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.

Visual Place Recognition (VPR) is essential for robotics and autonomous driving, enabling localization by matching current observations with a database of known places. While monocular VPR methods rely on visual features, they are sensitive to environmental changes, and multimodal approaches using LiDAR or radar incur high costs and complexity. Multi-view camera configurations offer a cost-effective alternative by expanding perception range and providing richer structural information. In this work, we propose IMVPR, an implicit BEV-enhanced multi-view place recognition network that achieves consistent and parallel multi-view feature fusion and place descriptors aggregation. Unlike methods that explicitly construct BEV features, we introduce descriptor queries to implicitly represent 3D spatial locations, facilitating spatial point projection-based fusion. A cross-attention mechanism further enables end-to-end multi-view feature aggregation. We evaluate IMVPR on four scenes from the nuScenes dataset, including both in-domain and out-of-domain scenarios, demonstrating its superior accuracy and generalization compared to state-of-the-art methods, including multimodal approaches. Our results highlight the potential of multi-view vision-based methods as a scalable and robust solution for VPR.

To address the challenges of localization drift and perception-planning coupling in unmanned aerial vehicles (UAVs) operating in open-top scenarios (e.g., collapsed buildings, roofless mazes), this paper proposes EAROL, a novel framework with a downward-mounted tilted LiDAR configuration (20° inclination), integrating a LiDAR-Inertial Odometry (LIO) system and a hierarchical trajectory-yaw optimization algorithm. The hardware innovation enables constraint enhancement via dense ground point cloud acquisition and forward environmental awareness for dynamic obstacle detection. A tightly-coupled LIO system, empowered by an Iterative Error-State Kalman Filter (IESKF) with dynamic motion compensation, achieves high level 6-DoF localization accuracy in feature-sparse environments. The planner, augmented by environment, balancing environmental exploration, target tracking precision, and energy efficiency. Physical experiments demonstrate 81% tracking error reduction, 22% improvement in perceptual coverage, and near-zero vertical drift across indoor maze and 60-meter-scale outdoor scenarios. This work proposes a hardware-algorithm co-design paradigm, offering a robust solution for UAV autonomy in post-disaster search and rescue missions. We will release our software and hardware as an open-source package3for the community. Video: https://youtu.be/7av2ueLSiYw.

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.

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.

In this article, the problem of distributed robust fault estimation (FE) for leader-follower multiagent systems using relative measurements is considered. A distributed intermediate-based fault estimator is constructed using the local relative measurements and the state estimation from neighbors. The gain matrices of the fault estimator are calculated based on H∞performance in terms of linear matrix inequality (LMI) to improve the robustness of the estimator. Then, the LMI is separated and simplified by spectral decomposition, and its equivalent condition is proposed based on the maximum and minimum eigenvalue. A distributed eigenvalue estimation algorithm based on the power method is presented to fully distribute the proposed FE scheme. Finally, the numerical simulations are provided to verify the effectiveness of the proposed scheme.

Coalition formation is an important coordination problem in multi-agent systems, and a proper description of collaborative abilities for agents is the basic and key precondition in handling this problem. In this paper, a model of task-oriented collaborative abilities is established, where five task-oriented abilities are extracted to form a collaborative ability vector. A task demand vector is also described. In addition, a method of coalition formation with stochastic mechanism is proposed to reduce excessive competitions. An artificial intelligent algorithm is proposed to compensate for the difference between the expected and actual task requirements, which could improve the cognitive capabilities of agents for human commands. Simulations show the effectiveness of the proposed model and the distributed artificial intelligent algorithm.

The problem of flocking of second-order multiagent systems with connectivity preservation is investigated in this paper. First, for estimating the algebraic connectivity as well as the corresponding eigenvector, a new decentralized inverse power iteration scheme is formulated. Then, based on the estimation of the algebraic connectivity, a set of distributed gradient-based flocking control protocols is built with a new class of generalized hybrid potential fields which could guarantee collision avoidance, desired distance stabilization, and the connectivity of the underlying communication network simultaneously. What is important is that the proposed control scheme allows the existing edges to be broken without violation of connectivity constraints, and thus yields more flexibility of motions and reduces the communication cost for the multiagent system. In the end, nontrivial comparative simulations and experimental results are performed to demonstrate the effectiveness of the theoretical results and highlight the advantages of the proposed estimation scheme and control algorithm.

In this paper, we discussed the simultaneous intervention problem that arises from human-robot teams. The problem concerns with the case that one human operator has to intervene with several robots at almost the same time and it cannot be handled properly by existing methods. A model predictive control(MPC) based mixed initiative controller was proposed to solve this problem by embedding an intention model into the optimization problem. This method is in essence suitable for general MPC based mixed initiative controllers. The embedded intention model may cause the robot to deviate from its desired trajectory, for which conditions were developed to guarantee that the task completeness is not be affected. A comparison experiment was conducted and results showed that the proposed controller is more efficient and helpful in shortening the intervention time and allowing more robots to be successfully intervened in a simultaneous intervention scenario.

The coordination between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) is a proactive research topic whose great value of application has attracted vast attention. This paper outlines the motivations for studying the cooperative control of UAVs and UGVs, and attempts to make a comprehensive investigation and analysis on recent research in this field. First, a taxonomy for classification of existing unmanned aerial and ground vehicles systems (UAGVSs) is proposed, and a generalized optimization framework is developed to allow the decision-making problems for different types of UAGVSs to be described in a unified way. By following the proposed taxonomy, we show how different types of UAGVSs can be built to realize the goal of a common task, that is target tracking, and how optimization problems can be formulated for a UAGVS to perform specific tasks. This paper presents an optimization perspective to model and analyze different types of UAGVSs, and serves as a guidance and reference for developing UAGVSs.

Differential evolution (DE) and particle swarm optimization (PSO) are two formidable population-based optimizers (POs) that follow different philosophies and paradigms, which are successfully and widely applied in scientific and engineering research. The hybridization between DE and PSO represents a promising way to create more powerful optimizers, especially for specific problem solving. In the past decade, numerous hybrids of DE and PSO have emerged with diverse design ideas from many researchers. This paper attempts to comprehensively review the existing hybrids based on DE and PSO with the goal of collection of different ideas to build a systematic taxonomy of hybridization strategies. Taking into account five hybridization factors, i.e., the relationship between parent optimizers, hybridization level, operating order (OO), type of information transfer (TIT), and type of transferred information (TTI), we propose several classification mechanisms and a versatile taxonomy to differentiate and analyze various hybridization strategies. A large number of hybrids, which include the hybrids of DE and PSO and several other representative hybrids, are categorized according to the taxonomy. The taxonomy can be utilized not only as a tool to identify different hybridization strategies, but also as a reference to design hybrid optimizers. The tradeoff between exploration and exploitation regarding hybridization design is discussed and highlighted. Based on the taxonomy proposed, this paper also indicates several promising lines of research that are worthy of devotion in future.

High-precision autofarming is rapidly becoming a reality with the requirements of agricultural applications. Lots of research works have been focused on the automatic guidance control of farm vehicles, satisfactory results have been reported under the assumption that vehicles move without sliding. But unfortunately the pure rolling constraints are not always satisfied especially in agriculture applications where the working conditions are rough and not expectable. In this paper the problem of path following control of autonomous farm vehicles in presence of sliding is addressed. To take sliding effects into account, a vehicle-oriented kinematic model is constructed in which sliding effects are introduced as additive unknown parameters of the ideal kinematic model. Based on backstepping method a stepwise procedure is proposed to design an adaptive controller in which time-invariant sliding effects are learned and compensated by parameter adaptations. It is theoretically proven that for the farm vehicles subject to sliding, the lateral deviation can be stabilized near zero and the orientation errors converge into a neighborhood near the origin. To be more robust to disturbances including external noises and unmodeled time-varying sliding components, the adaptive controller is refined by integrating Variable Structure Controllers (VSC) or projection mappings. Simulation results show that the proposed robust adaptive controllers can reject sliding effects and guarantee high path-following accuracy.

In automatic guidance of agriculture vehicles, lateral control is not the only requirement. Lots of research works have been focused on trajectory tracking control which can provide high longitudinal-lateral control accuracy. Satisfactory results have been reported as soon as vehicles move without sliding. But unfortunately pure rolling constraints are not always satisfied especially in agriculture applications where working conditions are rough and not expectable. In this paper the problem of trajectory tracking control of autonomous farm vehicles in presence of sliding is addressed. To take sliding effects into account, two variables which characterize sliding effects are introduced into the kinematic model based on geometric and velocity constrains in presence of sliding. With linearization approximation a refined kinematic model is obtained in which sliding appears as additive unknown parameters to the ideal kinematic model. By integrating parameter adaptation technique with backstepping method, a stepwise procedure is proposed to design a robust adaptive controller. It is theoretically proven that for the farm vehicles subjected to sliding, the longitudinal-lateral deviations can be stabilized near zero and the orientation errors converge into a neighborhood near the origin. To be more realistic for agriculture applications, an adaptive controller with projection mapping is also proposed. Simulation results show that the proposed (robust) adaptive controllers can guarantee high trajectory tracking accuracy regardless of sliding.

In this paper the problem of interference avoidance for robots subject to dynamic constraints is investigated. First computed-torque method is used to obtain a linearized closed-loop system. For this linearized system the desired state that the robot is going to take at the next sampling period is checked by phase plane analysis to ensure the robot can be stopped without interferences, dynamic constraints are taken into account by calculating the bounds of the drive torques with interval evaluation. When the desired next state is not valid for interference avoidance, a new state is scheduled by optimizing the next velocity, interval analysis is used again which allows to partition the complex constrained optimization problem into a simple two-stage problem. The resulting optimal state not only secures the robot against interference but also leads the robot to trace the desired path closely. Simulation results of a 2-DOF robot arm show the effectiveness of the proposed approach.