LanderPi: AI Models + SLAM Navigation + 3D Vision in One Robot

At its core, Hiwonder LanderPi is powered by the Raspberry Pi 5, combined with high-end components like TOF LiDAR, a 3D depth camera, a 6 DOF robot arm, and an AI voice interaction system, creating a complete loop of perception, decision-making, and action. This hardware setup lays the foundation for handling complex tasks like never before.

On the software and algorithm side, LanderPi runs on the robust ROS2 robot operating system, seamlessly integrating advanced visual algorithms like YOLOv11 for real-time object recognition. It also uses MoveIt for precise motion planning and control of its robotic arm. What sets it apart is the deployment of OpenAI’s multimodal large AI models, enabling the robot to not only hear and see, but also think and act.

Let’s move beyond the tech talk and look at a more relatable scenario. Picture yourself in a smart community, giving LanderPi a few simple commands: “LanderPi, pick up the waste block and take it to the recycling station. Then head to the supermarket and check out the fruit. Next, swing by the garden to see if the dog’s around. Finally, go to the delivery station, bring my red package back home.”

Seems pretty casual and complex, right? In the past, this could’ve easily caused a robot to freeze. But what would LanderPi do in this situation?

First comes the deep understanding and breakdown of the task. When LanderPi receives this complex, everyday command through its AI voice interaction module, its built-in language model kicks into action. Rather than simply matching keywords, it delves into the semantic meaning of the instructions: identifying the core actions such as picking up, placing, patrolling, and transporting, the target objects like the waste block, fruit, dog, and red package, as well as a series of locations including the recycling station, supermarket, garden, delivery station, and home. This process is like giving the robot a super brain capable of understanding vague human intentions.

Next comes autonomous environmental perception and task execution. Once the overall task is understood, the robot integrates its technologies to get to work:

The robot activates its LiDAR to build or call upon an existing high-precision map of the environment, whether known or unknown. When it needs to travel from point A to point B, for example, from its current location to the waste block, and then to the recycling station, its navigation system combines global planning algorithms like A* with local obstacle avoidance algorithms such as TEB. This allows it to generate the optimal path and dynamically avoid any pedestrians or vehicles that suddenly appear, ensuring stable and flexible multi-point navigation.

Once LanderPi reaches the designated area, it moves into the action phase. For example, the task of finding a waste block in a cluttered space. The robot uses its 3D depth camera to scan the environment and identify the target. Through advanced point cloud processing and algorithms like YOLOv8, it accurately locates the block by analyzing its color, shape, and depth. The robot arm, guided by MoveIt and inverse kinematics, then calculates the best path for grasping the object, adjusting the gripper’s angle as needed to securely pick it up. This seamless hand-eye coordination between perception and action demonstrates LanderPi’s precision and agility.

When tasked with more cognitively demanding actions like "go to the grocery store and check out the fruit" or "go to the garden and see if the dog is around," LanderPi's capabilities are taken to the next level. It's not just about moving to a specific location—its visual AI model continuously analyzes the camera feed, deeply understanding the context of the scene. In the Store, it can identify and list items like apples, bananas, oranges. In the garden, it can determine whether "the dog is present." This deep understanding of the environment, objects, and events transforms LanderPi from a machine following preset instructions into an intelligent agent that actively observes and reports on its surroundings.

Finally, at the drop-off point, LanderPi must use its 3D vision to accurately identify the red package from a pile of parcels. Once located, it grabs the package with the robot arm and, through autonomous navigation, delivers it back to the home, completing the entire task loop.

To sum up, what makes LanderPi robot truly stand out is its ability to seamlessly integrate precise low-level motion control such as SLAM navigation and robot arm operations, powerful mid-level perception technologies like 3D vision and object recognition, and high-level cognitive intelligence including AI model-based semantic understanding and task planning into one cohesive system. It's no longer just a fragmented demo model from a lab, but a fully embodied intelligent system that understands natural human language commands and autonomously completes a series of complex tasks in real-time, ever-changing environments! Don't forget to follow Hiwonder Github and share with your friends who love AI learning.