calling a function allows the robotic arm's end effector to precisely reach any coordinate in 3D space.

LanderPi AI robot utilizes RTAB-VSLAM technology to achieve 3D visual mapping and navigation. The system integrates vision and radar data to create a 3D color map with rich semantic information, allowing the robot not only to see its surroundings clearly but also to comprehend its precise position in space. This capability, in deep collaboration with the robotic arm's operation, forms a complete "Perception-Mapping-Localization-Action" cycle.

This ability, combining precise coordination and seamless action, enables LanderPi to perform tasks with human-like flexibility. It can adjust its gripping posture in real-time when handling parts of varying heights or containers placed at an angle. In complex environments, it can perform precise operations while autonomously navigating and avoiding obstacles, overcoming the limitations of traditional robotic arms' rigid operations.

LanderPi ros2 robot offers more than only ease of use—it's an innovation platform that's simple to develop on. Built on the ROS2 framework, it provides a full-end development environment that ensures seamless integration from URDF model simulation to real-world deployment. You can simulate robotic arm motion planning in virtual space using MoveIt, and visualize the results with RViz, eliminating concerns about disconnection between simulation and actual deployment.

To greatly reduce the development barrier, we provide a full suite of open source learning materials, ranging from beginner to advanced levels, which includes in-depth tutorials and user manuals. You can quickly get started without getting bogged down in low-level debugging, allowing you to focus on functional innovation. For both educational training and project development, it stands as a truly "ready-to-use, infinitely expandable" hands-on platform. You can explore all open sources on our Hiwonder Github.