Automated Guided Vehicles (AGV) are becoming increasingly important in modern warehouses to cope with the enormous logistic demands of developing e-commerce and the growing operational costs. The key component of implementing such a system is planning the paths of a large horde of AGVs to deliver orders from shelves to packing locations. The existing solutions regard it as a Multi Agent PathFinding (MAPF) problem, but they can hardly be applied in practice because none of them could satisfy the continuous (temporal), lifelong (future task unknown and keeps appearing), kinematic (acceleration/deceleration/rotation), online (fast response), and scalability (large network, large AGV number, large task number) at the same time. Therefore, we first propose an AGV routing framework that can satisfy all these properties with its corresponding routing algorithm. Then, to improve the efficiency, we propose the Multi-Hop Conflict-Aware Search method (MHCAS) with action combination, MHSC to reduce the search space, and OHSMD to decompose motions such that routing time is reduced by three orders of magnitude. Extensive experimental studies verify the superiority of our methods compared with the state-of-the-art.

Deadlock is always a challenging problem for multi-agent pathfinding, especially when the system is in high scales in terms of number of agents and map size. Some recent studies showed that the agents can learn to resolve the deadlock problem through reinforcement learning. However, most of them are not designed for non-holonomic robots, which are commonly applied in warehouses. In particular, the rotation movement may cause the agents staying at the same locations for a long time, and the deadlock happens more frequently especially in dense environment. In this paper, an algorithm called MAPF-rot with a deadlock breaking scheme is proposed to tackle the deadlock problem arising from the rotation movement in the multi-agent pathfinding problem. Experiments are performed to demonstrate the efficiency of the proposed algorithm.

Soft biometric traits such as skin color, tattoos, shoe size, height, and weight have been regularly used for forensic investigation , especially when hard biometric traits , e.g., faces and fingerprints are not available. Recently, a new soft biometric trait, androgenic hair also called body hair, was evaluated. The previous study showed that low resolution androgenic hair patterns have potential for forensic investigation . However, it was believed that they are not a distinctive biometric trait because of the reported accuracy. To explore discriminative information in androgenic hair patterns, in this paper, a new algorithm, which makes use of leg geometry to align lower leg images, large feature sets (about 60,000 features) extracted through multi-directional grid systems to increase discriminative power and robustness, and class-specific partial least squares (PLS) models to utilize the features effectively, is employed. To further enhance the performance of the class-specific PLS models trained on very limited positive samples, one to three images per model in the experiments, and further enhance robustness against viewpoint and pose variations, a scheme is designed to generate more positive samples from a single image. Experimental results on 1493 low resolution leg images with large viewpoint and pose variations from 412 legs demonstrate that low resolution androgenic hair patterns contain rich information and the impression of low discriminative power on androgenic hair is due to the method used in the previous study.

Skin texture without obvious features is different from other hard biometrics on the skin, such as fingerprints and palmprints. Skin texture gives an impression that it is not distinctive like other soft biometric traits. It was proposed for personal identification a decade ago but did not draw attention from the biometric community, partially due to the success of other biometric technologies for commercial applications. However, in some forensic cases, e.g., identifying masked terrorists in images, skin texture may be the only option. Faces, tattoos, and skin marks are not always available for identification. To address these forensic needs, researchers have recently attempted to visualize blood vessels hidden in color images. Their performance is highly sensitive to image quality. Skin texture that is easily captured even in low-resolution images, such as that of the forearm skin, is suitable for these forensic applications. To study the distinctiveness of low-resolution skin texture, in this paper, an algorithm composed of a positive sample generation scheme, dynamic and directional grids, a large feature set generation scheme, and partial least squares regression has been proposed. More than 6300 inner forearm and thigh images collected from a laboratory environment and from the internet with large pose, viewpoint, and illumination variations were employed in this paper. The proposed algorithm was compared with the state-of-the-art texture recognition methods, and skin texture was compared with blood vessels, a hard biometric trait, extracted from color and infrared images. The results showed that the proposed algorithm performed significantly better than did the texture recognition methods, and skin texture outperformed blood vessels in all of the experiments, achieving encouraging performance.

Identifying criminals and victims in digital media can be very challenging when neither their faces nor tattoos are observable. These criminals and victims can be masked gunmen, paedophiles, and victims in child pornographic images. Though skin marks and blood vessel patterns hidden in color images have been proposed to address this problem, they have different limitations. Blood vessel patterns are not suitable for subjects with high concentration of body fat or melanin and skin marks are not suitable for the cases where only low resolution images are available. A recent paper pointed out that androgenic hair, which is also called body hair, and its follicles can be used as biometric traits and demonstrated that androgenic hair patterns in low resolution images are effective for personal identification. In that study, viewpoint and pose variations were ignored, but these variations are unavoidable in real cases. To overcome the weaknesses of the previous method, this paper proposes an algorithm based on geometric information for aligning androgenic hair patterns on lower legs. The experimental results on 1,138 low resolution images from 283 different legs show that the proposed alignment algorithm offers more than 5% improvement.

Criminal and victim identification is always vital in forensic investigation. Many biometric traits, such as DNA, fingerprint, face and palm print, have been regularly used by law enforcement agencies. However, they are not applicable to legal cases where only non-facial body sites of criminals or victims in evidence images are available for identification. These cases include but are not limited to violent protests, masked gunmen and child pornography. To address this challenging identification problem, skin marks, blood vessels hidden in color images, androgenic hair patterns and tattoos have been considered. Tattoos are not always available. Skin marks and blood vessels are suitable for high resolution images. Androgenic hair patterns provide useful identification information even in low resolution images, but their performance is still far from perfect. Thus, new biometric traits are still demanded especially for low resolution evidence images. This paper evaluates lower leg geometry as a soft biometric trait for criminal and victim identification. Lower legs are considered in this study because they are often observable in evidence images. The algorithm utilized in this evaluation first aligns two lower leg shapes from input images and extracts geometric features, including the partial sum of squared difference, the polynomial coefficients and the number of intersection points of the aligned leg shapes. Support vector machines, neural networks and decision trees are used to perform the classification. The algorithm is applied to 1,138 images from 283 subjects. The experimental results indicate that lower leg geometry is an effective soft biometric trait. This study provides a foundation for further research on criminal and victim identification based on body geometry.