The occurring growth in e-commerce comes along with an increasing number of first-time delivery failures due to the customer’s absence at the delivery location. Failed deliveries result in rework, causing a significant impact on the carriers’ delivery cost. Hence, the last mile is the portion of a journey that involves moving people and commodities from a transportation hub to a final destination, which should be an efficient process. The above-mentioned concept is used in supply chain management and transportation planning. The paper at hand is a position paper that aims to scrutinize the concept of driverless last-mile delivery, with autonomous vehicles, in order to highlight and stress the challenges and limitations in the existing technology that hinder level five autonomous driving. Specifically, this work documents the current capabilities of the existing autonomous vehicles’ perception and cognition system and outlines their future skills towards addressing complete autonomous last-mile delivery, as well as efficient robotic process automation in logistics from warehouse/distribution center to hub’s delivery.

Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties. In this paper, we present a comprehensive autonomous navigation architecture for holonomic mobile robots in greenhouses. Our approach utilizes the heating system rails to navigate through the crop rows using a single stereo camera for perception and a LiDAR sensor for accurate distance measurements. A finite state machine orchestrates the sequence of required actions, enabling fully automated task execution, while semantic segmentation provides essential cognition to the robot. Our approach has been evaluated in a real-world greenhouse using a custom-made robotic platform, showing its overall efficacy for automated inspection tasks in greenhouses.

During the last decades, the research endeavours on autonomous driving found great resonance in Advanced Driver-Assistance Solutions that equipped the contemporary civilian vehicles and significantly boosted their driver-less mobility. The existing applications are mostly focused on urban scenarios where signs, road lanes and markers are well defined and ordered favouring the motion of the vehicles whilst, less attention has been paid to the semi-structured and rural environments where traffic infrastructure is scarce. The paper at hand introduces a holistic framework for autonomous vehicles navigation in semi-structured environments. Semantic cues fused with geometrical information of LiDAR data are used for road detection and tracking. OpenStreetMaps are employed as a rough route planner, the waypoints of which are rectified via a probability distribution function over the visible area of vehicle’s vicinity. Thus, vehicle’s localization is obtained by Normal Distribution Transform (NDT) SLAM, where the covariance of egomotion estimation is obtained by processing short-term 3D maps, fused with GPS measurements by means of an extended Kalman filter. Local planning and execution of vehicle’s motion is applied on local cost maps formulated by the union of 2D laser readings and the detected road boundaries fitted through Bézier curves. The complete framework has been evaluated with the aid of a real Autonomous Guided Vehicle in a constrained semi-structured urban area, exhibiting robust navigation performance.

Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportional–integral–derivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examined by using different configurations of the sensor that is used to perform the area coverage.

The rapid growth of data-driven models in advanced driver assistance systems (ADAS) has been significantly accelerated by the widespread availability of large-scale public datasets enabling more accurate training and benchmarking. Despite this progress, achieving higher autonomy still requires continuous progress in core functionalities, necessitating an increasing supply of high-quality data. This escalating demand, coupled with the growing number of smart vehicles, presents substantial challenges to the sustainability of data operations, particularly concerning computational and energy resource consumption. This paper directly addresses the critical needs for sustainable practices in autonomous vehicle data operations, with a particular focus on data creation and post-processing. To this end, we present an applied study demonstrating a structured integration of existing data handling techniques into a scalable and modular pipeline, that strategically combines existing, proven data management techniques—such as deduplication, compression, anonymization, and event-driven filtering. This pipeline is particularly designed to minimize storage needs and increase data usability, thereby contributing significantly to overarching sustainability objectives without compromising data quality. The practical benefit of the proposed method lies in its ability to reduce storage requirements by over 50%, lower computational overhead, and simplify AI model training workflows using more compact and privacy-compliant datasets. The proposed pipeline has been validated on two real-world autonomous driving datasets, confirming its effectiveness in improving sustainability and efficiency in data operations for autonomous vehicles.