A novel adaptive event-triggered control strategy is proposed for multi-quadrotor systems under intermittent communications, addressing the leader-follower consensus-seeking problem where the leader has an unknown bounded input. Firstly, an activation time ratio condition is proposed, eliminating the reliance on the maximum time interval of intermittent communication. Secondly, a compensation term related to the leader’s unknown bounded input is designed in the controller to compensate for the error caused by intermittent communication in each period. Meanwhile, a prediction method is developed to eliminate the dependence on continuous information of neighboring quadrotors. Zeno behavior is strictly excluded, and communication among quadrotors is efficiently reduced with the designed event-triggering condition. Finally, numerical simulations verify the effectiveness and superiority of the proposed control strategy.
Unmanned Aerial Vehicles (UAVs) are versatile platforms with potential applications in precision agriculture, disaster management, and more. A core need across these applications is a navigation system that accurately estimates location based on environmental perception. Commercial UAVs use multiple onboard sensors whose fused data improves localization accuracy. The bioinspired Rat-Simultaneous Localization and Mapping (Rat-SLAM) system, is a promising alternative to be explored to tackle the localization and mapping problem of UAVs. Its cognitive capabilities, semi-metric map construction, and loop closure make it attractive for localization in complex environments. This work presents an improved Rat-SLAM algorithm for UAVs, focusing on three innovations. First, Spiking Neural Networks (SNNs) are incorporated into Rat-SLAM’s core modules to emulate biological processing with greater efficiency. Second, Neuromorphic Computing models the neurons of the SNNs, assessing the feasibility of implementing SNNs on specialized hardware to reduce software processing, a key advantage for UAVs with limited onboard resources. Third, SNNs are developed based on the Memristive Leaky Integrate-and-Fire model, integrating memristors into artificial neurons to leverage their low power and memory properties. Our approach was evaluated through trajectory simulations using the Hector Quadrotor UAV in the Gazebo environment within the Robot Operating System, yielding valuable insights and guiding future research directions.
Due to the complex geometry of the monuments, it is often necessary to adapt the image collection process for their mapping. For the optimal mapping of the stronghold of Lazaritsa Chorygi (Greece) and its slopes, vertical, inclined, and horizontal images from different heights were collected using an Unmanned Aircraft System. Thus, for a monument of special archaeological/historical interest and natural beauty, a large set of high-spatial resolution data and final products (digital surface model and orthophotomosaic with spatial resolution 5.6 cm and 2.8 cm, respectively) is available. In addition, in the wider area of the fortified site, military structures (fire trenches, communication trenches, shelters, front and support trenches, and strong points) of the Great War length of 9 km were identified and mapped, which were identified in the 2003 or 2004 Google Earth Pro images, but worryingly are almost absent from the contemporary Google Earth Pro images.
The behaviour of co-axial rotors is well understood, and they are especially practical for large UAVs due to their increased thrust without changing the vehicle footprint. However, for co-axial systems with varying propeller diameters between the two disks, research is more limited. The goal of this paper was to determine an optimal configuration for several different unequal co-axial setups using numerous different propeller combinations and separation ratios. Propellers with diameters of 26 and 29 inches are tested at separation ratios of 0.05 to 0.35. Thrust and power were collected using an off-the-shelf FS15-TYTO thrust stand, with the upstream and downstream propellers running at equal throttles. From this, performance was assessed through efficiency, thrust, and power consumption, and comparisons were made to an ideal combination without losses. The results show that for unequal combinations, the user should place the smaller propeller upstream for greater efficiency, but for maximum thrust capacity, two equal propellers are preferred. When compared to two independent rotors of the same size, a 26″ upstream rotor and a 29″ downstream rotor minimised thrust loss to 16%, compared to 23% for the opposite arrangement. It was also found that the optimal separation ratio is always approximately 0.2.
This paper addresses the finite-time stabilization problem for a nonholonomic wheeled mobile robot (NWMR) with input constraints. By utilizing the hyperbolic tangent function tanh(·), bounded finite-time stabilization controllers are developed. In addition, an explicit upper-bound estimate for the closed-loop settling time is given, and the level of input constraints is characterized by parameters that depend on the actuator’s capacity. A thorough finite-time stability analysis is carried out using appropriate Lyapunov functions. For a compact set contained in the domain of attraction, a guideline is presented to clarify how to construct it. Finally, simulation results show the effectiveness of the developed controllers.
In this paper, the distributed leader-follower consensus of a group of agents with second-order dynamics under the undirected graph communication topology is studied. The main objective of this study is to solve a major practical multi-agent problem in which the acceleration of the leader is not communicated to each follower. In contrast, the follower agents include some unknown dynamics in their intrinsic structure. By assuming a linear regression structure for leader acceleration and agent’s unknown dynamics, Lyapunov-based adaptive control algorithms are devised to control the network of agents in the presence of the communication loss and modeling uncertainties. The presented study describes two multi-agent control strategies called fully-distributed adaptive control (FDAC) and partially-distributed adaptive control (PDAC) systems in the first method, the followers do not have any a priori information about the communication graph, while in the second method, some information about the eigenvalues of the communication graph is available. The mathematical manipulations required to prove the stability of the FDAC and PDAC methods are presented. Finally, illustrative simulations are conducted to render the proposed algorithms’ merits and efficiencies.
In response to the ever-growing global demand for Unmanned Aerial Vehicles, efficient battery solutions have become vital. This paper proposes a design and concept of an Autonomous Mid Air Battery Swapping System for Vertical Take-Off and Landing Unmanned Aerial Vehicles. The proposed design integrates Aerial Mechatronics, Lighter than Air Systems, and Digital Modelling by leveraging the innovative concept of aerostats for battery swapping. This adaptive and effective technology paves the way for the next generation of autonomous Vertical Take-Off and Landing, ensuring a longer flight time and range. Modern-day technologies have empowered Unmanned Aerial Vehicles to operate autonomously and be remotely controlled, expanding their utility across diverse industries. The enhanced Vertical Take-Off and Landing capabilities include the ability to dock on an aerostat-mounted system, facilitating seamless battery swapping without human intervention and ensuring extended flight duration and operational flexibility. These advancements promise to broaden the applications of Unmanned Aerial Vehicles across various industries.
This paper proposes a distributed reinforcement learning method for multi-robot cooperative target search based on policy gradient in 3D dynamic environments. The objective is to find all hostile drones which are considered as targets with the minimal search time while avoiding obstacles. First, the motion model for unmanned aerial vehicles and obstacles in a dynamic 3D environments is presented. Then, a reward function is designed based on environmental feedback and obstacle avoidance. A loss function and its gradient are designed based on the expected cumulative reward and its differentiation. Next, the expected cumulative reward is optimized by a reinforcement learning algorithm that makes the loss function update in the direction of the gradient. When the variance of the expected cumulative reward is lower than a specified threshold, the unmanned aerial vehicle obtains the optimal search policy. Finally, simulation results demonstrate that the proposed method effectively enables unmanned aerial vehicles to identify all targets in the dynamic 3D airspace while avoiding obstacles.
Amid a global metacrisis of health, environmental and economic challenges, medical delivery drones (or uncrewed aerial vehicles) offer a promising method to prepare for, and rapidly respond, to future emergencies. This opinion article summarizes the current medical delivery drone landscape, evidence base, and policy implications in the context of public health emergencies, such as pandemics, natural disasters, and humanitarian crises, with a particular emphasis on the region of sub-Saharan Africa. Using a multilateral, international health policy perspective, key challenges and opportunities, such as the development of sustainable funding mechanisms, robust regulatory frameworks, and capacity building, are identified.
This paper, intended for expert and non-expert audiences, evaluates the technical and regulatory requirements for Unmanned Aerial Systems (UAS) to operate beyond visual line of sight (BVLOS) services. UAS BVLOS operations have the potential to unlock value for the industry. However, the regulatory requirements and process can be complex and challenging for UAS operators. The work explored the BVLOS regulatory regime in the UK, Europe and the US and found similarities in process and requirements covering themes like Detect and Avoid (DAA), Remote identification and Reliable Connectivity. A unifying goal across these jurisdictions is to operate BVLOS safely and securely in non-segregated airspace. However, operating BVLOS in segregated airspace as the default or routine mode could accelerate approval and adoption. The paper reviewed existing challenges, highlighting Coverage, Capacity and Redundancy as critical for UAS BVLOS Operations. The work also highlighted the crucial role of Non-terrestrial Network (NTN) assets like Satellites and HAPS (High Altitude Platform Station) since terrestrial networks (not optimised for aerial platform coverage) may not be reliable for BVLOS connectivity.
