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Runfeng Chen

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3 papers

2 author rows

IROS Conference 2025 Conference Paper

Asynchronous Harmony-based Decentralized Auctions Method for Scalable UAV Swarm

Runfeng Chen
Jie Li
Yiting Chen
Yuchong Huang
Zehao Xiong

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.

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IROS Conference 2023 Conference Paper

A Distributed Scheduling Method for Networked UAV Swarm based on Computing for Communication

Runfeng Chen
Jie Li
Yiting Chen
Yuchong Huang

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.

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JAAMAS Journal 2023 Journal Article

A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

Jie Li
Runfeng Chen
Xiangke Wang

Abstract Multi-task distributed scheduling (MTDS) remains a challenging problem for multi-agent systems used for uncertain and dynamic real-world tasks such as search-and-rescue. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the problem of non-convergence that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm through the integration of a task removal inference strategy and a deadlock avoidance mechanism. Specifically, the task removal inference strategy results in better exploration performance than the original PI, improving the suboptimal solutions caused by the heuristics for local task selection as done in PI. In addition, we design a deadlock avoidance mechanism that limits the number of times of removing the same task and isolating consecutive inclusions of the same task. Therefore, it guarantees the convergence of the MTDS algorithm. We demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of the search-and-rescue task. The results show that the proposed algorithm can obtain a lower average time cost and the highest total allocation number.

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