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.

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.

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).

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.