Unmanned Systems 

About: Unmanned Systems is an academic journal published by World Scientific. The journal publishes majorly in the area(s): Computer science & Control theory (sociology). It has an ISSN identifier of 2301-3850. Over the lifetime, 78 publications have been published receiving 35 citations.

Warehouse Inspection with an Autonomous Micro Air Vehicle

Abstract: In this work, we present an approach to address the problem of warehouse inspection using a MicroAir Vehicle (MAV) that performs autonomous flight by following a set of waypoints in a GPS-denied environment. During the navigation, a second onboard camera is used to observe the inspection area where it is expected to observe QR codes attached to packages placed on shelves. To this end, we use a metric monocular simultaneous localization And Mapping (SLAM) system to estimate the MAVs position in metres, avoiding the need for an external positioning system. The onboard computer executes the detection and decoding of QR codes and the SLAM system. The MAV can also communicate with a Ground Control Station (GCS) to display telemetry, the MAVs position, images from the inspection camera with detected QR codes, and a list of the found packages. This approach was used to compete in the indoors competition of the International Micro Air Vehicle Competition (IMAV) 2019, where we received the special award: Best Flight Performance.

ℒ1 Adaptive Output Feedback Control of Small Unmanned Aerial Vehicles

Abstract: An approach for output feedback [Formula: see text] adaptive control of small Unmanned Aerial Vehicles (UAVs) is presented in this paper. The design is based on a state observer instead of the state predictor. The main advantage is that a full state measurement can be avoided, and the design and implementation of the controller are simplified. Furthermore, since the state space description is maintained, the system dynamics including uncertainties can be specified with physical insight, which simplifies practical applications. The adaptation law borrows insights from the sliding mode control to estimate the unknown bounds of external disturbances. Flight test results for the control of a small UAV show the robustness of the [Formula: see text] adaptive controller to large uncertainties and disturbances.

Reinforcement Learning Applications in Unmanned Vehicle Control: A Comprehensive Overview

Abstract: This paper briefly reviews the dynamics and the control architectures of unmanned vehicles; reinforcement learning (RL) in optimal control theory; and RL-based applications in unmanned vehicles. Nonlinearities and uncertainties in the dynamics of unmanned vehicles (e.g. aerial, underwater, and tailsitter vehicles) pose critical challenges to their control systems. Solving Hamilton–Jacobi–Bellman (HJB) equations to find optimal controllers becomes difficult in the presence of nonlinearities, uncertainties, and actuator faults. Therefore, RL-based approaches are widely used in unmanned vehicle systems to solve the HJB equations. To this end, they learn the optimal solutions by using online data measured along the system trajectories. This approach is very practical in partially or completely model-free optimal control design and optimal fault-tolerant control design for unmanned vehicle systems.

An Improved RRT* UAV Formation Path Planning Algorithm Based on Goal Bias and Node Rejection Strategy

Abstract: This paper proposes an improved RRT * formation path planning algorithm based on goal bias and node rejection strategy to solve UAVs’ formation path planning problems in a complex environment. Aiming at the position constraint problem of multi-UAV in the planning process, the leader–follower structure among UAVs and the formation configuration model are established. Furthermore, aiming at node redundancy and slow planning speed caused by the RRT * algorithm in a complex environment, this paper sets the goal bias information so that the random tree could find the initial path quickly. At the same time, this paper proposes a node rejection strategy to prevent the nodes that do not meet the pre-set conditions from participating in the subsequent expansion. Compared to the standard RRT-related algorithms, the proposed improved algorithm can shorten the planning time, reduce the number of algorithm iterations and improve the algorithm’s applicability in the formation path planning problem.

Real-time 3D UAV Path Planning in Dynamic Environments with Uncertainty

Abstract: The integration of Unmanned Aerial Vehicles (UAVs) is being proposed in a spectrum of applications varying from military to civil. In these applications, UAVs are required to safely navigate in real-time in dynamic and uncertain environments. Uncertainty can be present in both the UAV itself and the environment. Through a literature study, this paper first identifies, quantifies and models different uncertainty sources using bounding shapes. Then, the UAV model, path planner parameters and four scenarios of different complexity are defined. To investigate the effect of uncertainty on path planning performance, uncertainty in obstacle position and orientation and UAV position is varied between 2% and 20% for each uncertainty source first separately and then concurrently. Results show a deterioration in path planning performance with the inclusion of both uncertainty types for all scenarios for both A* and the Rapidly-Exploring Random Tree (RRT) algorithms, especially for RRT. Faster and shorter paths with similar same success rates (>95%) result for the RRT algorithm with respect to the A* algorithm only for simple scenarios. The A* algorithm performs better than the RRT algorithm in complex scenarios.