Trajectory planning is crucial in multi-robot systems, particularly in environments with numerous obstacles. While extensive research has been conducted in this field, the challenge of coordinating multiple robots to flow collectively from one side of the map to the other—such as in crossing missions through obstacle-rich spaces—has received limited attention. This paper focuses on this directional traversal scenario by introducing a real-time scheduling scheme that enables multi-robot systems to move from edge to edge, emulating the smooth and efficient flow of water. Inspired by network flow optimization, our scheme decomposes the environment into a flow-based network structure, enabling the efficient allocation of robots to paths based on real-time congestion levels. The proposed scheduling planner operates on top of existing collision avoidance algorithms, aiming to minimize overall traversal time by balancing detours and waiting times. Simulation results demonstrate the effectiveness of the proposed scheme in achieving fast and coordinated traversal. Furthermore, real-world flight tests with ten drones validate its practical feasibility. This work contributes a flow-inspired, real-time scheduling planner tailored for directional multi-robot traversal in complex, obstacle-rich environments. Code: https://github.com/chengji253/FlowPlanner.

Fire poses significant threats to life and property, necessitating efficient inspection and accurate identification. Although aerial computer vision algorithms hold great promise, the deployment and computational limitations of onboard platforms prevent existing algorithms from meeting high standards of accuracy and real-time performance. To address these challenges, we propose an lightweight aerial fire detection model, LAFNET. This model incorporates the EffiDarknetLight backbone, optimized for both lightweight design and ease of deployment, integrates specially designed LightGhost(LG) block components within the LightGhost-Path Aggregation Network(LG-PAN) neck, resulting in a model Params of only 1.3 M. Experimental results demonstrate that our method attains a good trade-off between lightweight design and detection accuracy. Compared to the smallest standard YOLO series' model YOLOv5n, LAFNET improves MAP by$\mathbf{2. 1 \%}$, while reducing Params and FLOPs by$\mathbf{2 7. 8 \%}$and$\mathbf{2 9. 3 \%}$, the inference speed on Nvidia Orin Nano edge computing side improves 24.8 %. These experiments indicate that LAFNET offers a highly efficient solution for aerial fire detection, combining speed and accuracy.

Typical robot controllers assume firm ground, limiting their effectiveness in controlling robots on dynamic platforms such as trucks or ships. To address this limitation, we propose a reinforcement learning framework for robot locomotion on dynamic rigid platforms and a simulation in which 6-DoF dynamic platforms emulating ship oscillation. The framework enables a reinforcement learning model to estimate platform motion during robot locomotion control. In the simulation, our framework significantly reduces the quadruped robot’s fall rate and trajectory deviation compared to baseline controllers. Experiments on a real robot show that our framework enabled a quadruped robot to adapt to platform motions, including those that threw the robot into the air, while baseline models struggled in this case. Thus, our framework can advance the deployment of robots in real-world marine and vehicular applications.

Bearing-only Target Motion Analysis (TMA) is a promising technique for passive tracking in various applications as a bearing angle is easy to measure. Despite its advantages, bearing-only TMA is challenging due to the nonlinearity of the bearing measurement model and the lack of range information, which impairs observability and estimator convergence. This paper addresses these issues by proposing a Recursive Total Least Squares (RTLS) method for online target localization and tracking using mobile observers. The RTLS approach, inspired by previous results on Total Least Squares (TLS), mitigates biases in position estimation and improves computational efficiency compared to pseudo-linear Kalman filter (PLKF) methods. Additionally, we propose a circumnavigation controller to enhance system observability and estimator convergence by guiding the mobile observer in orbit around the target. Extensive simulations and experiments are performed to demonstrate the effectiveness and robustness of the proposed method. The proposed algorithm is also compared with the state-of-the-art approaches, which confirms its superior performance in terms of both accuracy and stability.

Formulating expert policies as macro actions promises to alleviate the long-horizon issue via structured exploration and efficient credit assignment. However, traditional option-based multipolicy transfer methods suffer from inefficient exploration of macro action's length and insufficient exploitation of useful long-duration macro actions. In this article, a novel algorithm named enhanced action space (EASpace) is proposed, which formulates macro actions in an alternative form to accelerate the learning process using multiple available suboptimal expert policies. Specifically, EASpace formulates each expert policy into multiple macro actions with different execution times. All the macro actions are then integrated into the primitive action space directly. An intrinsic reward, which is proportional to the execution time of macro actions, is introduced to encourage the exploitation of useful macro actions. The corresponding learning rule that is similar to intraoption Q-learning is employed to improve the data efficiency. Theoretical analysis is presented to show the convergence of the proposed learning rule. The efficiency of EASpace is illustrated by a grid-based game and a multiagent pursuit problem. The proposed algorithm is also implemented in physical systems to validate its effectiveness.

Swarm drones flying is a very attractive field of robotics research, motivated by natural bird flocking or other animal collective behaviors. In this paper, we propose and develop an open-source11https://github.com/micros-uav/CoFlyers universal platform CoFlyers for end-to-end whole-chain development from flocking-inspired models to real-drone swarm flying. In particular, CoFlyers is more user-friendly with only a unified programming language of MATLAB&Simulink, rather than several existing platforms with mixed programming languages or more efforts on raw functional modules. The prototype simulator of CoFlyers is implemented in MATLAB, allowing users to quickly develop and prototype swarm flying algorithms, and to conduct task-oriented parameter auto-tuning and batch processing within reproducible scenarios. Moreover, a real-world verification module of swarm drones is developed in Simulink as well, which directly calls the prototype simulator modules for code reuse. It connects the external platforms via a standardized user-datagram-protocol communication in-terface. As a case study, CoFlyers is utilized into a multi-drone collective flying scenario in confined environments, by implementing ROS&PX4&Gazebo for high-fidelity simulation and Optitrack&Tello-drones for experiments. Eventually, both simulation and experimental results have demonstrated and validated the user-friendly practicability of CoFlyers.

It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.

A swarm of fixed-wing unmanned aerial vehicles (UAVs) is expected to efficiently accomplish various tasks in complex scenarios. This paper proposes an attention-based population-invariant multi-agent deep reinforcement learning (MADRL) approach to deal with the decentralized collision-free flocking problem for a scalable fixed-wing UAV swarm. First, this problem is modeled as a decentralized partially observable Markov decision process from the perspective of each follower. Then, an improved multi-agent deep deterministic policy gradient (MADDPG) algorithm is presented to efficiently learn the population-invariant flocking policy. In this algorithm, the parameter sharing with ego-centric representation mechanism is incorporated to improve learning efficiency. Besides, the attention-based population-invariant network structure (APINet) is designed by leveraging the self-attention mechanism. With this structure, the learned flocking policy is invariant to the population of the swarm. Finally, both numerical and hardware-in-the-loop simulation results verify the efficiency and scalability of the proposed approach.

This paper presents an onboard micro aerial vehicle (MAV) localization algorithm VLO using onboard multi-sensor system consisting of a camera, a laser scanner and an inertial measurement unit. On the basis of onboard processor, the VLO can operate in real time without any prior information and ground assistance. Besides, it shows a strong robustness since it can work in both small and large, indoor and outdoor environment. As the main sensing devices of this system, the camera and laser scanner generate different characteristic data. The VLO fuses these two kinds of data for a more sufficient information about environment. A filter and an optimizer are then designed to estimate the MAV poses with extra onboard sensors data. Finally, an incremental dense map is updated. Different with vision-based or laser-based odometry, this system has no requirements for environments such as strong texture or structured surroundings. The Gazebo-based simulated and real MAV systems are built together for algorithm validation. The simulated and real results show that our onboard odometry VLO performs strong robustness without any prior information and basic assumptions.

Switching behavior among different swimming modes of fish is a normal phenomenon in nature. We find that there is the joints' vibration fact for robotic fish in the process of switching. It is believed that the difference might be caused by discontinuous driven signal. This paper analyzes the discontinuous signal's the effect on the robotic fish joints and fin surface. Furthermore, this paper proposes an effective phase compensation method based on sinusoidal model to solve the discontinuous problem that enables robotic fish to mimic this spontaneous mode switching behavior of live fish. Finally, experimental results illustrate that the phase compensation method shows the superiority in saving energy and reducing the complexity of the control tracking algorithm.

In this paper, biomimetic undulating fins are considered with the focus on their hysteresis nonlinearity. Hysteresis is confirmed with experimental data on the biorobotic fin prototype, and qualitative modeling on this nonlinear action is then achieved by using Preisach equations. The developed iterative learning control is applied to eliminate hysteresis nonlinearity in biorobotic undulating fins. Both the simulation and experimental results show that the proposed control method is effective and feasible to improve the tracking performance of biorobotic fins by considering the hysteresis effect. Furthermore, the control methods should facilitate biomimetic investigation on propulsive modes and waveforms of fish swimming.

Hysteresis characteristics is highly nonlinear, has memory and is common in engineering systems. Its presence introduces uncertainties and nonlinearity in dynamic modelling and thus difficulties in achieving a good control design. This paper studies the suitability of iterative learning control (ILC) to compensate hysteresis uncertainties for a class of continuous-time dynamic systems. We examine dynamic systems with Preisach model hysteresis nonlinearity. It is shown that this class of systems possess properties of continuity and repeatability which are required for ILC. Furthermore, anticipatory iterative learning control (or A-type ILC) is applied to overcome the uncertainties and nonlinearity introduced by hysteresis. Simulation results are presented to validate the effectiveness of ILC laws to eliminate tracking error due to hysteresis uncertainties.

This paper proposes a novel conceptual underwater bio-robot inspired by Rhinecanthus aculeatus, which belongs to median and/or paired fin (MPF) propulsion fish and impresses researchers with agility by cooperative undulation of the dorsal-and-anal fins. Such a fish-like robot is anticipated to outperform the conventional aquatic robots in maneuverability and stability for oceanic exploitation necessities, e.g. benthonic mineral exploration. To begin with, a specimen of R. aculeatus was filmed in a glass aquarium (150cm times 50cm times 60cm) in which artificial seawater was maintained at 26 degC or so. Afterwards, we analyzed a few characteristics in morphology and locomotion with image processing and other approaches. The morphological and kinematical bionic inspirations were summarized, and in succession, we elaborately delineated the design scheme of our conceptual robotic fish including the schematic architectures, the structural and outside form, and the undulatory multi-fin propulsor

Within median and/or paired fin (MPF) propulsion, many fish routinely use the long-based undulatory fins as the sole means of locomotion. In this paper, the long-based undulatory fin of an Amiiform fish "G. niloticus" was investigated. A simplified physical model was brought forward, which makes up of N equal thin rods and a rectangular elasticmembrane connecting them together. With light mass, small stiffness and low natural vibration frequency, the long-based fin undulating and fluid loading may induce large flexible distortion of their long-based fins when swimming. We established a kinematic model of the long-based undulatory fin on the basis of analyzing the long-based dorsal fin locomotion and considering the fluid-structure interaction. Further, the equilibrium equations of the undulatory fin were obtained by applying the membrane theory of thin shells in which the geometrical non-linearity of the structure is taken into account. Last, we apply the derived the kinematic model and equilibrium equations of the undulatory fin to analyze the thrust and propulsive efficiency varying with the aspect ratio of the fin and the maximum swing amplitude