This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM${}_{2.5}$ sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM${}_{2.5}$ trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.

In this article, a novel dense underwater 3-D mapping paradigm based on pose graph simultaneous localization and mapping (SLAM) using an acoustic camera mounted on a rotator is proposed. The demands of underwater tasks, such as unmanned construction using robots, are growing rapidly. In recent years, the acoustic camera, which is a state-of-the-art forward-looking imaging sonar, has been gradually applied in underwater exploration. However, distinctive imaging principles make it difficult to gain an intuitive perception of an underwater environment. In this study, an acoustic camera with a rotator was used for dense 3-D mapping of the underwater environment. The proposed method first applies a 3-D occupancy mapping framework based on the acoustic camera rotating around the acoustic axis using a rotator at a stationary position to generate 3-D local maps. Then, scan matching of adjacent local maps is implemented to calculate odometry without involving internal sensors, and an approximate dense global map is built in real time. Finally, based on a graph optimization scheme, offline refinement is performed to generate a final dense global map. Our experimental results demonstrate that our 3-D mapping framework for an acoustic camera can achieve dense 3-D mapping of underwater environments robustly and accurately.

This paper presents a novel method to detect when a vehicle is changing lanes by considering individual driving styles. Some previous works have focused on the relation between lane changes and driving styles; however, the detection method of lane changes while considering driving styles is still not reported. The proposed method extracts a new feature to appropriately describe driving styles based on drivers' risk taking behavior when following the preceding vehicle. Based on the driving style of the target vehicle, performing a lane change is estimated at each time step. The determined driving style is used as input for the lane-change detection method based on the gap acceptance model, and it is integrated with the detection method based on vehicle movements. Experiments are used to demonstrate that the proposed method considerably improves detection performance.

Robots navigating in a robot–human coexisting environment need to optimize their paths not only for task-related performance (e.g. safety and efficiency) but also for their social compliance to other pedestrians. This is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning (DRL) techniques by employing them to train efficient policies for robot navigation. However, their performance deteriorates when the crowd size grows. We cope with this problem by allowing the robot to keep an adapting distance from the pedestrians and perform safe navigation even in high density environments. We first derive a quantitative formula representing the relationship between uncomfortable distance and pedestrian density from a real-word tracking dataset. Then this formula is applied in reward shaping of DRL to get resilient reward functions (R2F). Qualitative and quantitative evaluation results demonstrate that our method outperforms state-of-the-art methods in both low and high pedestrian density environments. GRAPHICAL ABSTRACT

In this paper, a novel method is proposed to estimate the driving styles of other drivers based on a driving risk feature. This new feature is proposed using a dynamic potential field method wherein the distribution changes depending on the relative number of adjacent vehicles. A more appropriate description of driving risk is obtained compared to other indices. The proposed feature dramatically improves the accuracy of estimating the driving style. To estimate the driving styles, this study considers a problem of the fundamental model under a scenario wherein the target vehicle follows the preceding vehicle. The proposed estimation method is validated through experimental results.

In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.