Unmanned Systems

Over the past decade, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this paper systematically reviews and analyzes the use of 3D ToF LiDARs for mobile robotics under harsh conditions such as adverse weather, GPS-denied, and highly dynamic environments for both research and industrial applications. The former include LiDAR data processing in adverse weather, object detection, and autonomous navigation. The latter encompasses autonomous driving, service robotics, and public health crises applications. We hope that our efforts can effectively provide readers with a reference based on our hands-on experiences and promote the deployment of existing mature technologies in real-world systems.

Trajectory prediction models play a crucial role in various applications such as autonomous driving and human–robot interaction. For trajectory prediction models, understanding and accounting for the intentions of vehicles, such as whether they intend to change lanes, make turns, or maintain their current course, is vital for enhancing the safety and efficiency of autonomous systems. However, one significant limitation of some existing trajectory prediction models is their failure to fully consider the impact of vehicle intentions on agent trajectories. This paper addresses the problem that some existing trajectory prediction models do not fully consider the impact of vehicle intentions on agent trajectories. To tackle this challenge, we propose a self-supervised learning-based module for detecting vehicle intentions, which can be added to most trajectory prediction models. The module consists of a simple and effective pretext task and a lightweight intent detection model. We conduct extensive experiments on a large-scale motion forecasting benchmark to demonstrate the effectiveness of the proposed approach in improving the performance of trajectory prediction baselines.

The wide spectrum of recent applications for UAVs imposes further challenges to their abilities and control. This is especially true when operating in harsh and hostile environments where disturbances are huge and actuators are prone to failure. Conventional systems and traditional control techniques are not sufficient for stability and tracking under these circumstances. This paper proposes a unique innovative overactuated quadrotor system that has six DoFs. The vehicle has four rotors and each rotor can tilt independently in the $YZ$-plane. It has the ability to correct its position and attitude in a decoupled way which is different from the conventional quadrotor configurations. Sliding-mode control associated with switching control mode is used for the control algorithm of the system. The system shows agility in the face of large disturbances and robustness against actuator failure which makes it a perfect fit for extreme and challenging environments. The concept of the system and its ability are illustrated in simulation with promising results.

The spacecraft’s (SC) electric power system (EPS) is one of the vital systems. It could limit the SC capabilities or reduce the SC lifetime. The EPS should provide the required energy for the SC loads to perform their tasks successfully until End-of-Life (EOL). Furthermore, the EPS should guarantee the power needed to execute the customer requirements under all degradations (battery degradation, radiation, etc.) and other external factors (orbital degradation). The SC’s primary power generation sources are solar arrays and storage batteries. This paper investigates a SC with a Nickel Hydrogen Storage Battery (NHSB) consisting of 17 cells: 4 measured cells and 13 unmeasured cells. In this paper, two distinct objectives have been achieved: The first objective is formulating a control algorithm for battery testing and reconditioning (CABTR). This algorithm is applied to an NHSB in an orbital setting mode, rectifying the memory effect in the NHSB and restoring the battery’s total capacity. The second objective constructs an environment that effectively simulates the dynamic behavior of the battery during in-orbit operations. This simulated environment calculates crucial parameters such as the state of charging (SOC), battery temperature, voltage, and pressure of the NHSB during reconditioning. This computational analysis assures the robustness of the CABTR, and confirms the optimal battery performance. Subsequent validation encompasses ground tests and real-time telemetry data, which affirms the battery’s dynamic behavior and the developed algorithms’ efficacy.

Unmanned Aircraft Systems (UASs) have become increasingly important assets for naval and maritime law enforcement operations. In response to the ever-increasing demand for autonomous systems, this paper presents a design, development, integration, and flight testing of an unmanned rotorcraft system capable of operating with a moving ship. The system was developed initially by playing back ship motion data in a simulation environment. Ship motion prediction, relative guidance, operator control interface, and landing flight management system were developed and tested against simulated sea state motion. The system was then flight tested using a 15-kg electric helicopter operating off a US Naval Academy’s yard patrol craft. The craft was instrumented with IMU and GPS for the craft’s own state estimation. The craft was also used as a moving-base station for Real-time Kinematic (RTK) relative positioning. The flight operation was done in the Chesapeake Bay. Autonomous capabilities including takeoff, landing, station-keeping, and maneuvering relative to a moving ship were successfully demonstrated.

Optimal trajectory planning in a windy environment is a complex problem when airplane performance characteristics are considered. This paper introduces a novel form of Legendre pseudospectral optimization to solve boundary value problems in UAV trajectory planning. The proposed architecture applies Legendre–Gauss–Lobatto and Hamilton–Jacobi–Bellman equations to generate candidate pieces of trajectories with respect to the UAV dynamic constraints. Analytical performance-based solutions are also developed for sample cases to achieve an optimal criterion in trajectory planning. Moreover, the notion of wind soaring is exploited to use the beneficial effects of tailwind velocities in trajectory planning. Integral cost functions are handled by Gauss-type quadrature rules. Simulations demonstrate the efficiency of the pseudospectral method compared with other solvers, in eliminating the difficulties of boundary value problems by employing the boundary points in the interpolation equation. The effectiveness of the proposed approach is demonstrated through dynamic simulations.

This paper considers the problem of trajectory planning for articulated vehicles in complex environments. We formulate this problem as an optimal control problem (OCP) and propose a greedy-strategy-based planner. This planner consists of three stages. In stage 1, an IAA* algorithm is proposed to identify the homotopy class. In stage 2, the collision-free tunnels are constructed along the guiding trajectory generated in stage 1 to simplify the intractable collision-avoidance constraints. In stage 3, a greedy-strategy-based iterative optimization (GSIO) framework is designed, which contributes to escaping from local optimums, making the optimization process more targeted, and converging to the global optimum solution quickly, especially in complex tasks. One feature of the proposed planner is that it is suitable for any type of articulated vehicle, and the proposed optimization framework can be used as an open framework to optimize any criterion that can be described explicitly by a polynomial. Furthermore, in the set simulation cases, our work shows significant competitiveness, under the premise of ensuring moderate CPU processing time, our algorithm achieves approximately a 40% performance improvement in optimization effects compared to selected comparative algorithms.

With advances in unmanned aerial vehicle (UAV) technologies, they have been put to use in a wide variety of scenarios, including emergency surveillance and disaster management. However, their widespread usage is still limited due to their expensive nature. Only user-piloted UAVs are extensively used still today; but their low-budget design leads to lack of auto-guidance and cooperative functions in both line-of-sight and obstacle-sensitive operations. This paper presents a new approach of self-guidance in these budget UAVs for stable maneuverability even in the obstacle-sensitive surroundings. Further, the paper additionally incorporates cooperative functioning in such self-guided budget UAVs for completing collaborative tasks of emergency surveillance. Comparative testbed results show the cost-effective benefits of the proposed methods and the precise working abilities in no-obstacle as well as obstacle-sensitive sites.

This paper focuses on trajectory tracking, robustness and stabilization of a golf swing robot which has been recently developed to simulate the ultra-high-speed swing motions of a golfer. The proposed control strategies are based on the Lyapunov stability theory and include Backstepping and Sliding-Mode Control based techniques. To attenuate the chattering phenomena caused by a discontinuous switching function and improve the dynamic response of the manipulator, a fuzzy system is used in this research; a Backstepping Sliding-Mode Controller (BSMC), a Backstepping Fuzzy Sliding-Mode Controller (BFSMC) and a Super-twisting Backstepping Sliding-Mode Controller (STBSMC) are used to evaluate the proposed hybrid controller’s BFSMC performance. The Lyapunov stability theory is used to guarantee the stability of the proposed closed-loop robot technique. Numerical simulations show the effectiveness of the proposed strategy based on the fuzzy logic mechanism under different disturbances and uncertainties.

Tailless Unmanned Aerial Vehicles (UAVs) offer several advantages over their conventional counterparts, including enhanced maneuverability, higher durability, and better stealth. However, they are challenging in their stability and control due to the absence of stabilizing and control surfaces that typically exist in conventional empennage. A crucial process for analyzing the tailless UAVs’ stability/performance characteristics and determining/validating their flight control parameters is done via the modeling and simulation of flight dynamics. This paper presents a comprehensive and systematic procedure for investigating the flight dynamics of a tailless UAV, including modeling, simulation, analytical verification, and flight testing, while also explaining the interconnections among these elements. It also addresses the common challenge of limited accessibility of UAV essential data through using diverse analytical, empirical, and experimental methods. First, a rapid and effective first-principles modeling approach is introduced to simulate the nonlinear six-degree-of-freedom flight dynamics of a small tailless UAV case study. The modeling process follows a modular framework where well-defined experiments and commercial of-the-shelf software, tools, and sensors are employed to build the necessary sub-models, including geometric, mass–inertia, aerodynamic, propulsion, and actuator models. Then, all sub-models are integrated into a simulation environment to allow the prediction of the UAV dynamic response obtained from the given control inputs. The developed flight dynamic model is subjected to a thorough verification process to ensure its integrity and proper functionality by comparing the simulated trim and natural flight modes with the calculated analytical results. Finally, a set of specific flight tests are performed to validate the developed simulation model and verify relevant performance characteristics for the case-study UAV. The results show that the proposed approach provides a systematic and straightforward method for examining the flight dynamics of small tailless UAVs with reasonable accuracy.

An essential characteristic of a fully autonomous robot is the capability to examine an unfamiliar environment and construct a representation of it. The challenge of autonomous exploration involves overcoming various sub-problems, including Simultaneous Localization and Mapping (SLAM), motion planning, target identification, and informed decision-making for target selection. This paper presents a frontier-based methodology to identify potential navigation targets for the autonomous exploration of unknown environments by an omnidirectional robot. Permanent and temporary Rapidly-exploring Random Tree (RRT)-based structures are used to search the map and detect frontier points. A novel temporary RRT-based structure, Frontier Temporary Tree, is introduced in this study. It is noteworthy that RRT is solely used to search the explorable space for frontier points and does not contribute to motion planning. A cost-benefit analysis, taking into account path cost, heading cost, and information gain, is used to evaluate the frontier points and determine the best target among them. The proposed method is subjected to rigorous testing through both simulation and experimental studies with an omnidirectional robot under real-world scenarios. Comparative results from simulation studies show that our method consistently outperforms, demonstrating its robustness and efficacy.

Accurate and robust state estimation is critical for the heterogeneous agent systems, particularly when considering the challenges posed by Unmanned Aerial Vehicles (UAVs) operating in perceptually-degraded environments where access to Global Navigation Satellite System (GNSS) signals is lost. We can, however, actively increase the amount of optimal localization available to UAV by augmenting them with a small number of more expensive, but less resource-constrained, heterogeneous agents. In this paper, we propose a novel detection, localization, and tracking framework for UAV based on LiDAR. First, we present an innovative approach that integrates range image projection and Depth Cluster of LiDAR point clouds with UAV technology. Subsequently, we devise a multidimensional feature probability detection and tracking evaluation function, enabling the detection, estimation, and active tracking of UAV movement. Finally, we conduct comprehensive experiments using heterogeneous agent systems to assess the effectiveness and robustness of the developed framework. The experiments reveal a minimum 20% reduction in running time and an average localization accuracy error of 1.98cm.

The Unmanned Aerial System (UAS) Traffic Management (UTM) surveillance plays an important role in monitoring the safety compliance of UAV flights within specific operational zones. However, the quality of data reception from UAVs depends on transmission signal quality, leading to variable data latency during flights. As a means of addressing this issue and improving UAV operational safety, trajectory prediction emerges as a promising solution. This study aims to implement real-time trajectory prediction through the integration of machine learning techniques within the UTM surveillance system using Broadcast Remote ID. To facilitate this, a homemade receiver for broadcast Remote ID is developed using the ESP32 microcontroller. Subsequently, two distinctive flight tests are executed using a DJI Phantom 4 UAV. In the first flight, UAV trajectory data are used to serve as training input for the machine learning algorithms. The second flight focuses on the implementation of real-time trajectory prediction. The trajectory prediction model uses inputs such as latitude, longitude, height, speed, direction, latency time, and the Received Signal Strength Indicator (RSSI). Through an analysis of the first flight’s offline data, the Gated Recurrent Unit (GRU) algorithm is preferred to the Long Short-Term Memory (LSTM) algorithm. The outcomes of the second flight test prove the feasibility of the GRU-based trajectory prediction. The GRU model successfully produces real-time predictions during UAV flight, showcasing accuracy levels similar to those derived from offline prediction analyses with short processing time. This real-time trajectory prediction could improve the safety of UAV operation by providing the estimated position when the latency time is higher than the threshold.

In this paper, an exponentially stable trajectory tracking controller is proposed for multi-UAV payload transport. The multi-UAV payload system has a 2-DOF magnetic spherical joint between the UAVs and the vertical rigid links of the payload frame so that the UAVs can roll or pitch freely. These vertical links are rigidly attached to the payload and cannot move. An input–output feedback linearized model is derived for the complete payload-UAV system along with thrust vectoring control for trajectory tracking of the payload. Theoretical analysis of tracking control laws is shown to be exponentially stable, thus guaranteeing safe transportation along the desired trajectory. To validate the performance of the proposed control law, the results for a numerical simulation as well as a high-fidelity Gazebo real-time simulation are presented. The tracking performance is compared to the existing techniques in the literature, and it is seen that the proposed approach has the least RMSE for tracking performance. Next, the robustness of the proposed controller is analyzed against two practical situations: External disturbance on the payload and payload mass uncertainty. The results clearly indicate that the proposed controller is robust and computationally efficient while achieving exponentially stable trajectory tracking.

The growing utilization of unmanned aerial vehicles (UAVs) in military operations has necessitated the development of a suitable weaponry for these kind of platforms. One of the trending categories of such armaments is the aerial gliding vehicle (AGV). AGVs have no propulsion system, consequently, a critical need for a robust flight control system (FCS) tailored to this kind of aerial systems is raised. This research focuses on designing a nonlinear model based controller, starting with the construction of a precise model through practical experiments and the establishment of a dedicated testing and flight simulation environment. Recognizing the limitations of traditional nonlinear dynamic inversion (NDI) due to its dependence on the vehicle model, the modified incremental nonlinear dynamic inversion (MI-NDI) is developed to operate in the presence of wind, model mismatches, and external disturbances. In this research, an extensive testing is conducted in a hardware-in-the-loop (HIL) simulation environment which validates the MI-NDI controller’s superior performance, even in challenging conditions. The research outcomes mark a significant advancement in enhancing autopilot precision for advanced aerial weaponry and unmanned vehicles.

Due to their propensity for stripe noise distortions, infrared remote sensing images present substantial difficulty for interpretation. Our ability to address this issue by offering an easy, efficient, and fast technique for image stripe noise correction is what makes our work unique. Our proposed solution tackles stripe noise by subtracting the mean value along the stripes from the noisy image. Additionally, we leverage the wavelet transform on the average signal to exploit the inherent sparsity of noise in the wavelet domain. This approach not only enhances denoising performance without introducing blurring effects but also enables us to recover image details with remarkable precision, all without the need for intricate algorithms, iterative processes, or training models. To validate the effectiveness of our approach, we conducted evaluations using a dataset of real-world infrared remote sensing images. This dataset encompasses a wide range of examples, featuring both real and artificially induced noise scenarios.

Under the background that multi-domain collaborative tasks with complex constraints executed by heterogeneous unmanned systems, based on a consensus-based bundle algorithm, a consensus-based synergy algorithm is proposed to solve the distributed multi-domain collaborative task planning problem. First, a multi-domain collaborative task allocation model and score evaluation system are established by considering task resource requirement constraints, task timing constraints, and path threat constraints. Second, the time sequence constraints of collaborative tasks are transformed into elastic time window constraints. The bidding method based on collaborative constraints is used for task allocation, and the improved consistency conflict resolution principle is adopted to realize distributed multi-domain collaborative task allocation conflict resolution. Finally, the path planning is coupled to the task allocation process by using the Bezier curve path, and the results of multi-domain collaborative task allocation and path planning are obtained synchronously. Simulation results show that the proposed algorithm can effectively solve the problem of multi-domain collaborative task planning for heterogeneous unmanned systems.

Conventionally, the Three-Loop Autopilot is dominated by three main parameters namely the damping factor, time constant and the open-loop crossover frequency, to achieve the desired performance. In developing the autopilot gains, the same gains as the conventional approach can also be obtained using the general optimality theory. The basic idea of setting the design parameters and the feedback gains using methods of optimal control revolves around the proper selection of the performance index for which the control is optimized. In this paper, an explicit formula for autopilot gains is driven by taking into account the relations between the desired performance and the open-loop frequency, phase margin and time constant without the need to adjust the LQR weights. Since each set of design parameters selection doesn’t guarantee the existence of the optimal control law, the optimization criteria for the three-loop autopilot are derived to know the set of design parameters for which the control is optimized. Finally, an optimal criterion to select the design parameters and therefore the autopilot gains is developed, where the time constant is set to the designer objective while the open-loop crossover frequency and the phase margin as design constraints.

Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.