Distributed Consensus of USVs under Heterogeneous UAV-USV Multi-Agent Systems Cooperative Control Scheme

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.

This work addresses the distributed predefined-time cooperative formation of heterogeneous multiagent systems comprising unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) with inherent dynamic uncertainties. By transforming the model coordinates, the underactuated heterogeneous USV-UAV systems can be converted into a fully actuated second-order multiagent framework. Subsequently, a predefined-time dynamic observer is designed to estimate the uncertain dynamics of each agent. Combining the backstepping method and the virtual leader model, a predefined-time distributed cooperative formation control based on uncertain dynamic estimation is designed for the heterogeneous USV-UAV systems. The convergence of the formation errors is rigorously demonstrated by constructing a suitable Lyapunov function under a predefined time framework. Ultimately, the two numerical cases in both the fixed and time-varying formation scenarios confirm the effectiveness of the constructed method.

This paper provides both the core ideas of a heterogeneous marsupial robotic System and the details of the practical aspects related to its hardware architecture and cooperative system structure. This heterogeneous marsupial robotic system is composed by an unmanned surface vehicle (USV) that acts as a carrier and an unmanned aerial vehicle (UAV) that acts as a passenger, and it is designed for military tasks and other tasks like environmental monitoring, wild life tracking, and search & rescue missions. Through the combination, the cooperative marsupial robots benefit from their heterogeneity by using the superiority of each team member and overcoming their limitations. Moreover, the attitude control simulation model of the UAV is built, and the self-adaptive fuzzy parameter tuning rules for PID flight controller are given, so as to realize the online self-tuning of the controller parameters. Simulation results show that: compared with the conventional PID controller, this attitude control algorithm of fuzzy self-adaptive PID has a better dynamic response performance.

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

In this article, for a marine aerial-surface heterogeneous (MASH) system composed by a quadrotor unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV) with heterogeneity, completely unknown dynamics and disturbances, the accurate trajectory-tracking problem is solved by creating a novel coordinated trajectory-tracking control (CTTC) scheme. A family of coordinate transformations are devised to convert the MASH system tracking error dynamics into translation–rotation cascade manners, whereby the heterogeneity is removed and finite-time observers for complex unknowns are facilitated. In conjunction with sliding mode based rotation error dynamics, distributed tracking controllers for the quadrotor UAV and the USV are independently synthesized such that cascade tracking error dynamics are globally asymptotically stable. With the aid of cascade and Lyapunov analysis, the entire CTTC solution to the accurate trajectory-tracking problem of the MASH system is eventually put forward. Simulation results and comprehensive comparisons on a prototype MASH system demonstrate the effectiveness and superiority of the proposed CTTC scheme.

Recently, unmanned aerial vehicle (UAV)-assisted maritime communication systems have drawn considerable attention due to their potential for broadband maritime communication applications. However, their limited energy resources remains a critical issue in providing long-term data transmission support for maritime applications. In this study, an integrated sea–air–terrestrial communication system was constructed for marine data collection, where several unmanned surface vessels (USVs) are deployed to collect marine data from underwater sensors (UWSs), and a UAV hovers above these USVs as a relay node, transmitting marine data from USVs to an onshore base station (BS). To prolong the lifetime of the UAV relay, mobile edge computing technology is applied in USVs for partial data computing, which reduces the to-be-relayed data volume from UAVs to USVs to onshore BS as well as the relay energy consumption of UAV. A parallel data computing and transmission scheme was developed for simultaneous local data computing and relaying in the proposed system. Accordingly, a UAV energy consumption minimization problem was formulated with constraints on the USV’s computational ability, the USV’s transmission power budget, the UAV transmission power budget, and the maximum system latency. To effectively solve this nonconvex optimal problem, an energy optimal partial data computing and relaying strategy was constructed by successively optimizing the data partial computational offloading ratio, USV transmit power allocation, and UAV transmit power. Numerical simulations were used to verify the effectiveness of the proposed strategy.

The tragic Deepwater Horizon accident in the Gulf of Mexico in 2010 as well as the increase in deepwater offshore activity have increased public interest in counter-measures available for sub-surface releases of hydrocarbons. To arrive at proper contingency planning, response managers urge for a system for instant detection and characterization of accidental releases. Along these lines, this paper describes the application of a heterogeneous robotic system of unmanned vehicles: autonomous underwater vehicle (AUV), unmanned surface vehicle (USV) and unmanned aerial vehicle (UAV) extended with the oil spill numerical modeling, visualisation and decision support capabilities. A first set of field experiments simulating oil spill scenarios with Rhodamine WT was held in Croatia during the early autumn 2014. The objectives of this experiment were to test: effectiveness of the system for underwater detection of hydrocarbons, including multi-vehicle collaborative navigation and communication as well as visualisation of the system components.

To guarantee the stability and performance of an overall heterogeneous multi-agent system, feedback control structures must be capable of addressing the presence of heterogeneous agent-based nonlinear uncertainties, unknown actuation performance, and coupling effects. Additionally, some tasks require agents to track the leader's state and reach different states in a distributed way. Therefore, this paper proposes a distributed adaptive controller that drives a set of agents to user-assigned positions in the presence of the mentioned heterogeneous system anomalies. To address actuator dynamics, a hedging-based reference model is used to decouple adaptive updating from the actuator dynamics. To deal with the coupled dynamics, an observer-based estimation algorithm is proposed. Finally, the low-frequency learning method is used to deal with the high-frequency control response and, therefore, to obtain a better transient performance. The overall closed-loop system's stability is investigated using the Lyapunov Stability Theory, and the Linear Matrix Inequalities (LMI) technique is employed to identify stability limits regarding the actuator bandwidths of each agent. Illustrative numerical examples are presented to demonstrate the designed controller's performance in a heterogeneous uncertain multi-agent system's dynamics with unknown actuation capabilities and coupled dynamics.

The identification and classification of obstacles in navigable and non-navigable regions, as well as the measurement of distances, are crucial topics of investigation in the field of autonomous navigation for unmanned surface vehicles (USVs). Currently, USVs mostly rely on LiDAR and ultrasound technology for the purpose of detecting impediments that exist on water surfaces. However, it is worth noting that these approaches lack the capability to accurately discern the precise nature or classification of those obstacles. Nevertheless, the limited optical range of unmanned vessels hinders their ability to comprehensively perceive the entirety of the surrounding information. A cooperative USV-UAV system is proposed to ensure the visual perception ability of USVs. The multi-object recognition, semantic segmentation, and obstacle ranging through USV and unmanned aerial vehicle (UAV) perspectives are selected to validate the performance of a cooperative USV-UAV system. The you only look once-X (YOLOX) model, the proportional–integral–derivative-NET (PIDNet) model, and distance measurements based on a monocular camera are utilized to realize these problems. The results indicate that by integrating the viewpoints of USVs and UAVs, a collaborative USV-UAV system, employing the aforementioned methods, can successfully detect and classify different objects surrounding the USV. Additionally, it can differentiate between navigable and non-navigable regions for unmanned vessels through visual recognition, while accurately determining the distance between the USV and obstacles.

With the rapid development of marine networks, there have been growing demands for computation-intensive and delay-sensitive marine applications and services. However, the limited underwater energy supply and the acoustic channels result in the low efficiency for computing tasks and high transmission delay. In this paper, we investigate the unmanned aerial vehicles (UAVs)-assisted multi-access computation offloading in marine networks, with the objective of minimizing the energy consumption of ocean devices. Specifically, for the underwater segment, we consider the scenario that multiple underwater sensor nodes (USNs) covered by the unmanned surface vehicle (USV) upload their sensing data via non-orthogonal multiple access (NOMA) for improving the channel utilization. For the radio frequency segment, we consider the scenario that multiple UAVs hovering in the air act as the aerial edge servers for providing computing services, in which the USV offloads the workloads to UAVs via frequency division multiple access (FDMA) for avoiding their co-channel interference, while taking into account that a malicious node overhears the USV's offloading transmission. To improve the computation offloading efficiency, we formulate an optimization problem for USNs and USV to minimize the total energy consumption by jointly optimizing the USN's uploading time, USV's computation offloading, USV's offloading time, and the secrecy provisioning. Despite the non-convexity of the formulated joint optimization problem, we exploit a layered structure to decompose the problem, and propose efficient algorithms to obtain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms. Numerical results demonstrate that our algorithms can significantly reduce the energy consumption in comparison with the benchmark schemes.

For heterogeneous unmanned systems composed of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), using UAVs serve as eyes to assist UGVs in motion planning is a promising research direction due to the UAVs' vast view scope. However, its limitations on flight altitude prevent the UAVs from observing the global map. Thus motion planning in the local map becomes a Partially Observable Markov Decision Process (POMDP) problem. This paper proposes a motion planning algorithm for heterogeneous unmanned systems under partial observation from UAV without reconstruction of global maps. Our algorithm consists of two parts designed for perception and decision-making, respectively. For the perception part, we propose the Grid Map Generation Network (GMGN), which is used to perceive scenes from UAV's perspective and classify the pathways and obstacles. For the decision-making part, we propose the Motion Command Generation Network (MCGN). Due to the addition of the memory mechanism, MCGN has planning and reasoning abilities under partial observation from UAVs. We evaluate our proposed algorithm by comparing it with baseline algorithms. The results show that our method effectively plans the motion of heterogeneous unmanned systems and achieves a relatively high success rate.

This paper studies the formation control problem of a heterogeneous multi-agent system (HMAS) consisting of an unmanned ground vehicle (UGV) and multiple unmanned aerial vehicles (UAV). A heterogeneous formation platform based on the open-source autopilot pixhawk (PX4) and QGroundControl ground station (QGC) consisting of a rover and two quadrotors is introduced. With UGV as the leader and UAV as the follower, a formation control scheme based on leader-follower is adopted. To achieve the desired formation configurations, a sliding mode control algorithm using combined reaching law is designed so that follower-quadrotors can track leader-rover quickly and stably. Finally, the effectiveness of the theoretical results is verified by indoor formation experiment.KeywordsHeterogeneous formation platformFormation controlLeader-followerSliding mode control

This article proposes a distributed adaptive fault-tolerant formation-containment control with prescribed performance for heterogeneous multiagent systems (MASs) consisting of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. First, utilizing the neighborhood formation error information, the distributed fault-tolerant formation control strategy is developed for the trajectory dynamics of each UAV to achieve the formation tracking, that is, all UAVs track the virtual leader and perform the prespecified formation configuration. Then, the adaptive fault-tolerant containment algorithm, independent of the positions of the leaders, is proposed to guarantee the UGVs converge to the convex hull formed by the leader UAVs. The adaptive estimation scheme is constructed to compensate for the unknown system parameters and actuator loss-of-effectiveness and bias faults. The formation-containment tracking performance is analyzed based on Lyapunov theory with the synchronization errors satisfying the prescribed performance. A simulation example based on UAVs-UGVs systems is adopted to verify the effectiveness of the proposed control strategy.

An autonomous unmanned surface vehicle (USV) landing system of vertical takeoff and landing (VTOL) fixed-wing unmanned aerial vehicle (UAV) based on raptor vision is presented in this paper. The color saliency detection method based on raptor vision in the biological raptor vision color resolution space is developed to obtain the landing target and markers in the mission scene. A three-color nested landing target scheme is designed to accommodate the different height of UAV. Considering the various interferences exist in the complex environment, the target occlusion strategy and the go-around (GA) strategy are supplemented in the vision navigation system to improve the accuracy and security of the landing mission. The complete UAV/USV flight verification system is developed to validate the effectiveness of the proposed method. Results show that the autonomous landing system based on raptor vision obtain a quite satisfactory landing outcome. The horizontal errors of UAV autonomous landing are both less than 5cm. The success rate of UAV landing has been greatly improved. Therefore, the proposed autonomous landing system provides a feasible technical pathway for UAV landing on a USV.