Necessity and Challenges of Dynamic Obstacle Perception Algorithms in Robotics

The Necessity of Dynamic Obstacle Perception Algorithms

In dynamic and unpredictable environments, robots encounter a wide range of moving entities, including pedestrians, vehicles, and other robots. To navigate safely and efficiently, robots must not only detect these dynamic obstacles but also anticipate their future movements. Without dynamic obstacle perception, robots would treat moving objects as static obstacles, leading to potentially hazardous collisions. Therefore, the ability to perceive and predict the behavior of dynamic obstacles is essential for ensuring the reliability and safety of service robots in various real-world applications.

Dynamic obstacle perception is indispensable for safe and efficient robotic navigation in dynamic environments. However, implementing algorithms for this purpose is a multifaceted challenge, encompassing real-time processing, data fusion, object tracking, uncertainty handling, semantic understanding, scalability, adaptability, and robustness. Addressing these challenges is essential to advance the capabilities of service robots and facilitate their seamless integration into complex, real-world scenarios.

Dynamic Perception Algorithms for PUDU Robots

In the context of PUDU robots, two dynamic perception algorithms play a pivotal role in enabling safe and efficient navigation in dynamic environments:

1）Deep Learning for RGBD

This algorithm leverages advanced deep learning techniques, particularly Convolutional Neural Networks (CNNs), to train a model capable of robust object detection and classification. By processing RGBD data, the algorithm identifies and classifies various objects in the robot's environment. This is achieved by detecting the object's position and bounding boxes in the RGBD images. The deep learning model is trained on a diverse dataset to ensure accurate and reliable object recognition.

2）Single-Line Radar and RGBD Fusion with Random Forest

This algorithm combines data from a single-line radar with RGBD information. It utilizes the Random Forest method to filter out radar point cloud clusters resembling human legs, as well as RGBD point cloud clusters. By assessing the confidence levels of these clusters, the algorithm conducts a post-fusion process to extract pedestrians. It then employs a Kalman filter to track these pedestrians across multiple frames, calculating their direction and speed of movement. Using this information, the algorithm predicts the future trajectory of pedestrians, enabling the robot to anticipate their movements in the upcoming period.

Both of these dynamic perception algorithms have been successfully implemented across the entire range of PUDU robots, enhancing their capabilities in perceiving and interacting with dynamic environments. These algorithms represent significant strides in the field of robotics, ensuring safe and efficient navigation in complex, real-world scenarios.

About Pudu Robotics

Pudu Robotics is a global leader in design, R&D, production, and sales of commercial service robots with nearly 70,000 units shipped in over 60 countries worldwide. The company’s robots are currently in use across a wide variety of industries including restaurants, retail, hospitality, healthcare, entertainment, and manufacturing. Founded in 2016 and headquartered in Shenzhen, China, its mission is to use robots to improve the efficiency of human production and living. For more information on business developments and updates, follow PUDU on Facebook, YouTube, LinkedIn, Twitter and Instagram.