A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

A reactive collision avoidance algorithm is proposed to enable safe autonomous flight in the presence of multiple dynamic obstacles. A position-controlled hexacopter equipped with a visual sensor obtaining obstacle information is considered as an Unmanned Aerial Vehicle (UAV) platform. The proposed method centers on the concept of bounding tube which intrinsically extends the static bounding box to incorporate forthcoming movement of the obstacles into the collision avoidance framework. The processing pipeline consists of separate components for each of the sequential tasks in obstacle sensing and tracking. Computation of a spherical bounding box for each obstacle is followed by discrete-time Kalman filtering for prediction of obstacle trajectory to detect potential collision. If the current course of UAV turns out highly likely to end in collision with any of the obstacles, the vehicle steers to an aiming point chosen from among the bundle of candidates produced by constructing a bounding tube that takes account of predicted obstacle motion. The bounding-tube-based aiming point generation extends seamlessly to the case with multiple moving obstacles through running in series with multi-obstacle track management that combines hierarchical clustering of sensory data points for obstacle identification and a simple geometric method for data association. Numerical simulations are conducted to verify the performance of the proposed collision avoidance algorithm.

Integrated guidance and control using model predictive control against a maneuvering target is proposed. Equations of motion for terminal homing are developed with the consideration of short-period dynamics as well as actuator dynamics of a missile. The convex optimization problem is solved considering inequality constraints that consist of acceleration and look angle limits. A discrete-time extended Kalman filter is used to estimate the position of the target with a look angle as a measurement. This is utilized to form a flight-path angle of the target, and polynomial fitting is applied for prediction. Numerical simulation including a Monte Carlo simulation is performed to verify the performance of the proposed algorithm.