This paper proposes a novel approach for guidance law design to satisfy the impact-time constraints for a certain class of homing missiles. The proposed guidance law provides proper lateral acceleration commands that make the impact time error converge to zero by the time of impact. This scheme can be applied to any existing guidance law for which a formula of predicted time to go is available. Convergence of time-to-go errors is supported by Lyapunov stability. The optimal guidance law (OGL) and the impact angle control guidance (IACG) law are extended by the proposed method for impact-timecontrol guidance (ITCG) and impact-time-and-angle-control guidance (ITACG), respectively. The performance of the extended guidance laws is demonstrated by numerical simulation

This paper presents a cooperative distributed planning algorithm that ensures network connectivity for a team of heterogeneous agents operating in dynamic and communication-limited environments. The algorithm, named CBBA with Relays, builds on the Consensus-Based Bundle Algorithm (CBBA), a distributed task allocation framework developed previously by the authors and their colleagues. Information available through existing consensus phases of CBBA is leveraged to predict the network topology and to propose relay tasks to repair connectivity violations. The algorithm ensures network connectivity during task execution while preserving the distributed and polynomial-time guarantees of CBBA. By employing under-utilized agents as communication relays, CBBA with Relays improves the range of the team without limiting the scope of the active agents, thus improving mission performance. The algorithm is validated through simulation trials and through experimental indoor and outdoor field tests, demonstrating the real-time applicability of the approach.

This paper addresses anomaly detection and monitoring for swarm drone flights. While the current practice of swarm flight typically relies on the operator’s naked eyes to monitor health of the multiple vehicles, this work proposes a machine learning-based framework to enable detection of abnormal behavior of a large number of flying drones on the fly. The method works in two steps: a sequence of two unsupervised learning procedures reduces the dimensionality of the real flight test data and labels them as normal and abnormal cases; then, a deep neural network classifier with one-dimensional convolution layers followed by fully connected multi-layer perceptron extracts the associated features and distinguishes the anomaly from normal conditions. The proposed anomaly detection scheme is validated on the real flight test data, highlighting its capability of online implementation.

A spacecraft attitude control system provides mechanical and electrical control to achieve the required functions under various mission scenarios. Although generally designed to be highly reliable, mission failure can occur if anomalies occur and the attitude control system fails to properly orient and stabilize the spacecraft. Because accessing spacecraft to directly repair such problems is usually infeasible, developing a continuous condition monitoring model is necessary to detect anomalies and respond accordingly. In this study, a method for detecting anomalies and characterizing failures for spacecraft attitude control systems is proposed. Herein, features are extracted from multidimensional time-series data of a simulation of the attitude control system. Then, the artificial neural network learning algorithms based on two types of generation models are applied. A Bayesian optimization algorithm with a Gaussian process is used to optimize the hyperparameters for the neural network to improve the performance. The performance is evaluated based on the reconstruction error through the algorithm using the newly generated data not used for learning as input data. Results show that the detection performance depends on the operating characteristics of each submode in the operation scenarios and type of generation model. The diagnostic results are monitored to detect anomalies in operation modes and scenarios.