This article describes an effective control approach for Wheeled Omnidirectional Mobile Robot Formations, capable of compensating disturbances generated by unknown forces acting on the vehicles. The multi-robot formations are modeled as uncertain multi-variable dynamical systems subject to undesired traction dynamics, measurement noise, and unknown attraction or repulsion forces between robots, all of which significantly affect their motion. The proposed approach employs an Observer-Based Sliding Mode Control scheme derived from a dynamic model defined within a Cluster Space framework, which enables the decoupling of inter-robot disturbances along state variables conveniently selected. These decoupled disturbances are actively rejected through estimates provided by a reduced set of High-Gain Observers, which are then incorporated into the feedback control law. The performance of the proposed Cluster Space-based controller is evaluated against an Observer-based Active Disturbance Rejection Control formulated in the conventional pose space of robots, which requires a larger number of disturbance observers. Closed-loop behavior is assessed first through high-fidelity simulations of 2-robot and 3-robot multi-body formations, and then through real experiments with a 2-robot prototype. The results confirm that both controllers achieve successful trajectory tracking and disturbance rejection, with the proposed Cluster Space approach offering notable simplification due to the reduced number of observers, while maintaining comparable performance with a lower computational burden in the estimation process.

This work addresses the problem of fault tolerance in aerial manipulators, focusing on maintaining maneuverability after the failure of a single rotor without adding any extra hardware. Instead, the proposed approach exploits the ability to reposition the onboard manipulator or payload, thereby modifying the vehicle's center of mass (CoM) and moments of inertia to recover controllability. This study develops a Gain Scheduled (GS) adaptive controller that explicitly adapts to variations in the vehicle's moment of inertia induced by payload repositioning. The controller is implemented on a resource-constrained autopilot, which requires a design that balances computational efficiency with robustness. Simulations and experimental flight tests validate the proposed method, comparing the Gain Scheduled controller to a conventional PID controller under similar maneuvering scenarios.

In this work, we study angle-based localization and rigidity maintenance control for multi-robot networks under sensing constraints. We establish the first equivalence between angle rigidity and bearing rigidity considering directed sensing graphs and body-frame bearing measurements in both 2 and 3-dimensional space. In particular, we demonstrate that a framework in SE(d) is infinitesimally bearing rigid if and only if it is infinitesimally angle rigid and each robot obtains at least d-1 bearing measurements (d ∈{2, 3}). Building on these findings, this paper proposes a distributed angle-based localization scheme and establishes local exponential stability under switching sensing graphs, requiring only infinitesimal angle rigidity across the visited topologies. Then, since angle rigidity strongly depends on the robots' spatial configuration, we investigate rigidity maintenance control. The angle rigidity eigenvalue is presented as a metric for the degree of rigidity. A decentralized gradient-based controller capable of executing mission-specific commands while maintaining a sufficient level of angle rigidity is proposed. Simulations were conducted to evaluate the scheme's effectiveness and practicality.

The design of distributed control strategies for multi-robot systems (MRS) relies heavily on simulations to validate algorithms prior to real-world deployment. However, simulating such systems poses significant challenges due to their dynamic network topologies and scalability requirements, where full inter-robot communication becomes computationally prohibitive. In this paper, we extend the applications of the Emergent Behavior DEVS (EB-DEVS) formalism by developing an agent-based model (ABM) to address key distributed control challenges in networked MRS. The proposed approach supports both direct and indirect interactions between agents (robots) via event messages and through macroscopic-microscopic states sharing, respectively. We validate the model using a challenging cooperative target-capturing scenario that demands dynamic multi-hop communication and robust coordination among agents. This complex use case highlights the strengths of EB-DEVS in managing asynchronous events while minimizing communication overhead. The results demonstrate the formalism's effectiveness in supporting decentralized control and simulation scalability within a hierarchical micro-macro modeling framework.

This work presents a novel approach for bearing rigidity analysis and control in multi-robot networks with sensing constraints and dynamic topology. By decomposing the system's framework into subframeworks, we express bearing rigidity-a global property-as a set of local properties, with rigidity eigenvalues serving as natural local rigidity measures. We propose a decentralized gradient-based controller to execute mission-specific commands using only bearing measurements. The controller preserves bearing rigidity by keeping the rigidity eigenvalues above a threshold, using only information exchanged within subframeworks. Simulations evaluate the scheme's effectiveness, underscoring its scalability and practicality.

This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for improving UAV-based wildlife monitoring and conservation strategies through scalable and accurate AI-driven detection systems.

This work addresses the control problem of a foldable quadrotor whose in-flight arm reconfiguration changes its moment of inertia. A Linear Parameter Varying (LPV) controller is first designed to maintain consistent performance across configurations, but its computational cost limits its use on low-cost autopilots. Based on frequency domain analysis, we approximate the LPV with a low-order controller, akin to an adaptive PID, achieving similar response at lower complexity. Simulations and experimental tests on a Cortex M3-based platform, using both fixed-point and doubled-precision implementations, show similar behavior. The approach approximates LPV-like performance with PID-level efficiency, enabling real-time deployment on resource-constrained UAVs.

This paper evaluates a dual quaternion-based control algorithm designed to manage dynamic changes in the number of vehicles in robot formations. By defining a virtual structure, the algorithm coordinates the formation's position, orientation, and shape parameters, ensuring seamless transitions when the number of vehicles changes. The approach enables a low-level controller to calculate commands for individual robots while maintaining the overall formation integrity. The strategy's performance is analyzed through simulations and experimental results, demonstrating its effectiveness in handling variations in the number of vehicles of robotic formations.

This article contributes to the study of graph rigidity and its interplay with fundamental graph invariants. Recently, a quantitative measure of graph rigidity in Rd, termed the generalized algebraic connectivity, was introduced. This development extends the notion of algebraic connectivity-the second-smallest eigenvalue of the Laplacian matrix. In this work, we show that the generalized algebraic connectivity is bounded above by the algebraic connectivity. To capture this relationship, we introduce the d-rigidity ratio, a normalized metric of a graph's rigidity relative to its connectivity. We also investigate the relationship between rigidity and the diameter. In this context, we provide the maximal diameter achievable by rigid graphs and show that generalized path graphs serve as extremal examples. Moreover, we establish a new upper bound for the algebraic connectivity that depends inversely on the diameter and the vertex connectivity. Finally, we derive an upper bound for the algebraic connectivity of generalized path graphs that asymptotically improves existing ones by a factor of four. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This paper presents a control strategy based on dual quaternions for the coordinated formation flying of small UAV groups. A virtual structure is employed to define the desired formation, enabling unified control of its position, orientation, and shape. This abstraction makes formation management easier by allowing a low-level controller to compute individual UAV commands efficiently. The proposed controller integrates a pose control module with a geometry-based adaptive strategy, ensuring precise and robust task execution. The effectiveness of the approach is demonstrated through both simulation and experimental results.

This work proposes a fault tolerant aerial manipulator based on a standard hexarotor vehicle, which is able to maintain independent control of torque and vertical thrust in case of motor failure by adequately changing the manipulator configuration and therefore modifying the position of the system’s center of mass. By analyzing the disturbance torque that the manipulator exerts on the vehicle, conditions to achieve stable flight and operation limits are established. To provide an experimental proof of concept, several flights are performed, both in indoor and outdoor environments, for different size and weight of manipulators.

En los últimos años, los vehículos aéreos multirotores han ganado popularidad tanto en productos de consumo como en aplicaciones profesionales. La seguridad es una de las principales preocupaciones durante la operación y diferentes enfoques a la tolerancia a fallas se han propuesto y continúan desarrollándose. Para que un sistema de control maneje situaciones fuera de lo nominal, las fallas deben detectarse e identificarse adecuadamente, por lo tanto, se requiere un algoritmo de detección e identificación de fallas. Además, el lazo de control debe modificarse en consecuencia para hacer frente a cada falla de la mejor manera posible. Estos algoritmos generalmente se ejecutan en la computadora de vuelo de bajo nivel del vehículo, lo que le impone una gran carga computacional adicional. En este trabajo se utiliza un módulo de detección e identificación de fallas para evaluar su impacto en términos de tiempo de procesamiento adicional en una computadora de vuelo basada en el microcontrolador Cortex-M3. Si bien se puede ejecutar una versión altamente optimizada del algoritmo, aún sugiere posibles limitaciones de hardware para expandir las capacidades del sistema. La evaluación del mismo módulo en un diseño de computadora de vuelo mejorado basado en un microprocesador Cortex-M7 muestra una huella significativamente reducida en el rendimiento general, lo que permite agregar un método aumentado para una detección de fallas más rápida.

In this paper we show that the d -dimensional algebraic connectivity of an arbitrary graph G is bounded above by its 1-dimensional algebraic connectivity, i.e., a d ( G ) ≤ a 1 ( G ) , where a 1 ( G ) corresponds the well-studied second smallest eigenvalue of the graph Laplacian.

Applications with small unmanned vehicles have grown remarkably in recent years. Along with this, the interest and the need to carry out tasks using several of these vehicles has gained relevance. This paper presents the use of a formation based on the cluster space control technique between an unmanned surface vehicle and an aerial vehicle, with the aim of monitoring river basins. The different perception of the environment that each vehicle has and the ability to transport different sensors are fundamental characteristics for this type of application. Results are presented both in a simulation environment and in a real application with unmanned vehicles belonging to the research groups.

Las aplicaciones con pequeños vehículos no tripulados han crecido notablemente en los últimos años. Junto a ello, el interés y la necesidad de realizar tareas utilizando varios de estos vehículos fue cobrando relevancia. En este trabajo se presenta el uso de una formación basada en la técnica de control en el espacio del cluster entre un vehículo no tripulado de superficie y uno aéreo, con el objetivo de realizar monitoreo de cuencas fluviales. La diferente percepción del entorno que cada vehículo posee y la capacidad de transportar diferentes sensores son características fundamentales para este tipo de aplicación. Se presentan resultados tanto en entorno de simulación como en una aplicación real con vehículos no tripulados pertenecientes a los grupos de investigación.

Las aplicaciones con pequeños vehículos no tripulados han crecido notablemente en los últimos años. Junto a ello, el interés y la necesidad de realizar tareas utilizando varios de estos vehículos fue cobrando relevancia.En este trabajo se presenta el uso de una formación basada en la teícnica de control en el espacio del cluster entre un vehículo no tripulado de superficie y uno aéreo, con el objetivo de realizar monitoreo de cuencas fluviales. La diferente percepción del entorno que cada vehículo posee y la capacidad de transportar diferentes sensores son características fundamentales para este tipo de aplicación. Se presentan resultados tanto en entorno de simulación como en una aplicación real con vehículos no tripulados pertenecientes a los grupos de investigación.

This paper presents an alternative approach to the study of distance rigidity in networks of mobile agents, based on a subframework scheme. The advantage of the proposed strategy lies in expressing framework rigidity, which is inherently global, as a set of local properties. Also, we show that a framework’s normalized rigidity eigenvalue degrades as the graph’s diameter increases. Thus, the rigidity eigenvalue associated to each subframework arise naturally as a local rigidity metric. A decentralized subframework-based controller for maintaining rigidity using only range measurements is developed, which is also aimed to minimize the network’s communication load. Finally, we show that the information exchange required by the controller is completed in a finite number of iterations, indicating the convenience of the proposed scheme.

We propose the use of convolutional neural networks (CNN) for counting and positioning people given aerial shots of visible and infrared images. Our data set is entirely made of semi-artificial images created from real photographs taken from a drone using a dual FLIR camera. We compare the performance between the CNNs using 3 (RGB) and 4 (RGB + IR) channels, both under different lighting conditions. The 4-channel network responds better in all situations, particularly in cases of poor visible illumination that can be found in night scenarios. The proposed methodology could be applied to real situations when an extensive data bank of 4-channel images is available. (c) 2021 Elsevier B.V. All rights reserved.

This work proposes a novel data-driven distributed formation-control approach based on multi-population evolutionary games, which is structured in a leader-follower scheme. The methodology considers a time-varying communication graph that describes how the multiple agents share information to each other. We present stability guarantees for configurations given by time-varying interaction networks, making the proposed method suitable for real-world problems where communication constraints change along the time. Additionally, the proposed formation controller allows for an agent to leave or enter the group without the need to modify the behaviors of other agents in the group. This game-theoretical approach is evaluated through numerical simulations and real outdoors experimental results using a fleet of aerial autonomous vehicles, showing the control performance.

A formulation based on a team of unmanned aerial vehicles operating as a fully articulated multi-camera jib crane is proposed for the application of aerial cinematography. An optimization-based controller commands the formation to follow an artistic trajectory defined by the director of photography, while actively avoiding collisions and cameras' mutual visibility. The proposed scheme, based on the cluster-space formulation, presents an intuitive way of maneuvering the virtual camera fixture while automatically adjusting the motions by imposing artistic and safety constraints, facilitating the operator task.