Drone Trajectory Research Articles

Preferred Distance in Human-Drone Interaction.

In two augmented-reality experiments, we transferred the paradigm of interpersonal distance regulation to human-drone interaction. In the first experiment, we used a simple spherical drone model and explored how both hovering height and approach angle affect the preferred distance. Drone height above the ground had a strong effect. The preferred distance to the drone was larger than that typically found toward human actors, in particular, when the drone trajectory was very high. In the second experiment, we sought to gain a deeper understanding of the factors that may influence this effect. In addition to the simple spherical drone model used in the first experiment, we also varied its appearance and attachment to the ground. Surprisingly, anthropomorphic features increased preferred distances. We, therefore, discuss the extent to which social aspects and subjectively perceived danger influence the preferred distance for interaction with drones, which thus need to be considered in the design of human-drone interaction.

Reservoir Computing for Drone Trajectory Intent Prediction: A Physics Informed Approach.

The design of accurate trajectory prediction algorithms is crucial to implement adequate countermeasures against drones with anomalous performances. Wrong predictions may cause high-false-positives that compromise safety in national infrastructures. In this article, a physics informed reservoir computing (PIRC) scheme for drone trajectory prediction is proposed. The approach is comprised of two main complementary learning algorithms that enhance the prediction and generalization capabilities: 1) a standard reservoir computing scheme for high-dimensional encoding exploitation and 2) a nonlinear control scheme that gives a physical feedback to the reservoir weights to ensure the prediction error is minimized. The nonlinear control scheme is modeled by the prediction error dynamics and a feedback linearization controller. Two different PIRC schemes are proposed which preserve the reservoir properties and enhance the prediction robustness. Lyapunov stability theory is used to verify the boundedness and convergence of the proposed algorithms. Simulation studies and comparisons are given to verify the proposed approach.

A dynamic target tracking framework of UGV for UAV recovery under random disturbances

PurposeDynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.Design/methodology/approachTo mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.FindingsTo validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.Originality/valueThis paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

ORION: Verification of drone trajectories via remote identification messages

With the widespread adoption of drones in daily life, next-generation smart cities need to establish highways, i.e., trajectories where drones can fly and operate safely. However, due to the untrusted nature of their ecosystem, drones might misbehave and take disallowed trajectories, e.g., to reduce the time to fly to a destination, reduce energy consumption, visit unauthorized areas, or disrupt operations of sensitive sites. In this paper, we address the cited problem by proposing ORION, a new framework for online drone trajectory verification. ORION requires one or more receivers distributed in a given area capable of receiving and analyzing standard Remote Identification (RID) messages emitted by operational drones. ORION compares the locations reported in such messages with the closest set of coordinates in the allowed trajectory. It raises an alarm if the distance between such locations exceeds a threshold calibrated offline. We validate the performance of ORION through data collected from both a real drone flight in Amsterdam (Netherlands) and taxi trajectories in Porto (Portugal), achieving a True Positive Ratio (correct detection of disallowed trajectories) up to 0.95 and a False Positive Ratio (incorrect detection of disallowed trajectories) up to 0.04. Our solution significantly outperforms existing approaches used for drone detection or time-series analysis. Finally, we also release the gathered data as open-source to foster future research.

Beyond Static Obstacles: Integrating Kalman Filter with Reinforcement Learning for Drone Navigation

Autonomous drones offer immense potential in dynamic environments, but their navigation systems often struggle with moving obstacles. This paper presents a novel approach for drone trajectory planning in such scenarios, combining the Interactive Multiple Model (IMM) Kalman filter with Proximal Policy Optimization (PPO) reinforcement learning (RL). The IMM Kalman filter addresses state estimation challenges by modeling the potential motion patterns of moving objects. This enables accurate prediction of future object positions, even in uncertain environments. The PPO reinforcement learning algorithm then leverages these predictions to optimize the drone’s real-time trajectory. Additionally, the capability of PPO to work with continuous action spaces makes it ideal for the smooth control adjustments required for safe navigation. Our simulation results demonstrate the effectiveness of this combined approach. The drone successfully navigates complex dynamic environments, achieving collision avoidance and goal-oriented behavior. This work highlights the potential of integrating advanced state estimation and reinforcement learning techniques to enhance autonomous drone capabilities in unpredictable settings.

Research on Vision-Based Servoing and Trajectory Prediction Strategy for Capturing Illegal Drones

A proposed strategy for managing airspace and preventing illegal drones from compromising security involves the use of autonomous drones equipped with three key functionalities. Firstly, the implementation of YOLO-v5 technology allows for the identification of illegal drones and the establishment of a visual-servo system to determine their relative position to the autonomous drone. Secondly, an extended Kalman filter algorithm predicts the flight trajectory of illegal drones, enabling the autonomous drone to compensate in advance and significantly enhance the capture success rate. Lastly, to ensure system robustness and suppress interference from illegal drones, an adaptive fast nonsingular terminal sliding mode technique is employed. This technique achieves finite time convergence of the system state and utilizes delay estimation technology for the real-time compensation of unknown disturbances. The stability of the closed-loop system is confirmed through Lyapunov theory, and a model-based hardware-in-the-loop simulation strategy is adopted to streamline system development and improve efficiency. Experimental results demonstrate that the designed autonomous drone accurately predicts the trajectory of illegal drones, effectively captures them using a robotic arm, and maintains stable flight throughout the process.

Optimization of trucks and drones in tandem delivery network with drone trajectory planning

Drones are widely recognized as an efficient method for delivering small and lightweight parcels, such as medicines, to customers who are difficult to reach by logistic vehicles. Various studies and projects have been conducted to integrate drones into the traditional logistic service. In this approach, drones stay on trucks, take off from trucks at specific locations to make deliveries to customers, and return to the trucks to replenish and recharge. However, drone trajectories could overlap when multiple drones are used for delivery, which can lead to the risk of collisions. Consequently, the need to address the trajectory planning of drones becomes essential. This work introduces a novel truck-drone routing problem. The novelty presented by this problem lies in the integration of drone trajectory planning problems into the truck-drone routing to generate collision-free routes for drones. This concept has not been previously explored in the existing body of literature. The problem is formulated as a mixed-integer programming problem, and a matheuristic is proposed to solve it. Computational experiments show that the proposed matheuristic outperforms the commercial solver and genetic algorithm in terms of solution quality and search efficiency. Results demonstrate that the integrated truck-drone delivery mode has a lower operating cost than the mere truck mode, and including trajectory planning can prevent potential collisions.

A cyber-physical social system for autonomous drone trajectory planning in last-mile superchilling delivery

Logistics drones are theoretically advantageous in automating last-mile delivery activities, but practically challenging in urban environments, including public concerns about risk of crashes and privacy. In view of the above concerns existing in the trajectory planning of logistics drones, this paper presents a parallel automatic delivery model following the cyber-physical social system (CPSS) for the last mile delivery of superchilling products, revealing the social value of logistics drone operations. Considering the operational constraints of logistics drone, a multi-objective optimisation model is established to balance the social value, energy efficiency and productivity of using logistics drones in the last-mile superchilling delivery. To effectively achieve the above optimisation, improved strategies, including the material exchange mechanism based on the random proportion rule and the Universe-Particle search strategy, are developed, resulting in an improved two-stage heuristic algorithm. Finally, simulation experiments are carried out in a simulated environment with 154 buildings, and compared with other frontier algorithms used for unmanned aerial vehicle (UAV) trajectory planning. The results show that the proposed framework can effectively reduce the threats to public life safety and improve the energy efficiency of logistics drones, while ensuring the productivity in the delivery process.

Closed-Loop Output Error Approaches for Drone's Physics Informed Trajectory Inference

The design of adequate countermeasures against drone's threats needs accurate trajectory estimation to avoid economic damage to the aerospace industry and national infrastructure. As trajectory estimation algorithms need highly accurate physics informed models or off-line learning algorithms, radical innovation in on-line trajectory inference is required. In this paper, a novel drone's physics informed trajectory inference algorithm is proposed. The algorithm constructs a physic informed model and infers the drone's trajectories simultaneously using a closed-loop output error architecture. Two different approaches are proposed based on a physics structure and an admittance filtering model which considers: i) full states measurements and ii) partial states measurements. Stability and convergence of the proposed schemes are assessed using Lyapunov stability theory. Simulations studies are carried out to demonstrate the scope and high inference capabilities of the proposed approach.

Dispersal of blackbird flocks from sunflower fields: efficacy influenced by flock and field size but not drone platform

AbstractCrop depredation by blackbirds (Icteridae) results in substantial economic losses to the United States sunflower industry, and a solution to effectively reduce damage remains elusive. We evaluated the utility of uncrewed aircraft systems (UAS), or drones, as hazing tools to deter foraging blackbirds from commercial sunflower (Helianthus annuus) fields in North Dakota, USA, between September and October 2017. We compared the efficacy of 3 drones: a fixed‐wing predator model mimicking the form of an aerial raptor, a fixed‐wing airplane of similar size, and a multirotor drone. Multirotor drones are relatively easy to fly and are a multifunctional tool for agricultural use; however, they may not be an effective avian deterrent due to a lack of similarity in appearance with natural predators. Free‐ranging blackbird flocks (n = 58) reacted to every drone approach by initiating flight and took flight 1.6 times sooner for the fixed‐wing predator model (flight initiation distance [FID] = 90 m) and 1.8 times sooner for the fixed‐wing airplane (FID = 98 m) compared to the multirotor drone (FID = 55 m). However, the probability of a blackbird flock (n = 53) abandoning a field was greater with smaller field and flock sizes, rather than the specific drone deployed. In an applied setting, the performance of drones as avian hazing devices will likely depend on a combination of factors including platform selection, drone trajectory, duration of use, season, landscape context, and natural history of the pest species.

A qLPV-MPC Control Strategy for Trajectory Tracking of Quadrotors

This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution times, which can be suitable for real-time applications and for solving the optimization problem using quadratic programming (QP). The estimation of future values of the scheduling parameters along the prediction horizon is made by using the planned trajectory based on the previous optimal control actions. The performance of the proposed approach is tested by following different trajectories in simulation to show the effectiveness of the proposed control scheme.

DEVELOPING A THEORETICAL ASSESSMENT METHOD FOR AN ASSISTED DIRECT GEOREFERENCING APPROACH TO IMPROVE ACCURACY WHEN MAPPING OVER WATER: THE CONCEPT, POTENTIAL AND LIMITATIONS

Abstract. Drones offer a a unique survey platform that can operate below cloud cover and acquire very high spatial resolution datasets in near real-time. Studies have demonstrated that drones can be used for mapping over water using the Direct Georeferencing approach. However, this method is typically only feasible with high-end drones equipped with highly accurate GNSS/IMU systems. Moreover, placing targets over water to improve accuracy in post-processing can be challenging, further exacerbating this limitation. In this study, we developed an Assisted Direct Georeferencing method which combines the advantages of traditional Bundle Adjustment (BA) and Direct Georeferencing to overcome these challenges. Our approach utilizes BA over feature-rich segments of the drone trajectory, such as the shoreline, and DG in featureless areas, such as over water. To simulate a water-type environment or surface for our early tests, synthetic datasets have been created using Python for theoretical analysis. We then conducted a theoretical assessment of our approach under low and high variability attitude measurements. Our findings revealed that our methodology performs well under low variability attitude measurements, where wind conditions are close to optimal with an R-square value of 0.93. However, our model performs poorly under high variability attitude measurements, with an R-square value of only 0.028. These results suggest that Assisted Direct Georeferencing can serve as an alternative to high-end drones and Direct Georeferencing for water mapping applications in most standard. The findings from this theoretical assessment provide valuable insights into the achievable accuracy, error budgets, and limitations of the proposed model.

Trajectory Matters: Impact of Jamming Attacks Over the Drone Path Planning on the Internet of Drones

The Internet of Drones (IoD) is a network paradigm where drones will fly over well-defined airways. The Jamming Attack (JA) poses a severe risk to IoD, affecting the drone’s trajectory. Although JA has been investigated in UAV-based networks, the current solutions consider the airspace free to fly, whereas IoD allows drones to fly over constrained airspace, named airways. This study investigates the impact of JA over IoD, mainly regarding the drone path planning and, therefore, the drone trajectory. Also, we conduct a thorough discussion regarding the current challenges. To overcome these challenges, we propose the IoD-JAPM, an airway-aware protection mechanism against JA on the IoD, ranging from analyzing the airway’s availability to the potential reformulation of the drone path planning. We perform an experimental evaluation through simulations to compare IoD-JAPM with a baseline solution and an existing approach that considers the airspace free to fly, varying the IoD topology and the number of jammers in the environment. We analyze four different metrics to measure at what level IoD-JAPM can mitigate a JA. Through the results, we observe a relation between the impact of JA on the drone path planning and the robustness of the topology, where the more restricted the topology, the more impact JA causes. Also, IoD-JAPM mitigates the effects of JA on path planning, causing few reformulations or even flight cancellations. Specifically, IoD-JAPM prevents all drones from being attacked when the environment has one jammer and mitigates the attack at 30% for two jammers. Furthermore, IoD-JAPM overcomes the baseline study regarding the increasing drone flight distance, and power consumption. Since the drone propellers affect most of the drone battery, the more the flight distance the more the power consumption. Also, the results reinforce that “free-to-fly” approaches cannot be applied in airway-oriented environments with strong jamming signals. In a nutshell, IoD-JAPM represents a robust protection mechanism against JA considering the aerial space defined through airways, advancing the state of the art of IoD-based protection mechanisms against JA.

Trajectory Optimization for Drone Logistics Delivery via Attention-Based Pointer Network

Drone logistics delivery is a potential booster to redefine the logistics system efficiency, which has been a new special hot research field. Among that, how to optimize drone trajectory data and find optimal delivery path is a crucial problem. However, most existing studies fail to derive the feasible trajectory data due to underestimating drone energy consumption and delivery multi-variant constraints for air transportation. In this paper, we propose a novel self-driven learning procedure, named attention-based pointer network (A-Ptr-Net) model, which can solve drone delivery trajectory optimization problem. The generated A-Ptr-Net model coupled with attention mechanism is effective on adapting to the new drone trajectory data automatically, regardless of explicit distance matrix. We go into developing the convex function constraints related to drone nonlinear energy consumption, customer demand and service time windows, which is applied on A-Ptr-Net model for optimizing the drone logistics delivery. Numerical experiments indicate that the proposed method performs significantly better than classical heuristics for drone trajectory analysis and optimization.

Concentration map reconstruction for gas source location using nano quadcopters: Metal oxide semiconductor sensor implementation and indoor experiments validation

In this paper, we present a mobile gas monitoring system based on Nano-drones to map indoors gas distributions and to locate the gas source. The system combines a gas sensing unit of reduced size, weight, and consumption with a commercial Nano-drone. The sensing unit is outfitted with a metal oxide gas sensor for the detection of Volatile Organic Compounds (VOC). Several experiments were conducted in this study. A first set of experiments were devoted to characterizing the effect of the rotors on the sensor signals, commonly known as downwash. The experiments were designed to determine the speed and response intensity of the gas sensor placing the gas source at different heights above and below the drone. We repeated the experiments with the drone’s rotors turned on and turned off to compare the results. A more comprehensive set of experiments was conducted to test the monitoring system in gas distribution maps generation and source localization problems. Several scenarios were designed and tested with different drone trajectories, drone speeds, including one or two drones. Our results support the ideas that a good coverage of the area of interest combined with a moderate drone speed improves the accuracy of the gas concentration map as well as the accuracy of the estimation of the source location.

Receiver diversity with selection combining for drone communication around buildings at frequency 10 GHz

The communication network for cellular network keep development. This research analyzed about cellular network was used drone network. The mobile drone used frequency at 10 GHz for communication. The mobile drone moved around buildings. Buildings were used high variation. Base Station placed around building. This research was using macro diversity Base Station, variation building, variation modulation, and variation height of drone trajectory. Macro diversity mechanism used for that two Base Station. Selection Combining (SC) method was used for that macro diversity mechanism. The modulation communication based from Adaptive Modulation and Coding (AMC). Adaptive Modulation and Coding (AMC) was used Modulation and coding scheme (MCS). Modulation was used QPSK, 16 QAM, and 64 QAM. As the result described signal to noise ratio (SNR) at every node communication, probability MCS, and percentage coverage of drone trajectory. MCS probability for 64 QAM become increased with selection combining method. The percentages coverage of drone trajectory was obtained 77.2% of the first BS, 66.8% of the second BS, and 87.2% with SC method.

Optimization of Trucks and Drones in Tandem Delivery Network with Drone Trajectory Planning

Optimization of Trucks and Drones in Tandem Delivery Network with Drone Trajectory Planning

A New Bundle Picture for the Drone

A new <inline-formula><tex-math notation="LaTeX">$\mathrm{S}^{1}$</tex-math></inline-formula> principal bundle over a vector space with the origin removed is constructed for the drone with the yaw dynamics evolving on the <inline-formula><tex-math notation="LaTeX">$\mathrm{S}^{1}$</tex-math></inline-formula> fiber and the remaining part of drone dynamics on the base space. Two local trivializations are constructed to cover the whole bundle such that on each trivialization the drone dynamics is transformed via dynamic feedback to a linear controllable system with the yaw dynamics decoupled from the rest of the drone dynamics to make easy the design of drone trajectories and controls. The manifold <inline-formula><tex-math notation="LaTeX">$\mathrm{S}^{2}$</tex-math></inline-formula> often used for thrust directions is made obsolete by the new bundle.

The B-Scan Image Simulation Method of a Ground-Penetrating Radar Mounted on a Drone Using a High-Frequency Technique

A B-scan image simulation method of a ground-penetrating radar mounted on a drone using a high-frequency technique is proposed. The proposed simulation method aims to predict drone mounted-GPR receiving signals in a complex three-dimensional (3D) scenario, including the reflected signal from various ground types, backscattered signals from considered underground objects, and backscattered volume clutter signals from underground random object clusters. The modeled scenario, including various 3D drone trajectories, target objects, underground random object clusters, ground types, antenna radiation patterns, and desired transmitting time-domain short pulse signals, are taken into account. A high-frequency technique consisting of the geometrical optic (GO) ray-tracing technique to find the GO ray paths, and the GO ray field approximation technique to find the electromagnetic (EM) fields of every GO ray path, are employed instead of conventional numerical methods. This is useful for EM problems with large computational domains. The efficiency in terms of computational time and computer resources is better than that of numerical techniques. Finally, the effect of drone positioning errors, ground types, underground random object clusters, and the antenna radiation patterns on A-scan and B-scan images of a drone-mounted GPR will be illustrated. It is found that the distortion of the hyperbolic signature of underground target objects in the B-scan image occurred by unwanted back-scattered signals or radar clutter from the ground surface, volume clutter from underground random object clusters, and drone positioning errors. Moreover, a high directivity antenna enhances the intensity of the hyperbolic signature. The proposed simulation method will be useful for predicting the drone mounted-GPR signals in various complex 3D scenarios, having various kinds of transmitting signals, and target object configurations and antenna types.

Continuous aerial path planning for 3D urban scene reconstruction

We introduce the first path-oriented drone trajectory planning algorithm, which performs continuous (i.e., dense ) image acquisition along an aerial path and explicitly factors path quality into an optimization along with scene reconstruction quality. Specifically, our method takes as input a rough 3D scene proxy and produces a drone trajectory and image capturing setup, which efficiently yields a high-quality reconstruction of the 3D scene based on three optimization objectives: one to maximize the amount of 3D scene information that can be acquired along the entirety of the trajectory, another to optimize the scene capturing efficiency by maximizing the scene information that can be acquired per unit length along the aerial path, and the last one to minimize the total turning angles along the aerial path, so as to reduce the number of sharp turns. Our search scheme is based on the rapidly-exploring random tree framework, resulting in a final trajectory as a single path through the search tree. Unlike state-of-the-art works, our joint optimization for view selection and path planning is performed in a single step. We comprehensively evaluate our method not only on benchmark virtual datasets as in existing works but also on several large-scale real urban scenes. We demonstrate that the continuous paths optimized by our method can effectively reduce onsite acquisition cost using drones, while achieving high-fidelity 3D reconstruction, compared to existing planning methods and oblique photography, a mature and popular industry solution.