Efficient chemical weed management considers precise application of herbicides, maximizing herbicide retention and absorption, reducing the impact of abiotic factors, and mitigating off-target movement in order to optimize herbicide efficacy. Hence, this study assessed the employability and cost-efficiency of an unmanned aerial vehicle (UAV) for pre-planting application and post-emergence selective grass weed control infesting legume cover crops (LCCs) in immature oil palm (Elaeis quineensis Jacq.) plantation. Field experiments were conducted in 2020 and 2021 at a research center and an oil palm replanting area in Jerantut, Pahang, Malaysia. Droplet deposition and distribution analyses revealed that the pressure at 0.25 MPa yielded better spraying coverage and increased droplet counts compared to 0.15 MPa. For pre-planting application, both UAV and mist blower resulted in total weed control. Meanwhile for selective grass control in the LCCs, conventional knapsack sprayer (CKS) application provided slightly better weed control than the UAV over the 12 weeks observation. However, cost-efficiency analysis revealed that UAV spraying yielded economically favorable for areas over 3,000 hectares, with potential savings ranging from 4% to 28%. Furthermore, UAV spraying demonstrated superior operational efficiency and reduced working hours by 37%, water consumption by 91%, and human labor expenses by 81% compared to both conventional methods. These findings underscore the potential of UAV-based spraying for large-scale weed control in oil palm plantations and highlight its efficiency, comparable effectiveness, and cost-saving benefits.

Path planning for the unmanned aerial vehicle (UAV) is to assist in finding the proper path, serving as a critical role in the intelligence of a UAV. In this paper, a path planning for UAV in three-dimensional environment (3D) based on enhanced gravitational search algorithm (EGSA) is put forward, taking the path length, yaw angle, pitch angle, and flight altitude as considerations of the path. Considering EGSA is easy to fall into local optimum and convergence insufficiency, two factors that are the memory of current optimal and random disturbance with chaotic levy flight are adopted during the update of particle velocity, improving the balance between exploration and exploitation for EGSA through different time-varying characteristics. With the identical cost function, EGSA is compared with seven peer algorithms, such as moth flame optimization algorithm, gravitational search algorithm, and five variants of gravitational search algorithm. The experimental results demonstrate that EGSA is superior to the seven comparison algorithms on CEC 2020 benchmark functions and the path planning method based on EGSA is more valuable than the other seven methods in diverse environments.

From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.

The present paper aims to design and simulate an adversarial strategy where a swarm of quadrotor UAVs is herding anti-aircraft land vehicles (AALV) that actively oppose the swarm’s objective by potentially taking them down. The main strategy is to block the AALVs’ line of sight to their goal zone (AALVs’ objective), shifting its trajectory so it reaches a kill zone instead (UAVs’ objective). The counter-swarm strategy performed by the AALVs consists of taking down the closest aerial units to the goal zone. As a result, a consensus algorithm is executed by the UAVs in order to assess the communication network and re-group. Consensus is based on the propagation of local observations that converge into a global agreement on a communication graph. Re-grouping is done via positioning around the kill zone vector or preferring an anti-clockwise formation to better close gaps. The adversarial strategy was tested in an empty arena and urban setting, the latter making use of a path-planning procedure that re-routes the AALV trajectory based on its current destination. Simulation results show a maximum UAV mission success rate converging to roughly 80% in the empty arena. When targeted elimination procedures are executed, UAV mission performance drops 5%, making no distinction between re-grouping strategies in the empty arena. The urban setting shows lower performance due to navigation complexity but favors the decision to re-group based on a formation that close gaps rather than positioning around the kill zone vector.

In the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.

Unmanned Aerial Vehicles (UAVs) have been used extensively in many applications, such as surveillance, medical transportation and delivery tasks. These applications usually involve an attachment system to connect a payload. However, when connected to such a coupled system, the UAV exhibits lower flight performance mainly due to gust loads. Mathematical modeling is used in this paper to illustrate the comparison between an UAV with a coupled system and a conventional UAV. Proportional Derivative (PD) and Sliding Mode Control (SMC) approaches are used to evaluate the vibrations of the payload, and the UAV trajectory subjected to Dryden gust. This work therefore uses these methods to investigate the dynamic response of a quadrotor-type UAV equipped with a payload and a flexible attachment system.

The mass proliferation and increasing affordability of unmanned aerial vehicles (UAVs) in recent years has given rise to weaponized UAV use by terrorists, leading to mounting and credible concerns this attack methodology will be the next terrorism modus operandi. Counter-Terrorism Medicine (CTM) specialists need to consider how UAVs alter or create new mass-casualty scenarios that can further exploit existing medical preparedness vulnerabilities. With an opportunity to be proactive in disaster prevention, mitigation, and preparedness, it is imperative this gathering storm be acknowledged and stakeholders explore how best to prepare for, respond to, and mitigate the consequences of UAV incidents.

Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

Efficient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower-level data interfaces, all the way down to sensors and actuators. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision-making, several key issues remain open. One of these issues is the selection, coordination, and decision-making related to the several specialized tasks required for fulfilling mission objectives. This work addresses decision-making for the cognitive unmanned-aerial-vehicle architecture coined as ARCog. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of the state-of-the-art technology in the field. The system is designed to provide high-level decision-making. Experiments prove that ARCog works correctly in its target scenario.

This study presents the first subglacial topography and ice thickness models of the largest ice caps of the Argentine Islands, Wilhelm Archipelago, West Antarctica. During this study, ground-penetrating radar was used to map the thickness and inner structure of the ice caps. Digital surface models of all studied islands were created from aerial images obtained with a small-sized unmanned aerial vehicle and used for the construction of subglacial topography models. Ice caps of the Argentine Islands cover ~50% of the land surface of the islands on average. The maximum thickness of only two islands (Galindez and Skua) exceeds 30 m, while the average thickness of all islands is only ~5 m. The maximum ice thickness reaches 35.3 m on Galindez Island. The ice thickness and glacier distribution are mainly governed by prevailing wind direction from the north. This has created the prominent narrow ice ridges on Uruguay and Irizar islands, which are not supported by topographic obstacles, as well as the elongated shape of other ice caps. The subglacial topography of the ice caps is undulated and mainly dependent on the geological structure and composition of magmatic rocks.

Quantifying the abundance of species is essential for their management and conservation. Much effort has been invested in surveys of freshwater dolphins in the Amazon basin but river dimensions and complex logistics limit replication of such studies across the region. We evaluated the effectiveness of using unmanned aerial vehicles (UAVs) for surveying two Amazon dolphin species, the tucuxi Sotalia fluviatilis and pink river dolphin Inia geoffrensis, in tropical rivers. In 2016 we conducted drone and visual surveys over 80 km of the Juruá River in Brazil. The aerial surveys provided higher accuracy than human observers in counting individuals detected in groups. Compared to estimates derived from visual surveys, the use of UAVs could provide a more feasible, economical and accurate estimate of Amazon river dolphin populations. The method could potentially be replicated in other important areas for the conservation of these species, to generate an improved index of river dolphin populations in the Amazon.

Supraglacial streams are a significant part of the glacial hydrological system and important for understanding glacial hydrology and dynamics. Here we infer factors that influence the long-term development of perennial supraglacial streams, particularly in reference to canyon, incised and surface stream formation. Orthophotos and digital elevation models generated from high-resolution aerial imagery taken with unmanned aerial vehicles or piloted helicopters between 2010 and 2017 were used to compare seven streams on Fountain Glacier, Bylot Island, Canada over time. Results show canyon formation occurs from a combination of fluvial processes and the impact of solar radiation. The greater the discharge or slope, the faster the incision and higher the sinuosity. With greater sinuosity, the distance between the top of the valley banks increases, and cutoffs cause trapezoidal canyon-like valleys to form. Solar radiation causes the backward migration of the valley walls, further enhancing canyon area. Canyons are less likely to occur in areas of low discharge and slope. Less incised channels are also more likely to have water flow jumping the channel banks, changing the channel path. The presence of medial moraines and crevasses also increases rerouting of small streams. Lastly, windblown created snow-plugs may lead to stream diversion.

In this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.

Effective technical methods for verifying the authenticity and accuracy of Automatic Identification System (AIS) data, which are important for safe navigation and traffic regulation, are still lacking. In this study, we propose a new method to verify AIS data by using video images taken by an Unmanned Aerial Vehicle (UAV). An improved ViBe algorithm is used to extract the ship target image from the video images and the ship's spatial position is calculated using a monocular target-positioning algorithm. The positioning results are compared with the position, speed and course data of the same ship in AIS, and the authenticity and accuracy of the AIS data are verified. The results of the experiment conducted in the inland waterways of Huangpu River in Shanghai, China, show that AIS signals can be automatically checked and verified by a UAV in real time and can thus improve the supervision efficiency of maritime departments.

Autonomous landing on the deck of a boat or an unmanned surface vehicle (USV) is the minimum requirement for increasing the autonomy of water monitoring missions. This paper introduces an end-to-end control technique based on deep reinforcement learning for landing an unmanned aerial vehicle on a visual marker located on the deck of a USV. The solution proposed consists of a hierarchy of Deep Q-Networks (DQNs) used as high-level navigation policies that address the two phases of the flight: the marker detection and the descending manoeuvre. Few technical improvements have been proposed to stabilize the learning process, such as the combination of vanilla and double DQNs, and a partitioned buffer replay. Simulated studies proved the robustness of the proposed algorithm against different perturbations acting on the marine vessel. The performances obtained are comparable with a state-of-the-art method based on template matching.