Biologically plausible and energy-efficient frameworks such as Spiking Neural Networks (SNNs) have not been sufficiently explored in low-level vision tasks. Taking image deraining as an example, this study addresses the representation of the inherent high-pass characteristics of spiking neurons, specifically in image deraining and innovatively proposes the Visual LIF (VLIF) neuron, overcoming the obstacle of lacking spatial contextual understanding present in traditional spiking neurons. To tackle the limitation of frequency-domain saturation inherent in conventional spiking neurons, we leverage the proposed VLIF to introduce the Spiking Decomposition and Enhancement Module and the lightweight Spiking Multi-scale Unit for hierarchical multi-scale representation learning. Extensive experiments across five benchmark deraining datasets demonstrate that our approach significantly outperforms state-of-the-art SNN-based deraining methods, achieving this superior performance with only 13% of their energy consumption. These findings establish a solid foundation for deploying SNNs in high-performance, energy-efficient low-level vision tasks.

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.

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.

Long-term monitoring and exploration of extreme environments, such as underwater storage facilities, is costly, labor-intensive, and hazardous. Automating this process with low-cost, collaborative robots can greatly improve efficiency. These robots capture images from different positions, which must be processed simultaneously to create a spatio-temporal model of the facility. In this paper, we propose a novel approach that integrates data simulation, a multi-modal deep learning network for coordinate prediction, and image reassembly to address the challenges posed by environmental disturbances causing drift and rotation in the robots' positions and orientations. Our approach enhances the precision of alignment in noisy environments by integrating visual information from snapshots, global positional context from masks, and noisy coordinates. We validate our method through extensive experiments using synthetic data that simulate real-world robotic operations in underwater settings. The results demonstrate very high coordinate prediction accuracy and plausible image assembly, indicating the real-world applicability of our approach. The assembled images provide clear and coherent views of the underwater environment for effective monitoring and inspection, showcasing the potential for broader use in extreme settings, further contributing to improved safety, efficiency, and cost reduction in hazardous field monitoring.

Continuous and reliable underwater monitoring is essential for assessing marine biodiversity, detecting ecological changes and supporting autonomous exploration in aquatic environments. Underwater monitoring platforms rely on mainly visual data for marine biodiversity analysis, ecological assessment and autonomous exploration. However, underwater environments present significant challenges due to light scattering, absorption and turbidity, which degrade image clarity and distort colour information, which makes accurate observation difficult. To address these challenges, we propose DEEP-SEA, a novel deep learning-based underwater image restoration model to enhance both low- and high-frequency information while preserving spatial structures. The proposed Dual-Frequency Enhanced Self-Attention Spatial and Frequency Modulator aims to adaptively refine feature representations in frequency domains and simultaneously spatial information for better structural preservation. Our comprehensive experiments on EUVP and LSUI datasets demonstrate the superiority over the state of the art in restoring fine-grained image detail and structural consistency. By effectively mitigating underwater visual degradation, DEEP-SEA has the potential to improve the reliability of underwater monitoring platforms for more accurate ecological observation, species identification and autonomous navigation.

Recent advancements in autonomous swarm systems have made a pivotal point in robotic science. Utilising a large-scale swarm of simple robots to accomplish complex tasks offers efficient, robust, and reliable solutions inspired by natural phenomena. Although bio-inspired methodologies have presented competent algorithms, those approaches inspired by the physical interactions in viscoelastic materials demonstrate more structured methods to prove the stability and robust performance of the algorithms mathematically. This paper proposes a new viscoelastic swarm algorithm which applies to heterogeneous swarm systems. In this paper, the algorithm development utilises the Lyapunov method to determine stability criteria and corresponding conditions. Therefore, the resulting approach does not rely on complex optimisation to obtain the parameters that guarantee stable performance. In addition to the theoretical framework, a series of Monte Carlo simulations have been conducted to assess the algorithm’s performance and its sensitivity to the key variables. Furthermore, the algorithm’s performance has been evaluated by a series of experiments with real robots to examine the effect of different variables, such as neighbourhood conditions and the stiffness coefficient, on the algorithm’s output. The results obtained from the simulations and experiments demonstrate the stable and bounded performance of the algorithm and how the key variables, such as stiffness coefficient and number of neighbours for each robot, affect the swarm performance. The comparison results, obtained from real-world experiments with a state-of-the-art algorithm, show that the proposed framework significantly reduces the control effort for the robots while improving the swarm behaviour.

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.

Honeybees are one of the most important pollinators in the ecosystem. Unfortunately, the dynamics of living honeybee colonies are not well understood due to their complexity and difficulty of observation. In our project “RoboRoyale”, we build and operate a robot to be a part of a bio-hybrid system, which currently observes the honeybee queen in the colony and physically tracks it with a camera. Apart from tracking and observing the queen, the system needs to monitor the state of the honeybee comb which is most of the time occluded by workerbees. This introduces a necessary tradeoff between tracking the queen and visiting the rest of the hive to create a daily map. We aim to collect the necessary data more effectively. We evaluate several mapping methods that consider the previous observations and forecasted densities of bees occluding the view. To predict the presence of bees, we use previously established maps of dynamics developed for autonomy in human-populated environments. Using data from the last observational season, we show significant improvement of the informed comb mapping methods over our current system. This will allow us to use our resources more effectively in the upcoming season.

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.

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot. This approach is built upon a recently proposed idea of using deep neural networks (DNNs) to approximate the optimal motion trajectories, which has been validated that a fast approximation performance can be achieved. To further enhance the network prediction performance, a recurrent network model capable of fully exploiting the inherent relationship between preoptimized system state and control pairs is advocated. In the lower level, a deep reinforcement learning (DRL)-based collision-free control algorithm is established to achieve the waypoint tracking task in an uncertain environment (e.g., the existence of unexpected obstacles). Since this approach allows the control policy to directly learn from human demonstration data, the time required by the training process can be significantly reduced. Moreover, a noisy prioritized experience replay (PER) algorithm is proposed to improve the exploring rate of control policy. The effectiveness of applying the proposed deep learning-based control is validated by executing a number of simulation and experimental case studies. The simulation result shows that the proposed DRL method outperforms the vanilla PER algorithm in terms of training speed. Experimental videos are also uploaded, and the corresponding results confirm that the proposed strategy is able to fulfill the autonomous exploration mission with improved motion planning performance, enhanced collision avoidance ability, and less training time.

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.

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.

In this paper, we proposed a pheromone-based aggregation method based on the state-of-the-art BEECLUST algorithm. We investigated the impact of pheromone-based communication on the efficiency of robotic swarms to locate and aggregate at areas with a given cue. In particular, we evaluated the impact of the pheromone evaporation and diffusion on the time required for the swarm to aggregate. In a series of simulated and real-world evaluation trials, we demonstrated that augmenting the BEECLUST method with artificial pheromone resulted in faster aggregation times.

Pheromone-based communication is one of the most effective ways of communication widely observed in nature. It is particularly used by social insects such as bees, ants and termites; both for inter-agent and agent-swarm communications. Due to its effectiveness; artificial pheromones have been adopted in multi-robot and swarm robotic systems for more than a decade. Although, pheromone-based communication was implemented by different means like chemical (use of particular chemical compounds) or physical (RFID tags, light, sound) ways, none of them were able to replicate all the aspects of pheromones as seen in nature. In this paper, we propose a novel artificial pheromone system that is reliable, accurate and it uses off-the-shelf components only - LCD screen and low-cost USB camera. The system allows to simulate several pheromones and their interactions and to change parameters of the pheromones (diffusion, evaporation, etc.) on the fly allowing for controllable experiments. We tested the performance of the system using the Colias platform in single-robot and swarm scenarios. To allow the swarm robotics community to use the system for their research, we provide it as a freely available open-source package.

Ultra-wideband (UWB) technology is a new concept in wireless short-range communication. It has numerous advantages that are resulted in producing different new products in various applications. This technology uses very short duration pulses hence occupies very large transmission bandwidth. This phenomenon may cause harmful interference in the existing wireless devices. The main objective of this study is to evaluate the effective parameters in ultra-wideband communication for indoor short-range devices. In this regard, two commercial UWB devices are utilized and effects of these devices in different experimental configurations are investigated. Three configurations including the effects of distance, narrowband spectrum, and another UWB device on bit error rate performance are evaluated. The results of the performed experiments showed the stability and amenability of the UWB technology to be used in multiple UWB devices.