Reliable formation control in real-world multi-agent systems is challenging due to the concurrent need to meet performance specifications, enforce safety constraints, and respect actuator limitations. While prescribed performance control (PPC) ensures bounded error evolution via predefined performance functions, incorporating safety and input constraints within this framework remains nontrivial. This paper develops a modular decoupled control architecture that integrates PPC and control barrier function (CBF) to enforce performance and safety. The fixed performance bound limitation in PPC control is overcome through the introduction of an auxiliary system that adaptively adjusts performance functions, thereby effectively enabling the quantification of performance degradation due to constraints while avoiding control singularities. To further mitigate conflicts between safety and performance, an online trajectory optimization module is designed to generate smooth and collision-free reference trajectories. The proposed approach is validated on a team of Crazyflie quadrotors navigating obstacle environments, demonstrating safe and accurate formation tracking under stringent constraints.

RGB-Event tracking has become a promising trend in visual object tracking to leverage the complementary strengths of both RGB images and dynamic spike events for improved performance. However, existing artificial neural networks (ANNs) struggle to fully exploit the sparse and asynchronous nature of event streams. Recent efforts toward hybrid architectures combining ANNs and spiking neural networks (SNNs) have emerged as a promising solution in RGB-Event perception, yet effectively fusing features across heterogeneous paradigms remains a challenge. In this work, we propose ISTASTrack, the first transformer-based ANN-SNN hybrid Tracker equipped with ISTA adapters for RGB-Event tracking. The two-branch model employs a vision transformer to extract spatial context from RGB inputs and a spiking transformer to capture spatio-temporal dynamics from event streams. To bridge the modality and paradigm gap between ANN and SNN features, we systematically design an ISTA adapter for bidirectional feature interaction between the two branches. The ISTA adapter is derived from the sparse representation theory by unfolding the iterative shrinkage-thresholding algorithm. Additionally, we incorporate a temporal downsampling attention module within the adapter to align multi-step SNN features with single-step ANN features in the latent space. Experimental results on RGB-Event tracking benchmarks, such as FE240hz, VisEvent, COESOT, and FELT, have demonstrated that ISTASTrack achieves state-of-the-art performance while maintaining high energy efficiency. This work highlights the effectiveness and practicality of hybrid ANN-SNN designs for robust visual tracking. The code is publicly available at https://github.com/lsying009/ISTASTrack.git.

Morphing quadrotors are capable of adapting to constrained environments through geometric reconfiguration. However, existing systems are limited by mechanical complexity and rigid links, which affect both safety and performance in such environments. In this paper, we propose a strut-actuated tensegrity aerial vehicle that integrates shape adaptation with collision resilience. By incorporating deformable struts and a cable network, our vehicle enables real-time morphological adjustments during flight while maintaining stability. We present a hierarchical planning framework that ensures the entire vehicle remains confined within an icosahedral space, thereby guaranteeing full-body safety. An on-manifold Model Predictive Controller (MPC) is employed to track these optimized trajectories and compensate for inertia shifts during shape deformation. Simulation results validate the effectiveness of the proposed framework, demonstrating its capability to navigate in restricted scenarios.

In multi-robot systems, accurate relative localization is indispensable for executing collaborative tasks in GPS-denied environments. This paper focuses on the relative localization problem relying on onboard UWB and IMU sensors. First, we propose a nominal adaptive gradient-based relative position observer for each robot. The estimation of time-varying relative position is transformed into the online constant parameter identification problem using only relative distance and velocity information. Furthermore, in order to relax the standard assumptions of persistently excited relative motions, a finite-time adaptive relative localization scheme is developed using the dynamic regression extension and mixing (DREM) technique. This scheme merely requires filtered relative velocity satisfying interval excited condition, which is milder than the persistent one. Finally, simulations are presented to verify the effectiveness of our theoretical results, followed by flight experiments on a team of three quadcopters. It indicates that the relative localization accuracy can reach centimeter level. Note to Practitioners—This paper is motivated by the relative localization problem without relying on any external infrastructure under GPS-denied environments, especially for situations where the robots’ trajectories cannot be persistently excited. Existing relative localization approaches generally assume that the robots’ velocities or displacements satisfy the persistent excitation condition, which restricts the motion forms of robots. This paper presents a new method that only requires the filtered relative velocity between robots to satisfy the interval excitation condition, so that accurate relative position estimations can be achieved within a finite time. In this paper, we provide a linear regressor equation generation method using the linear filter techniques, which mathematically characterizes the relationship between measurable signals (distance, velocity) and relative position. Then, we design a relative localization scheme based on the DREM method and give the convergence analysis. Both simulations and physical experiments suggest that the proposed method in this paper shows high localization accuracy about 10 cm and fast convergent speed. But it has not yet been applied to the specific control tasks. In future research, we will address the integration of relative localization and formation control in such scenarios.

The aim of this study is to investigate the problem of cooperative multi-robot variation parameter design for dynamic formation maneuvers with bearing constraints. Notably, scaling and translation are relatively economical bearing-preserving motions in terms of formation changes. Typically, the variation parameters, i.e., the desired scaling size and translation vector, are designed offline a priori, and it is often challenging to dynamically generate the desired formation in response to a changing ambient environment. This paper proposes an online distributed design method to determine the variation parameters of an entire formation. First, local variation policies are generated by the proposed high-order control barrier functions based on received local excitations from the environment. Subsequently, using the distributed average tracking technique, consensus filters are employed to integrate various local variation policies in a weighted-average manner, which ensures that the bearing is maintained in dynamic formation maneuvers. Finally, numerical simulations and experiments are conducted to demonstrate the effectiveness of the proposed method.Note to Practitioners—This paper is motivated by the neglect of the research on the automatic co-adjustment of the formation variation parameters in most existing formation control schemes, which rely on fixed and pre-defined desired variation parameters (scaling size and translation vector). To address this limitation, this paper suggests an online distributed design method to determine the variation parameters of an entire formation in dynamic ambient environments. The proposed method consists of three parts: 1) By considering received local excitations from the environment as perturbations to asymptotically stable virtual systems, unconstrained local variation policies are generated. 2) By employing high-order control barrier functions, we solve the bounded magnitude constraints for distributed average tracking (DAT) algorithms and the minimum scale constraint for collision avoidance, leading to the generation of constrained local variation policies. 3) By using DAT algorithms, all robots can cooperatively obtain a uniform variation parameter, which is exactly the weighted average of the constrained local variation policies. This ensures that the bearing is maintained in dynamic formation maneuvers. Therefore, the proposed method can be deployed to multi-robot systems in a distributed manner. Finally, numerical simulations and experiments are conducted to demonstrate the feasibility of the proposed method and its potential in industrial applications.

Task-oriented training with haptic rendering can boost robot-aided motor learning to tasks with similar dynamics. Although multi-DOF robots better match the rendering of real task scenarios, single-DOF haptic robots show great potential for home use with enhanced task rendering performance. This study presents our attempts to optimize and develop a single-DOF cable-driven robot with appropriate workspace and force rendering capacity. The core technologies consist of two aspects: 1) a multi-objective optimization method was adopted to obtain optimal configuration of the haptic robot; and 2) a slider-crank-mechanism-based portable cable-driven robot was developed. Performance evaluation experiments demonstrated that 1) the robot has a workspace larger than 300 mm; 2) the robot can achieve 40 N force output and 40 N. mm-1stiffness for hard contact; 3) the root mean square of the resistance during free motion is 0.93 N; 4) in the purely passive case (without motor compensation), the average resistance to back drive the motor is 2.5 N. These lead us to believe that the developed robot holds the promise to serve as a robotic rehabilitation training platform for home use on the neurological-impaired patients.

In many applications such as aerial transportation, delivery, and manipulation, it is essential to know the external wrench exerted on multirotor aerial vehicles precisely. This paper presents an algorithm to estimate external wrench using a rotor speed measurement unit, an inertial measurement unit and a motion capture system. Under a cascaded architecture containing two sub-systems, one error-state Kalman Filter is designed to estimate velocity and attitude and eliminate the bias of the measurement from the inertial measurement unit, the other error-state Kalman Filter is designed to estimate the external wrench. Observability of the two estimation subsystems is verified by the Lie derivative method. The proposed algorithm has been tested in simulations and real-world experiments, which demonstrates its superiority in providing real-time and accurate external wrench estimation.

When teleoperating a multirobot system (MRS) in outdoor environments, human operators can often detect obstacles that are not detected by robots and spot emergencies faster than robots do. However, the lack of efficient methods for operators to manipulate an MRS has limited the number of robots in a human-robot team. To handle this problem, a distributed hierarchical shared control scheme is proposed, aiming to provide a safe and flexible control interface for a few human operators to interact with a large MRS. The proposed hierarchical control scheme employs a two-layered structure. In the upper layer, intention field networks are designed to generate virtual human control signals. Two functionalities for human teleoperation, called: 1) group management and 2) motion intervention, are realized using intention fields, allowing the operators to split the robot formation into different groups and steer individual robots away from immediate danger. In parallel, a blending-based shared control algorithm is designed in the lower layer to resolve the conflict between human intervention inputs and autonomous formation control signals. The input-to-output stability (IOS) of the proposed distributed hierarchical shared control scheme is proved by exploiting the properties of weighting functions. Results from a usability testing experiment and a physical experiment are also presented to validate the effectiveness and practicability of the proposed method.

In this article, the distributed cooperative control problem of redundant mobile manipulators is investigated. A novel method is proposed to solve the problem by integrating formation control with constrained optimization, which not only transports the object along a reference trajectory in a distributed manner but also obtains the dexterous joint postures and end-effector displacements under safety constraints for collision avoidance. For the constrained optimization, the cost function and safety constraints are designed to quantify the mobility and manipulability of mobile manipulators, and collision-free working ranges with the object and obstacles, respectively. A discontinuous projected primal-dual algorithm with damping terms is proposed to solve the constrained optimization problem, providing the joint postures and end-effector displacements, which minimize the cost function and satisfy safety constraints. For the formation control, a finite-time control law, guided by end-effector displacements from the primal-dual algorithm, is developed in order to transport the object by establishing a prescribed formation and moving its centroid to track the reference trajectory. The cooperative manipulation is therefore achieved by the proposed method, which is further validated through numerical simulations.

In aerial cooperative transportation missions, it has been recognized that for small-sized but heavy payloads, the cable-suspended framework is a preferred manner. However, to maintain proper safe flight distances, cables always stay inclined, which implies that horizontal force components have to be generated by UAVs, and only partial thrust forces are used for gravity compensation. To overcome this drawback, in this paper, a new cooperative transportation system named Truss Aerial Transportation System (TATS) is proposed, where those horizontal forces can be internally compensated by the bar spherical joint structure. In the TATS, rigid bars can powerfully sustain the desired distances among UAVs for safe flight, resulting in a more compact and effective transportation system. Thanks to the structural advantage of the truss, the rigid bars can be made lightweight so as to minimize their induced gravity burden. The construction method of the proposed TATS is presented. The improvement in energy efficiency is analyzed and compared with the cable-suspended framework. Furthermore, the robustness property of a TATS configuration is evaluated by computing the margin capacity. Finally, a load test experiment is conducted on our made prototype, the results of which show the effectiveness and feasibility of the proposed TATS.

The human-agent collaboration (HAC) is a prospective research topic whose great applications and future scenarios have attracted vast attention. In a broad sense, the HAC system (HACS) can be broken down into six elements: "Man," "Agents," "Goal," "Network," "Environment," and "Tasks." By merging these elements and building a relation graph, this article proposes a systematic analysis framework for HACS, and attempts to make a comprehensive analysis of these elements and their relationships. We coin the abbreviation "MAGNET" to name the framework by stringing together the initials of the above six terms. The framework provides novel insights into analyzing various HAC patterns and integrates different types of HACSs in a unifying way. The presentation of the HACS framework is divided into two parts. This article, part I, presents the systematic analysis framework. Part II proposes a normalized two-stage top-level design procedure for designing an HACS from the perspective of MAGNET.

The human-agent collaboration (HAC) is a prospective research topic, whose great applications and future scenarios have attracted vast attention. It is very important to understand the design process of the HAC system (HACS). Inspired by the systematic analysis framework presented in Part I of this dual publication, this article proposes a normalized two-phase procedure, namely, GET-MAN, for the top-level design of HACS from the perspective of system engineering. The two-phase design procedure can produce a coherent and well-running HACS by sophisticatedly and properly determining the six elements of the HACS and their influences. In the verification phase of GET-MAN, by applying the formalized HACS framework proposed in Part I, a formal model can be constructed to look ahead (predict) and back (explain) at potential faults in the candidate HACS. An example of the HACS design for target searching is employed to illustrate the use of the GET-MAN design procedure. The potential challenges and future research directions are discussed in the light of the GET-MAN design procedure. The systematic analysis framework, Part I, as well as the GET-MAN design procedure, Part II, can serve as common guidance and reference for analyzing and developing various HACSs.

In this article, we study the problem of affine formation stabilization for multiagent systems in the plane. The challenges lie in the limited access to the information of the target formation in the sense that the prescribed values of the formation parameters, that is, the scaling size and rotation angle, are known only by one agent which we call the leader. Motivated by the fact that three agents (say, leaders) can determine the shape of a planar triangular formation using the stress matrix, we propose a class of estimators to guarantee that two agents in the leader set can gain access to the formation parameters. Then, an integrated control scheme is designed such that the target formation can be uniquely stabilized among all its affine transformations. The sufficient condition ensuring the stability of the closed-loop system is also given based on the cyclic-small-gain theorem. Simulations and experiments are carried out to show the effectiveness of the proposed control strategy.

This article proposes a decentralized collaboration scheme for the motion planning of multiagent systems under coupled linear temporal logic task specifications. In order to alleviate the massive computational complexity in centralized methods, coupled edges are introduced to decouple the product automata, and then the path of each agent is synthesized according to local messages. Furthermore, in order to achieve the real-time message exchange, the tableau and gossip protocol are employed during online communication, resulting in a distributed collaboration scheme. Finally, based on the resultant decoupled product automata, a united agent model is designed to deal with partial node failures, yielding a more robust collaboration scheme. Simulations are conducted to demonstrate the effectiveness and superiority of the proposed methods.

This article investigates the event-triggered leader-follower consensus control problem for linear continuous-time multiagent systems (MASs). A new consensus protocol and an event-triggered communication (ETC) strategy based on a closed-loop state estimator are designed. The closed-looped state estimator renders us more accurate state estimations, therefore the triggering times can be decreased while maintaining control performance. We show that the consensus of MASs can be achieved by employing the proposed control scheme. In addition, the Zeno-phenomena can be excluded. As a practical application, our method is successfully applied to the vehicle platoon control. The effectiveness is further verified by simulations conducted on the Prescan platform.

This paper discusses methods for growing tensegrity frameworks akin to what are now known as Henneberg constructions (HCs), which apply to bar-joint frameworks. In particular, this paper presents tensegrity framework versions of the three key HCs of vertex addition, edge splitting, and framework merging (where separate frameworks are combined into a larger framework). This is done for super stable tensegrity frameworks in an ambient 2-D or 3-D space. We start with the operation of adding a new vertex to an original super stable tensegrity framework, named vertex addition. We prove that the new tensegrity framework can be super stable as well if the new vertex is attached to the original framework by an appropriate number of members, which include struts or cables, with suitably assigned stresses. Edge splitting can be secured in R2(R3) by adding a vertex joined to three (four) existing vertices, two of which are connected by a member, and then removing that member. This procedure, with appropriate selection of struts or cables, preserves super-stability. In d-dimensional ambient space, merging two super stable frameworks sharing at least d+1 vertices that are in general positions, we show that the resulting tensegrity framework is still super stable. Based on these results, we further investigate the strategies of merging two super stable tensegrity frameworks in Rd, (d ∈ {2, 3}) that share fewer than d+1 vertices, and show how they may be merged through the insertion of struts or cables as appropriate between the two structures, with a super stable structure resulting from the merge.

This paper focuses on the simulation and comparison of different types of first-order decentralized sliding mode estimators (FDSMEs). From the previous works, three types of FDSMEs were presented and applied to solve the cooperative control problems. Utilizing the FDSME, a finite-time leader-follower tracking control algorithm is proposed for a networked single-integrator vehicle system. Then based on the existing structure of FDSME, a new compound FDSME is developed to improve the estimation performance. Simulation and comparison of all the presented FDSMEs are given in detail to evaluate the theoretical results. Finally, regulation rules of the parameters in the compound FDSME are summarized according to the simulation results.