Robotic fleets can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. However, it can be demanding or even impractical for an operator to directly control each robot. Thus, autonomy of the fleet and its online interaction with the operator are both essential, particularly in dynamic and partially unknown environments. The operator might need to add new tasks, cancel some tasks, change priorities and modify planning results. How to design the procedure for these interactions and efficient algorithms to fulfill these needs have been mostly neglected in the related literature. Thus, this work proposes a human-centric coordination and supervision scheme (HECTOR) for large-scale robotic fleets under continual and uncertain temporal tasks. It consists of three hierarchical layers: (I) the bidirectional and multimodal protocol of online human-fleet interaction, where the operator interacts with and supervises the whole fleet; (II) the rolling assignment of currently-known tasks to teams within a certain horizon, and (III) the dynamic coordination within a team given the detected subtasks during online execution. The overall mission can be as general as temporal logic formulas over collaborative actions. Such hierarchical structure allows human interaction and supervision at different granularities and triggering conditions, to both improve computational efficiency and reduce human effort. Extensive human-in-the-loop simulations are performed over heterogeneous fleets under various temporal tasks and environmental uncertainties.

The Artificial Internet of Things (AIoT) is growing into a new frontier field with broad development prospects, which essence is the collaborative enhancement of networked heterogeneous agent swarms. The market-based approach is an effective way for the cooperative scheduling of agent swarm, where networked agents need to distributedly select and arrange tasks meeting the spatio-temporal constraints. This paper proposes a new asynchronous consensus method aimed at substantially mitigating the communication traffic and decreasing the message transmission requirements associated with the market-based approach, ultimately leading to a reduction in scheduling time. Firstly, the method innovatively introduces timestamps of agent information updates, which are more informative, thereby reducing inter-agent communication volume to$ n/m$of that in the original protocol (where$ n$represents the number of agents and$ m$denotes the number of tasks, with$ m\gt n$). Secondly, agent-centric asynchronous consensus protocols are designed based on the new timestamps, which can resolve inter-agent task conflicts more rapidly and efficiently. Additionally, a mechanism for avoiding message flooding is proposed to prevent endless broadcasts caused by communication issues such as packet loss, link disruptions, and node withdrawals. Finally, through a self-developed ad-hoc network simulation system, the swarm scheduling under real networking conditions is simulated. The validation results demonstrate that the algorithm can significantly reduce communication traffic and scheduling time.

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. Coordination of such teams often involves two aspects: (i) selecting appropriate subteams for different tasks in various areas; (ii) coordinating agents in the subteams to execute the associated subtasks. Existing work often assumes that the tasks are static and known beforehand, where an integer program can be formulated and solved offline. However, in many applications, the team-wise tasks are generated online continually by external requests; and the amount of subtasks within each task is uncertain (e.g., the number of packages to deliver, and victims to rescue). The aforementioned offline solution becomes inadequate as it would require constant re-computation for the whole team and global communication to broadcast the results. Thus, this work tackles the large-scale coordination problem under continual and uncertain temporal tasks, specified as temporal logic formulas over collaborative actions. The proposed hierarchical framework (HULK) consists of two interleaved layers: the rolling assignment of currently-known tasks to subteams within a certain horizon, and the dynamic coordination within a sub-team given the detected subtasks during online execution. Thus, the coordination is performed hierarchically at different granularities and triggering conditions, to improve the computational efficiency and robustness. It is validated rigorously over large-scale heterogeneous systems under various temporal tasks and environment uncertainties.

Unmanned aerial vehicle (UAV) swarms find extensive applications in diverse fields, including search and rescue, logistics delivery, and environmental surveillance, necessitating meticulous task and temporal scheduling to meet intricate spatiotemporal requirements. A market-based strategy emerges as a suitable option for self-organizing swarm coordination. However, the consensus mechanisms employed by most market-based algorithms necessitate synchronous communication, leading to waiting times. Researchers have turned to asynchronous approaches for enhanced efficiency, yet the communication burden of existing asynchronous methods escalates swiftly with the growth of the swarm size. Therefore, this paper proposes an Asynchronous Harmony-based Decentralized Auctions (AHDA) method for networked UAV swarm to reduce the communication load and scheduling time required by a market-based approach. First, proximity communication is proposed to reduce the broadcast range and content of UAVs. Second, new conflict resolution protocols are designed to eliminate task conflict between UAVs faster. Third, propagation rules are designed to limit the scope of task information diffusion. Ultimately, it brings a decrease in communication load and scheduling time because it is expected to achieve the minimum requirement of no task conflict between UAVs, rather than swarm scheduling consistency. Monte Carlo simulations spanning 32 to 128 UAVs demonstrate that compared with the Asynchronous Consensus-Based Bundle Algorithm (ACBBA), the proposed AHDA achieves reductions of up to 70.16% in transmitted messages, 75.78% in communication traffic, and 63.12% in scheduling time.

Distributed task allocation in the UAV swarm is sensitive to excessive communication overhead and frequent transmissions. Combining reinforcement learning and task allocation demonstrates great potential in enhancing algorithm performance and optimizing communication. However, existing studies rely on ideal communication assumptions and the nonphysical environment, making training and validation impractical in applying networked swarms. This paper proposes the Communication-Aware Task Allocation, which aims to train a gating mechanism policy to coordinate the transmission timing, improving robustness and timelessness of the task allocation. First, the policy learning problem is formalized as a POMDP, for which the channel access and other features are designed for observations, actions are inter-agent adaptive gating mechanisms, and the shared reward reflects global task conflicts. Second, to address the asynchronous learning under the CTDE, an asynchronous experience collection and splicing method is proposed to align trajectories. Then, the MOCPPO is proposed, which combines a primal-dual operator with proximal policy optimization, updating the optimal Lagrange multiplier and strategy parameters to simultaneously minimize task conflicts and communication overhead. Finally, sim-to-real experiments are conducted in the HIL environment, and results illustrate the best trade-off optimization of the proposed method over all state-of-the-art approaches.

Automated synthesis of local controllers for multi-agent systems to satisfy complex task specifications has attracted extensive attention. However, it remains unclear how to formally guarantee that their composed behavior satisfies the specified global task. In this paper, we aim to synthesize an automated and distributed control strategy for fleet-wise tasks specified as linear temporal logic formulas, such that the agents act asynchronously and synchronize only on shared actions via local coordination. The proposed method consists of three main steps. First, the set of satisfying global control strategies is computed via parallel composition. Among these strategies the conditions for decomposability are evaluated, based on which the global strategy over a maximum synchronization scheme is found. Then the synchronization scheme is further refined to obtain more efficient local control strategies. It is formally proven that the resulting global behavior along with the synchronization scheme satisfies the specified global task. Also, the synthesized local controllers are reactive to changes in the workspace or in the fleet during online execution, without the need for replanning. Thus, collaborative relations among the agents are adaptive as needed. Numerical simulations and hardware experiments are conducted for nontrivial scenarios.Note to Practitioners—This paper was motivated by the problem of coordinating a fleet of autonomous robots in collaborative search and delivery processes, where local controllers are synthesized for each robot such that the specified global task specification is satisfied. Existing approaches to address such problems often rely on a fully-connected communication topology and fixed collaborative relations among the robots, thus limiting efficiency of the multi-robot execution and yielding difficulty of coordination. Also, most of existing approaches cannot directly deal with the changes in the workspace or in the fleet during task execution unless a task replanning is performed, which leads to longer time of system-wide communication and task completion, yielding failures during online execution. In this paper, we propose a novel distributed control architecture to tackle these issues, where the robots are controlled to operate asynchronously and achieve online local coordination by synchronization on collaborative actions. Moreover, compared with the common solutions, the collaborative relations among the robots are formed and removed dynamically as needed and the robots are robust to uncertainty during task execution. It is formally proven that the resulted global behavior of the robot fleet along with the synchronization scheme is consistent with the specified global task. We have shown that it is particularly useful for complex and coupled multi-robot applications, where the inter-robot collaborations are feasible and local. Experimental results suggest that this approach is applicable to multi-robot systems which greatly improves the concurrency and efficiency of task execution. In the future research, we will draw inspiration from decentralized approaches for task decomposition to alleviate the computational burden as the number of robots increases.

Multiagent scheduling has recently been reinvigorated by the burgeoning application of swarm, receiving significant attention due to its new characteristics. The market-based method is a fast distributed scheduling method that is naturally suitable for agent swarm, while its multiround communication is inevitably affected by the environment and the performance deteriorates. This article proposes an idea of computing-for-communication (CFC) with improving or even appropriately increasing computation to reduce communication rounds and improve the performance meanwhile, which does not add additional communication protocols and traffic but may moderately increase the amount of computation and storage. First, a new scoring function and a local optimization method are proposed to improve the agent’s schedule and resolve the conflict among agents in advance. Second, an agent location inference method and task-related agent selection strategy are presented for local optimization, which is expected to avoid the increase of communication in locations and the waste of computation on irrelevant agents. Third, some modifications for removing and adding tasks are proposed to further improve the performance of scheduling. Finally, extensive Monte Carlo experiments demonstrate the commendable performance of the proposed method in comparison with the representative consensus-based bundle algorithm (CBBA) and performance impact algorithm (PI).

Ground target tracking constitutes a crucial functionality for Unmanned Aerial Vehicles (UAVs), serving as the foundational element for missions including reconnaissance, search and rescue operations. This paper proposes a rapid and adaptive approach for fixed-wing UAVs to track a ground target by utilizing image-based visual servoing (IBVS). It is noted that a monocular camera is fixed on the UAV to observe the target. Initially, the feedback linearization coupled with the least squares method is employed to obtain the optimal control for the nonlinear underactuated system of the UAV. Subsequently, a "regulatory factor" is devised for the image Jacobian matrix to streamline the convergence of target position on the image and thereby expediting the approaching of the UAV to the target. Considering the necessity of target depth information in the image Jacobian matrix, an innovative strategy is conceived for the online, adaptive estimation of the depth. Simulation results validate the effectiveness of the proposed method.

UAV swarms have attracted much attention for post-disaster search and rescue, pollution monitoring and trace-ability, etc., where distributed scheduling is required to arrange careful tasks and time quickly. The market-based methods are widely favored but they rely on the environmentally influenced communication network to complete negotiation, while the on-board computing of UAV is robust and redundant. This paper proposes a distributed scheduling method for networked UAV swarm based on computing for communication, which trades a modest increase in computing for a significant decrease in communication. First, by analyzing the task removal strategies of two representative methods, the consensus-based bundle algorithm (CBBA) and performance impact (PI) algorithm, a new removal strategy is proposed, which expands the explo-ration of the bundle and can potentially reduce communication rounds. Second, the proposed task-related optimization method can extract task conflict nodes from the native communication protocol, and use the sampling and estimation strategies to resolve task conflicts in advance. Third, historical bids are cleverly used to infer others' locations, which is necessary for task-related optimization. Fourth, to verify the algorithm in real communication, a hardware-in-the-loop (HIL) ad-hoc network simulation system is constructed, which uses real network protocols and simulated channel transmissions. Finally, the HIL Monte Carlo simulation results show that, compared with CBBA and PI, the proposed method can significantly reduce the number of communication rounds and the total scheduling time, without increasing the communication protocol overhead and loss of optimization.

In this article, a multilayered and distributed architecture for mission-oriented miniature fixed-wing UAV swarms is presented. Based on the concept of modularity, the proposed architecture divides the overall system into five layers: 1) low-level control layer; 2) high-level control layer; 3) coordination layer; 4) communication layer; and 5) human interaction layer, and many modules that can be viewed as black boxes with interfaces of inputs and outputs. In this way, not only the complexity of developing a large system can be reduced but also the versatility of supporting diversified missions can be ensured. Furthermore, the proposed architecture is fully distributed that each UAV performs the decision-making procedure autonomously so as to achieve better scalability. Moreover, different kinds of aerial platforms can be feasibly extended by using the control allocation matrices and the integrated hardware box. A prototype swarm system based on the proposed architecture is built and the proposed architecture is evaluated through field experiments with a scale of 21 fixed-wing UAVs. Particularly, to the best of our knowledge, this article is the first work which successfully demonstrates formation flight, target recognition, and tracking missions within an integrated architecture for fixed-wing UAV swarms through field experiments.