Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas $ϵ$-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments.

This paper reports the field test validations of the recently proposed vision-based real-time multi-camera setups for detecting, tracking and 3D localizing multiple aerial targets (mainly drones). We also propose the additional concurrent camera pose estimation when the camera poses are not known beforehand. This extra step can be used alongside (in parallel) with the drone tracking and localizing process. We conducted flight tests using 2 drones flying in 2 specific scenarios, and used 3 cameras to observe, detect, track and locate the positions of both drones in global frame. The efficacy of our technique is measured by the accuracy of the temporal and spatial positions of the observed drones, against the drones’ own GPS recordings. Our initial results show reasonable accuracy, i.e. ±1m at 50m, as such, the proposed vision-based methods can be used for drone detection and tracking.

This paper presents an investigation of the fluid–structure interaction (FSI) effects on the stability of a quadrotor attached to a flexible hose conveying and ejecting pressurised fluid from an onboard nozzle. In this study, an analytical solution is derived to obtain the time and spatial responses of the free end, which could affect the quadrotor’s stability. First, the flow-induced force model was simulated at the hose plane to find out the contributing disturbances prior to the physical connection with the unmanned aerial vehicle (UAV). Thereafter, the flow-induced forces were introduced to the UAV dynamics model as disturbances to study the FSI response during flight. Physical experiments were conducted to compare the analytical responses of the UAV prior to and during ejection. The presented findings of the perturbations due to the FSI effect from the pressurised fluid flowing through the flexible hose to the free end and the jet reaction at the UAV nozzle will be used for the employment of a combined feedforward-feedback (FF-FB) quadrotor control strategy for a stable ejection phase. The proposed strategy shows an average improvement of 61.14% (x-axis) and 22.46% (z-axis) in terms of active position compensation during ejection as compared to a standard feedback (FB) control loop only.

This article delineates the enhancement of an autonomous navigation and obstacle avoidance system for a quadruped robot dog. Part one of this paper presents the integration of a sophisticated multi-level dynamic control framework, utilizing Model Predictive Control (MPC) and Whole-Body Control (WBC) from MIT Cheetah. The system employs an Intel RealSense D435i depth camera for depth vision-based navigation, which enables high-fidelity 3D environmental mapping and real-time path planning. A significant innovation is the customization of the EGO-Planner to optimize trajectory planning in dynamically changing terrains, coupled with the implementation of a multi-body dynamics model that significantly improves the robot’s stability and maneuverability across various surfaces. The experimental results show that the RGB-D system exhibits superior velocity stability and trajectory accuracy to the SLAM system, with a 20% reduction in the cumulative velocity error and a 10% improvement in path tracking precision. The experimental results also show that the RGB-D system achieves smoother navigation, requiring 15% fewer iterations for path planning, and a 30% faster success rate recovery in challenging environments. The successful application of these technologies in simulated urban disaster scenarios suggests promising future applications in emergency response and complex urban environments. Part two of this paper presents the development of a robust path planning algorithm for a robot dog on a rough terrain based on attached binocular vision navigation. We use a commercial-of-the-shelf (COTS) robot dog. An optical CCD binocular vision dynamic tracking system is used to provide environment information. Likewise, the pose and posture of the robot dog are obtained from the robot’s own sensors, and a kinematics model is established. Then, a binocular vision tracking method is developed to determine the optimal path, provide a proposal (commands to actuators) of the position and posture of the bionic robot, and achieve stable motion on tough terrains. The terrain is assumed to be a gentle uneven terrain to begin with and subsequently proceeds to a more rough surface. This work consists of four steps: (1) pose and position data are acquired from the robot dog’s own inertial sensors, (2) terrain and environment information is input from onboard cameras, (3) information is fused (integrated), and (4) path planning and motion control proposals are made. Ultimately, this work provides a robust framework for future developments in the vision-based navigation and control of quadruped robots, offering potential solutions for navigating complex and dynamic terrains.

The temporal and spatial relationships among various onboard sensors are crucial for ensuring the accuracy of visual observations carried out by the multiple unmanned aerial vehicles (multi-UAV) system. To leverage the integration within the multi-UAV system, we propose a buddy calibration method focused on calibrating the relative time offset and rotation parameters between the airborne camera and flight controller. We designate another UAV as the target and perform 6D pose estimation to calculate the relative pose between the camera mounted on the host UAV and the target UAV. Subsequently, we use the pose estimation results and the parameters recorded by the sensors on both UAVs to establish a comprehensive temporal and spatial calibration model. Finally, a specific method is developed to effectively solve the proposed model. Our approach was validated using two-UAV flight experiments. The results demonstrate a significant improvement, with leader attitude estimation errors decreasing to approximately less than 1 $^{\circ }$ post-calibration compared to pre-calibration. Furthermore, positioning errors for dynamic and static targets were substantially reduced over 42.59% and 53.36%, respectively.