This paper introduces AirLock+, an end-to-end vision system for scalable UAV-to-satellite image registration, enabling two key downstream tasks: (i) precise target geolocalization in geodetic coordinates and (ii) geospatial augmented reality to elevate situation awareness. AirLock+comprises three modules: A predictive tracker first localizes targets in UAV image frames, while a cross-view image matcher generates robust UAV-to-satellite homographies that withstand severe domain gaps, outdated satellite imagery, and generalize to unseen environments without finetuning. The resulting pixel-to-world correspondences enable target pixel coordinates to be mapped into geodetic space, yielding continuous trajectory estimates and supporting geospatial augmentation of UAV video feeds. Our system achieves an average target localization error of 20.23 m across 7.8 km real-world trajectories, demonstrating robustness in high-altitude, oblique-view conditions where existing methods typically fail.

Despite the rise of mobile robot deployments in community settings, the perceived safety of cohabitants remains understudied in many domains. To address this gap, we perform a study to identify elements of indoor human–mobile robot encounters that impact perceived safety. This study evaluates the effects of robot movement behavior and the number of robots nearby on perceived safety of participants. Further, this article investigates how the presence of other people impacts perceived safety in such settings. We leverage methodologies from physiological signal analysis, autonomy, surveys, and qualitative interviews to decode insights into the human experience during such encounters. Particularly, signal analysis yielded that the presence of multiple robots decreases perceived safety and that search behaviors were more comfortable than navigation behaviors. Similarly, interviews with participants demonstrated clear effects on perceived safety in the presence of others, and that sensemaking was a key component involved in their perceptions of safety. When the data were combined, interview data revealed that near collisions between the robots likely confounded the signal analysis findings with respect to the number of robots and their movement behavior. The data types agree that the presence of a robot impacts perceived safety; however, there are also conflicting results that we discuss, which highlight that near-collisions impact perceived safety. In aggregate, the study illustrates the benefits of leveraging eclectic methods to ascertain deeper insights. Overall, the article aims to unlock insights into human perceptions during encounters with community embedded robots, which can be used in the future design of such systems.

This paper proposes a bi-level Multi-Start Variable Neighborhood Search-Genetic Algorithm (MSVNS-GA) for the heat pipe-constrained component layout optimization (HCLO) problems. The proposed algorithm has won the first place in the CEC’2022 Competition on the Heat Pipe-Constrained Component Layout Optimization. First, the HCLO problem is divided into two sub-problems, heat pipe assignment (HA) and component location (CL). In the HA problem, components are assigned to different heat pipes. The best assignment scheme is taken as the input of the CL problem. In the CL problem, the specific coordinates of components are determined to meet practical engineering constraints. In this way, the complexity of the problem is lowered, and a part of the infeasible solution is cropped. Second, to address the HA problem, a multi-start variable neighborhood search algorithm is proposed and five efficient bottleneck-aware neighborhood structures are designed. And the genetic algorithm is used for CL problem. Finally, 30 independent experiments are carried out on the calculation examples with sizes of 6×4, 15×6, 40×16, and 90×32. The best result obtained by MSVNS-GA is 0.0%, 1.0%, 0.8%, and 1.1% different from the estimated lower bounds.

Future smart factories need to use intelligent transport devices like automated guided vehicles (AGVs) for connecting intelligent production and logistics. To address the lack of edge side functions in the current AGV systems, this paper proposes an agent-based system architecture for AGVs in cloud-edge computing environments. The system is divided into three main components: the cloud center, edge nodes, and AGV agents. The cloud center is largely responsible for AGV transport route planning, while the edge nodes are in charge of AGV transport control and equipment management. The driving function and executing commands are handled by AGV agents. AGV agents can communicate and collaborate with each other to address emergent issues. The proposed approach has been validated through simulations.

This paper presents a LiDAR-based end-to-end autonomous driving method with Vehicle-to-Everything (V2X) communication integration, termed V2X-Lead, to address the challenges of navigating unregulated urban scenarios under mixed-autonomy traffic conditions. The proposed method aims to handle imperfect partial observations by fusing the onboard LiDAR sensor and V2X communication data. A model-free and off-policy deep reinforcement learning (DRL) algorithm is employed to train the driving agent, which incorporates a carefully designed reward function and multi-task learning technique to enhance generalization across diverse driving tasks and scenarios. Experimental results demonstrate the effectiveness of the proposed approach in improving safety and efficiency in the task of traversing unsignalized intersections in mixed-autonomy traffic, and its generalizability to previously unseen scenarios, such as roundabouts. The integration of V2X communication offers a significant data source for autonomous vehicles (AVs) to perceive their surroundings beyond onboard sensors, resulting in a more accurate and comprehensive perception of the driving environment and more safe and robust driving behavior.

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

This paper presents a Vehicle-Platoon-Aware Bi-Level Optimization Algorithm for Autonomous Intersection Management (VPA-AIM) to coordinate the merging of Connected and Automated Vehicles at unsignalized intersections. The constraint-coupled bi-level optimization is operated within a rolling horizon to balance traffic performance and computational efficiency. In each decision step, the platoon formation scheme is incorporated into an upper-level traffic scheduling model as decision variables to pursue an optimal schedule from a systemic view. Meanwhile, the passing sequence and timeslots of vehicles are jointly optimized with the platoon configuration scheme by virtue of real-time traffic states to improve operational efficiency and fairness. After that, a lower-level trajectory planning model will generate dynamically-feasible and energy-efficient trajectories according to the given schedule and coupling constraints with the objective of improving space utilization to prevent spillbacks. Moreover, the quantifiable connection between the makespan of traffic scheduling schemes and the occurrence of spillbacks is established, demonstrating that the cooperative platoon formation strategy is effective in avoiding and mitigating spillbacks in normal and saturated traffic states. Additionally, the proposed algorithm can be extended to mixed traffic scenarios. Numerical experiments are conducted on extensive scenarios with different arrival flows, where the Constraint Programming technique is employed to produce the optimal schedule. Experimental results indicate the superiority of the proposed approach in optimality and stability with reasonable sub-second computation time for real-life applications.

This paper investigates a longitudinal trajectory optimization problem of connected and automated vehicles with an energy-aware non-linear objective. In this paper, we first approximate each vehicle trajectory with a cubic spline function using the proposed solution representation scheme, while the curve shape can be controlled by the knot vectors. After that, we propose a new coevolutionary algorithm that decomposes the initially high-dimensional problem and performs as the optimizer for subproblems. In the local exploitation phase, a problem-specific steepest ascent hill-climbing algorithm is developed to escape from local minimum points and speed up convergences. This proposed approach is compared with several state-of-the-art algorithms in multiple scenarios with different traffic densities and platooning sizes. Simulation results indicate that it can yield near-optimal solutions with reasonable computation times for real-life applications.