The Internet of Things (IoT) enables intelligent transportation services by connecting vehicles with roadside infrastructure and generating time-sensitive data. To support low-latency processing, edge-IoT vehicular networks deploy distributed edge servers near mobile users. However, high vehicular mobility and heterogeneous edge resources make it difficult for existing approaches to effectively exploit spatio-temporal mobility patterns and to support real-time offloading decisions. To address these challenges, this paper proposes TPADO, a Trajectory Prediction-Aware Dynamic Offloading framework that integrates a Spatio-Temporal Graph Convolutional Network (ST-GCN) with a multi-agent decision mechanism based on Proximal Policy Optimization (PPO). TPADO employs ST-GCN to perform high-fidelity trajectory prediction by explicitly modeling the graph structure of vehicular networks, thereby enabling proactive candidate-node selection and mobility-aware result delivery. Based on the predicted mobility information, we further design a hierarchical multi-strategy offloading framework, where a DRL-based policy layer adaptively selects offloading strategies, and a rule layer performs fine-grained node assignment and task partitioning. Extensive simulation results demonstrate that TPADO achieves the best overall performance among the compared methods. Compared with the centralized DQN baseline, it reduces global average latency by 6.4% and system saturation by 3.01 percentage points, while also delivering higher throughput and task success rate. These results validate the effectiveness and generalizability of the proposed framework.

This paper presents a safe and scalable control framework for heterogeneous multi-agent flocking using motion-aware control barrier functions. A nominal flocking controller is designed for cohesion and velocity alignment, while inter-agent separation and collision avoidance are enforced through pairwise safety constraints in local quadratic programs (QPs). Agent heterogeneity is explicitly considered, allowing for different physical radii and actuation limits. To reduce conservatism and computational burden, explicit reaction radii are derived for a distance-only safety activation policy, which characterise when pairwise safety constraints are automatically satisfied and can be removed from the QP. A motion-aware activation policy is then proposed that exploits both relative distance and approach velocity to activate safety constraints only when there is a potential collision risk. Simulations validate the effectiveness and scalability of the proposed framework, while real-world Crazyflie experiments demonstrate its practical feasibility.

Clients in federated learning (FL) may exhibit varying computing capabilities, leading to prolonged training latency when deploying complex deep neural networks. To address this challenge, split federated learning (SFL) presents an approach that offloads the main computational workload from resource-constrained devices to a server, while enabling parallel training. However, there are two significant limitations of existing SFL frameworks: The adoption of a uniform cut layer strategy fails to take into account the heterogeneous among clients; it fails to effectively utilize server-side resources to improve training efficiency. This article presents a framework, i.e., heterogeneous split federated learning, which considers personalized cut layer selection and server resource configuration to accelerate SFL in heterogeneous edge computing environments. By splitting the global model into two components for each client, our framework jointly optimizes both client-side workload, batch size control, and server resource configuration strategy, while considering device heterogeneity. Specifically, we develop an alternating iterative scheduling algorithm to obtain an approximate scheme for the cut layer, batch sizes, and server resource configuration to alleviate the impact of device heterogeneity. The experimental results illustrate that HSFL outperforms the compared methods, achieving performance improvements of up to 3.9%$\sim$32.2% across two datasets under various data distribution scenarios, which demonstrates the effectiveness of the proposed strategies.

Autonomous exploration of unknown, cluttered environments using unmanned aerial vehicle swarms is critical for applications like search and rescue, yet poses significant challenges in coordination, scalability, and adaptability. Classical methods often struggle with dynamic conditions and large swarm sizes, while standard Multi-Agent Reinforcement Learning (MARL) faces issues like state-space explosion and non-stationarity. This work proposes a novel hierarchical MARL framework to address these limitations. This approach integrates hierarchical grid decomposition for high-level strategic task allocation with multi-agent soft actor-critic for low-level reactive control, enabling efficient navigation and collision avoidance. Training employs a centralised training decentralised execution paradigm to foster cooperation while maintaining decentralised operation suitable for real-world constraints. Results demonstrate that the hierarchical learning approach significantly outperforms a non-learning heuristic baseline in exploration efficiency and exhibits robust scalability with increasing agent numbers.

Cooperative multi-robot navigation requires large teams of robots to reach individual goals without collisions in shared spaces, which is a challenge compounded by partial observability, dynamic interactions, and the combinatorial nature of joint decision-making. Centralized methods provide optimality but fail to scale, while decentralized approaches offer efficiency yet often suffer from deadlocks and weak cooperation. To overcome these limitations, we propose a decentralized reinforcement learning framework that integrates a dual-head soft actor-critic architecture with selective graph-attention communication and task-specific reward shaping. A compact encoder processes a novel observation representation that combines local fields of view, goal projections, and directional heuristics, enabling scalable awareness under limited sensing. Heuristic and blocking rewards further accelerate convergence and promote cooperative navigation in congested regions. Extensive experiments on grid environments of varying sizes and robot densities show that our method achieves faster convergence, higher success rates, and stronger scalability than state-of-the-art methods in this domain.

This paper introduces a time-optimal swarm trajectory planner for cooperative uncrewed aerial vehicle (UAV) systems, designed to generate collision-free trajectories for flocking control in cluttered environments. To achieve this goal, model predictive contour control is utilised to generate time-optimal trajectories for each UAV. By demonstrating the differential flatness dynamic equations, the system state constraints are simplified, the algorithm’s complexity is reduced, and the overall stability is improved. Additionally, flocking control is achieved among multiple UAVs by applying virtual repulsive and attractive forces. Furthermore, an event-triggered trajectory deconflict strategy for trajectory replanning is considered to resolve multiple trajectory conflicts. Comparative experiments with baseline methods have confirmed that the proposed planner can generate faster and safer trajectories than conventional methods.

Coordination of multi-agent systems has received significant attention during the past few years owing to its wide real-world applications, such as cooperative exploration, aircraft formation, and autonomous vehicle platooning. To address this issue, this research presents a novel method for multi-agent systems to navigate through environments with obstacles. The system consists of a group of agents with a leader-follower structure, where the leader aids in guiding the agents toward the target location and the followers are steered to maintain a flexible formation. To achieve cooperation, the agents communicate within a connected and undirected network, exchanging information within a specific radius. The leader's path is generated using the RRT* algorithm, which serves as a reference for the followers. A control law utilizes consensus and APF is then implemented, ensuring coordinated motion while maintaining safe distances among agents and between agents and obstacles. Finally, the effectiveness of the developed two-layer coordination strategy is verified by simulations.

This paper addresses a bearing-only formation tracking problem in robotic networks by considering exogenous disturbances and actuator faults. In contrast to traditional position-based coordination strategies, the bearing-only coordinated movements of the unmanned vehicles only rely on the neighboring bearing information. This feature can be utilized to reduce the sensing requirements in the hardware implementation. A gradient-descent protocol is first developed to achieve the desired coordination within a prespecified settling time, where the unknown disturbances are considered in the vehicle dynamics, then the bound of formation tracking error is guaranteed by the Lyapunov approach. In case of damage to the actuators (e.g., motors) in some of the vehicles during the task, fault-tolerant analysis of the proposed controller is provided to ensure the success of the task in extreme environments. Furthermore, the proposed bearing-based method is extended to deal with general linear systems, which can be applied to a wider range of robotic platforms. Finally, numerical simulations and lab-based experiments using unmanned ground vehicles are conducted to validate the effectiveness of the proposed strategy.Note to Practitioners—The aim of this paper is to develop and design a practical bearing-only formation control approach for multi-vehicle systems. Many real-world complex tasks can be solved by multiple unmanned aerial and ground vehicles being connected by a communication network. This paper has proposed a formation tracking scheme for networked multi-vehicle systems that only relies on the relative bearing information of the neighboring vehicles. Closed-loop stability of the scheme and finite-time convergence of the tracking error have been established using the Lyapunov stability approach. The proposed method ensures the robustness and fault-tolerance of the multi-vehicle system against hardware faults or exogenous disturbances. A systematic set of guidelines on how to apply the proposed strategy in practice is also provided for the control practitioners in the form of an algorithm. In order to demonstrate the feasibility and usefulness of the proposed coordination scheme, numerical simulations and lab-based hardware experiments were conducted. Potential applications of the proposed scheme include search and rescue, security surveillance and cooperative exploration.

Area coverage is an important problem in robotics applications, which has been widely used in search and rescue, offshore industrial inspection, and smart agriculture. This paper demonstrates a novel unified robust path planning, optimal trajectory generation, and control architecture for a quadrotor coverage mission. To achieve safe navigation in uncertain working environments containing obstacles, the proposed algorithm applies a modified probabilistic roadmap to generating a connected search graph considering the risk of collision with the obstacles. Furthermore, a recursive node and link generation scheme determines a more efficient search graph without extra complexity to reduce the computational burden during the planning procedure. An optimal three-dimensional trajectory generation is then suggested to connect the optimal discrete path generated by the planning algorithm, and the robust control policy is designed based on the cascade$NLH_\infty$framework. The integrated framework is capable of compensating for the effects of uncertainties and disturbances while accomplishing the area coverage mission. The feasibility, robustness and performance of the proposed framework are evaluated through Monte Carlo simulations, PX4 Software-In-the-Loop test facility, and real-world experiments.

Coordination of unmanned aerial vehicle (UAV) systems has received great attention from robotics and control communities. In this paper, we investigate the distributed formation tracking problem in heterogeneous nonlinear multi-UAV networks via bearing measurements. Firstly, a novel bearing-only protocol is designed for follower agents to achieve the desired formation. Particularly, we establish a compensation function on the basis of bearing measurements to deal with the non-linearity and actuator faults in the agent dynamics. The stability of the proposed strategy can be ensured by Lyapunov method in the presence of certain time delays. Moreover, to ensure safe operation in real-world scenarios, we extend the protocol and propose a sufficient condition to avoid potential collisions among the agents. The robustness of the collision-free controller with continuous action is also considered in the protocol design. Finally, the simulation case studies are presented to validate the feasibility of the theoretical results.

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in recent years due to their potential to enhance the traffic-carrying capacity of the roads and produce better safety for drivers and passengers. This paper proposes a distributed motion planning algorithm to ensure safe overtaking of autonomous vehicles in a dynamic environment using the Artificial Potential Field method. Unlike the conventional overtaking techniques, autonomous driving strategies can be used to implement safe overtaking via formation control of unmanned vehicles in a complex vehicle platoon in the presence of human-operated vehicles. Firstly, we formulate the overtaking problem of a group of autonomous vehicles into a multi-target tracking problem, where the targets are dynamic. To model a multi-vehicle system consisting of both autonomous and human-operated vehicles, we introduce the notion of velocity difference potential field and acceleration difference potential field. We then analyze the stability of the multi-lane vehicle platoon and propose an optimization-based algorithm for solving the overtaking problem by placing a dynamic target in the traditional artificial potential field. A simulation case study has been performed to verify the feasibility and effectiveness of the proposed distributed motion control strategy for safe overtaking in a multi-lane vehicle platoon.

This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environments as if we were omnipotent relative to the ants. In virtual reality, analogously, we can be a super-powered virtual giant who can supervise a swarm of robots in a vast and remote environment by flying over or resizing the world, and coordinate them by picking and placing a robot or creating virtual walls. This work implements this idea by using Virtual Reality along with Leap Motion, which is then validated by proof-of-concept experiments using real and virtual mobile robots in mixed reality. We conduct a usability analysis to quantify the effectiveness of the overall system as well as the individual interfaces proposed in this work. The results reveal that the proposed method is intuitive and feasible for interaction with swarm robots, but may require appropriate training for the new end-user interface device.

Public-facing social media platforms, such as Facebook and WeChat, are increasingly being embedded into corporate processes and routines. The use of public social media by employees has aroused widespread interest among scholars in recent years. This study summarises published theories and models and proposes a causal-chain framework for research exploration into employee usage of public social media platforms by systematically analysing the antecedent variables, mediators, moderators, and outcome variables used in 59 quantitative papers. The representative theories include: Social Capital Theory, Job Demands-Resources Model, Boundary Theory, Media Synchronicity Theory, Social Cognitive Theory, Technology Acceptance Model, Self-Determination Theory, and Media Richness Theory. Historically, researchers have studied social media usage behaviours as antecedents, rather than social factors, with many focusing on outcome variables such as job performance and job satisfaction, while the impact of employee social media usage on physical and mental health is less studied. In terms of moderators, variables such as use behaviour, user characteristics and job characteristics receive most attention. With regards to mediators, social capital, job satisfaction, and work conflict are most significant. This study proposes future research directions for this field, including topics relating to platform attributes, social power, organisational culture, and employee health.

Abstract Flocking is a social animals’ common behaviour observed in nature. It has a great potential for real-world applications such as exploration in agri-robotics using low-cost robotic solutions. In this paper, an extended model of a self-organised flocking mechanism using heterogeneous swarm system is proposed. The proposed model for swarm robotic systems is a combination of a collective motion mechanism with obstacle avoidance functions, which ensures a collision-free flocking trajectory for the followers. An optimal control model for the leader is also developed to steer the swarm to a desired goal location. Compared to the conventional methods, by using the proposed model, the swarm network has less requirement for power and storage. The feasibility of the proposed self-organised flocking algorithm is validated by realistic robotic simulation software.

Cooperative control of multirobot systems (MRSs) has earned significant research interest over the past two decades due to its potential applications in multidisciplinary engineering problems. In contrast to a single specialized robot, the MRS can be designed to offer flexibility, reconfigurability, robustness to faults, and cost-effectiveness in solving complex and challenging tasks. In this article, we aim to develop a unified cluster formation containment coordination framework for networked robots that can be decomposed into two layers containing the leaders and the followers. According to the proposed methodology, the leader robots are first distributed into a set of distinct and nonoverlapping clusters depending on the positions and priorities of the targets exploiting a game-theoretic rule. Then, they are steered to attain the desired formations around the corresponding targets. Subsequently, the follower robots are made to converge into the convex hull spanned by the leaders of the individual clusters. A prototype search and rescue operation is considered to highlight the usefulness of the proposed coordination framework. Furthermore, real-time hardware experiments were conducted on miniature mobile robots to validate the feasibility of the theoretical results.

The pricing mechanism of cloud-computing resources is an essential issue for both cloud customers and service providers, especially from the point of multi-provider competition. Although various mechanisms for resource provision are proposed, few studies have focused on multi-attribute resource provision with the objective of improving benefits of both cloud customers and service providers. To address the issue, we propose a price bidding mechanism for multi-attribute cloud-computing resource provision from the perspective of a non-cooperative game, in which the information of each player (customers and providers) is incomplete to others and each player wishes to maximize his/her own benefit. More specifically, considering the fairness pricing competition, we propose a novel and incentive resource provision model referring to the Quality-of-Service (QoS) and the bidding price. Then, combining with the resource provision model, the problem of price bidding is formulated as a game to find a proper price for each cloud provider. We demonstrate the existence of Nash equilibrium solution set for the formulated game model by assuming that the quantity function of provided resources from every provider is continuous. To find a Nash equilibrium solution, we propose an Equilibrium Solution Iterative (ESI) algorithm, which is proved to converge to a Nash equilibrium. Finally, a Near-equalization Price Bidding (NPB) algorithm is proposed to modify the obtained Nash equilibrium solution. Extensive simulated experiments results and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.

Mobile edge computing (MEC) is becoming a promising paradigm of providing computing servers, like cloud computing, to Edge node. Compared to cloud servers, MECs are deployed closer to mobile devices (MDs) and can provide high quality-of-service (QoS; including high bandwidth, low latency, etc) for MDs with computation-intensive and delay-sensitive tasks. Faced with many MDs with high QoS requirements, MEC with limited computation capacity should consider how to allocate the computing resources to MDs to maximize the number of served MDs. Besides, for each MD, he/she wants to minimize the energy consumption within an acceptance delay range. To solve these issues, we propose a Game-based Computation Offloading (GCO) algorithm including a task offloading profile of MEC and the transmission power controlling of each MD. Specifically, we propose a Greedy-Pruning algorithm to determine the MDs that can offload the tasks to MEC. Meanwhile, each MD competes the computing resources by using his/her transmission power-controlling strategy. We illustrate the problem of task offloading for multi-MD as a non-cooperative game model, in which the information of each player (MDs) is incomplete for others and each player wishes to maximize his/her own benefit. We prove the existence of the Nash equilibrium solution of our proposed game model. Then, it is proved that the transmission power solution sequence obtained from GCO algorithm converges to the Nash equilibrium solution. Extensive simulated experiments are shown and the comparison experiments with the state-of-the-art and benchmark solutions validate and show the feasibility of the proposed method.