The increasing popularity of online shopping has resulted in a surge of parcels that need to be sorted, which exerts great challenges to the sorting work. Robotic grasp can greatly improve the sorting efficiency. However, the dynamic grasp of moving parcels requires higher detection speed and grasping pose calculation accuracy. To address these requirements, this study proposes a new grasping system through the Neuromorphic vision (NeuroIV), which owns the advantages of low latency and lightweight computing. As a young field, Neuromorphic camera is rarely used in robotic grasp. In view of this, we present a novel parcel-grasping dataset. After that, a double channels’ down-sampling and grasping network (DCDG-Net) is designed, which can extract abundant features with ResNet and transformer branches, respectively. To mitigate the calculation burden introduced by the dual channels' network, we design a feature-vector multiplication to replace the dot-product multiplication, thereby reducing the computational load among different matrixes. Furthermore, channel and space attentions are fused to construct multidimensional network to suppress noisy features and highlight useful information. Finally, we have evaluated the proposed method in real-world scenarios. Together with qualitative and quantitative comparisons, this work provides a state-of-the-art grasping detection with the new NeuroIV dataset and network.

With the increase of car's number, the problem of insufficient parking space in cities is becoming more and more prominent. The high-density parking lots using parking robots can greatly improve space utilization. However, there is a great collision risk in the parking lot's tight space navigation. So, precise LIDAR localization and trajectory tracking control are the key technologies of autonomous driving along a predetermined path. Due to the laser points’ sparse character, the LIDAR points’ extracted features have distribution errors. In order to further optimize the LIDAR's localization, this article establishes the feature's error ellipsoid model, and the model's information and error entropies are calculated separately. The error entropy is utilized to optimize the feature-matching weight and improve the localization accuracy. Based on the higher localization result, a state-of-the-art lateral tracking model is proposed to address the challenges faced by the long parking robot. Then, the adaptive fuzzy control is designed to achieve precise control of wheel motion. Finally, an experimental platform is built to compare the effectiveness of different positioning and tracking algorithms. The comparison results show that the integration of laser evaluation localization and lateral tracking optimization algorithm can provides a collision-free improvement of 50% in the parking lot's tight spaces.

Moving-objects detection is a critical ability for an autonomous vehicle. Facing the high detection requirements and the slow target-extraction problem of a common camera, this article proposes to utilize neuromorphic vision sensor (DVS) for detecting the moving objects and estimating their movement states. For a better detection work, the DVS image's noise points are filtered and the CTRV kinematics model is built in advance. In order to distinguish the overlapped or nearby bodies and get the accurate clustering number, this article proposes a 3-D improved K -means method. As the clustering centers can be influenced by the movement easily, the moving objects' clustering centers appear unstable, so the movement estimation also fluctuates. In order to obtain stable movement estimation, this article proposes a strong tracking center differential external Kalman filter (SCDEKF) to track the moving objects, and the method has higher accuracy and less computational load. In order to verify the advantages of proposed methods, a simulation environment was established in the Gazebo, and the common cameras were also added in simulation for comparison with the DVS sensor. The simulation results show that the 3-D improved K -means method with DVS can cluster the moving objects accurately, and the SCDEKF can provide more accurate movement estimation than the traditional EKF method. Finally, two experiments were conducted to prove the methods' superiority. The main contribution is to solve the exploitation problems faced by the short-term borne sensor and promote its application in transportation.