In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm.

To improve the cooperative search and attack effectiveness of multiple heterogeneous unmanned aerial vehicles (UAVs) in unknown environment, a novel coalition formation method with communication constraints is presented in this paper. First, the coalition formation model is established on the basis of minimizing the target attack delay and minimizing the coalition size with the constraint of required resources and simultaneous strike. Second, considering communication constrains such as limited communication ranges and communication delays, a mechanism was developed in order to find the potential coalition members within a maximum number of hops over a dynamic UAV network. Third, to reduce the huge computational complexity in coalition formation optimization solution we propose a Multistage Sub-Optimal Coalition Formation Algorithm (MSOCFA) with low computational complexity. Furthermore, in order to enable multiple cooperative UAVs accomplish the search and prosecute missions autonomously, a distributed autonomous control strategy is proposed which is based on the Finite-State Machine (FSM). Comparison simulations are carried out to demonstrate that how the potential coalition members finding technique impact on the coalition achieved by MSOCFA. The effects of number of maximum allowed hops for a message and hop delay are studied by employing Monte-Carlo method. The experimental reveals that, in the cases of large communication delay, forming a coalition from the immediate neighbors is sufficient for a good performance in term of the mission completion time. Under smaller delays, including neighbors up to a few hops will increase performance, and any additional increase in hop count will degrade performance.

In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability.

This paper investigates the problem of cooperative standoff tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) with control input constraints. In order to achieve accurate target tracking in the presence of unknown background wind and target motion, a coordinated standoff target tracking algorithm is proposed. The objective of the research is to steer multiple UAVs to fly a circular orbit around a moving target with prescribed intervehicle angular spacing. To achieve this goal, two control laws are proposed, including relative range regulation and space phase separation. On one hand, a heading rate control law based on Lyapunov guidance vector field is proposed. The convergence analysis show that the UAVs can asymptotically converge to a desired circular orbit around the target, regardless of their initial position and heading. Through rigorous theoretical proof, it is concluded that the command signal of the proposed heading rate controller will not violate the boundary constraint on heading rate. On the other hand, a temporal phase is introduced to represent the phase separation and avoid discontinuity of the wrapped space phase angle. On this basis, a speed controller is developed to achieve equal phase separation. The proposed airspeed controller met the requirement of airspeed constraint. In addition, to improve the robustness of the aircraft during target tracking, an estimator is developed to estimate the composition velocity of the unknown wind and target motion. The proposed estimator uses the offset vector between the UAV’s actual flight path and the desired orbit which is defined by Lyapunov guidance vector field to estimate the composition velocity. The stability of the estimator is proved. Simulations are conducted under different scenarios to demonstrate the effectiveness of the proposed cooperative standoff target tracking algorithm. The simulation results of indicate that, the temporal-phase-based speed controller can achieve fast convergence speed and small phase separation error. Additionally, the composition velocity estimator exhibits fast response speed and high estimation accuracy.

To improve the effectiveness of multiple heterogeneous unmanned aerial vehicles (UAVs) cooperative with each other as a team to search and prosecute targets in unknown environment, a novel coalition formation method is presented in this paper. First, the coalition formation model is established based on minimizing the target prosecution delay and the size of the coalition with the constraint of required resources and simultaneous strike. Second, since solving the coalition formation optimization problem is computationally intensive, we develop a multistage sub-optimal coalition formation algorithm that has low computational complexity. Third, in order to enable multiple cooperative UAVs accomplish the search and prosecute missions autonomously, a distributed autonomous control strategy is proposed which is based on the finite state machine. The simulation result of a scenario shows the rationality, validity and high real-time performance of the method of coalition formation in multiple heterogeneous UAVs cooperative search and prosecutes in the unknown environment. Monte Carlo method is employed to validate the impact of the number of UAVs and targets on the performance of the coalition formation algorithm.

Human-System systems design is a critical part of Command and Control (C2) design, in order to improve the whole effectiveness of C2 design, the human beings and systems must be in harmony in the human-in-loop systems, which can be referred to as Cyber-physical-social systems (CPSS). Based on the coordination theory, self synchronization of a CPSS is both a self-organizing and self-adapting process, of which the end effect will be shown as the emergence of all the dependency relationships among the courses of action (COAs). Human-system systems design for C2 will be modeled in different views: the system view, the human view and the coordination view, being a loop iteration process.