The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on highprecision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available11https://ei-nav.github.io/OpenBench/.

Large-scale outdoor navigation is essential for unmanned ground vehicles (UGVs), but despite significant advancements, they still face two key challenges in practical applications. The first one is how to ensure safe navigation in environments with dynamic and low-lying obstacles that LiDAR cannot detect. The second one is how to conduct the adaptive re-planning of target points while some of them are blocked by temporary obstacles. To address these challenges, this work proposes a Dynamic and Low-lying-obstacle Avoidance Navigation (DLAN) system to conduct perception, planning, and point correction for UGVs. To efficiently and accurately detect dynamic obstacles, it designs a lightweight Ensemble3D framework that integrates three fast but low-accuracy detection methods. A multi-criteria waypoint optimizer is used to assist UGVs in path planning. It ensures a balance between obstacle avoidance and path following. To adjust blocked target points through local re-planning, this work designs a checkpoint correction method. Extensive simulations and real-world experiments demonstrate that DLAN enables reliable navigation with high efficiency and robust obstacle avoidance in complex environments. More details can be found on our project homepage and video1.

The growing demand for efficient last-mile delivery highlights the need for autonomous robots to improve operational efficiency and reduce costs. Traditional navigation methods rely on high-precision maps, which are expensive to produce and maintain, while learning-based approaches often struggle to adapt to diverse real-world environments. To address these challenges, this paper presents OpenStreetMap-enhanced oPen-air sEmantic Navigation (OPEN), a system that combines foundation models with classic navigation algorithms to enable scalable outdoor navigation. By leveraging off-the-shelf OpenStreetMap (OSM), OPEN eliminates the need for extensive pre-mapping and provides a lightweight, readily available map representation. The system uses Large Language Models (LLMs) to interpret delivery instructions and Vision Language Models (VLMs) for global localization without relying on GPS, real-time map updates, and entrance recognition, ensuring robust navigation in complex environments. To further enhance adaptability, OPEN incorporates a local replanning method that dynamically adjusts waypoints in response to environmental changes and OSM inaccuracies. Since existing benchmarks do not adequately reflect the challenges of last-mile delivery, this work introduces a new benchmark designed for residential navigation. Experiments conducted in both simulated and real-world settings demonstrate that OPEN improves navigation accuracy, efficiency, and reliability, outperforming existing semantic navigation methods. To facilitate further research, the code and benchmark are publicly available.

In narrow environments with dynamic dense obstacles, it is difficult to find feasible paths for autonomous ground vehicles (AGVs) by using existing methods due to the strict security constraints on AGV movements. To overcome such difficulty, this work proposes an improved local path tracking algorithm based on model predictive control and a dynamic control barrier function with slack variable (MPC-DS). In this algorithm, slack variable is integrated with the control barrier function to convert the strict constraints to soft ones. To regulate the values of slack variable, a suitable penalty coefficient selected by using a series of comparative simulations is incorporated into MPC’s cost function. To test effectiveness of the proposed method, it is compared with three mainstream methods in environments with dense and sparse dynamic obstacles. Results of simulations and physical experiments show that AGV controlled by the proposed algorithm can avoid obstacles safely and efficiently in complex environments. It is worth noting that its use reduces energy consumption by 21.1% in comparison with the existing ones. To facilitate further research, our code is publicly available at https://github.com/BUCT-RobotLab417/MPC-DS

The detection and localization of objects are critical for inspection robots. Camera images facilitate rapid object detection but are limited in providing accurate object localization. Conversely, LiDAR point clouds can provide precise object localization but lack semantic information. To solve this problem, this work proposes a multi-sensor data fusion-based method for object detection and localization. The rich semantic information of camera images and the precise location of LiDAR point clouds are combined. An R-tree spatial indexing algorithm is used to match and process data swiftly. Accurate object localization can be achieved by using the provided method based on the fused data. Moreover, the method leverages point cloud data to refine confidence, significantly reducing the miss rate of detection in glaring conditions. Simulations and experiments are conducted to compare the proposed method with other localization algorithms. The results show that it significantly outperforms them in terms of accuracy.