The robust formation tracking problems (FTPs) are investigated for noncooperative heterogeneous multiagent systems (MASs). The parameters and state dimensions of each agent in the MASs can be heterogeneous. The leader is noncooperative and has an unknown external input. The followers are affected by uncertainties and disturbances. A distributed extended state observer is designed to estimate the leader's state and the unknown external input. Then a robust formation tracking control method is proposed. It turns out that the output formation tracking error can reduce to any size neighborhood near the origin by selecting appropriate parameters. Finally, the effectiveness of the proposed observer and controller is demonstrated by a numerical simulation example and a hardware-in-the-loop (HITL) simulation example.

This study addresses the task assignment problem with robot coalitions, as encountered in practical scenarios, such as multiplayer reach-avoid games. Unlike the classical assignment problem where a single robot performs each task, the problem considered here involves tasks that require execution by a robot coalition consisting of two robots. This task assignment problem is a special instance of 3-set packing problem, which is known to be nondeterministic polynomial time (NP)-hard. We introduce the concept of$\epsilon$-coalition-competitive equilibrium ($\epsilon$-CCE) to characterize a kind of approximate solution that offers guaranteed performance. A distributed auction algorithm is developed to find an$\epsilon$-CCE within a finite number of iterations. In addition, several enhancements have been implemented to adapt the auction algorithm for practical applications where the task assignment problem may vary over time. Numerical simulations demonstrate that the distributed algorithm achieves satisfactory approximation quality.

This article considers a subspace guarding game in high-dimensional space which consists of a play subspace and a target subspace. Two faster defenders as a team cooperate to protect the target subspace by capturing an attacker which strives to enter the target subspace from the play subspace without being captured. A closed-form solution is provided from the perspectives of kind and degree. Contributions of the work include the use of the attack subspace (AS) method to construct the barrier, by which the game winner can be perfectly predicted before the game starts. In addition to this inclusion, with the priori information about the game result, a critical payoff function is designed when the defenders can win the game. Then, the optimal strategy for each player is explicitly reformulated as a saddle-point equilibrium. Finally, we apply these theoretical results to two half-space and half-plane guarding games in 3-D space and 2-D plane, respectively. Since the entire achieved developments are analytical, they require a little memory without the computational burden and allow for real-time updates, beyond the capacity of the traditional Hamilton-Jacobi-Isaacs method. It is worth noting that this is the first time in the current work to consider the target guarding games for arbitrary high-dimensional space and in a fully analytical form.

This paper discusses the trajectory tracking problem for miniature fixed-wing UAVs subject to wind gust disturbances and heterogeneous uncertainties. Based on a simplified aerodynamic model, a 6-state translational model is derived from the fixed-wing UAV dynamics by assuming an ideal inner-loop attitude controller. Combining signal compensation and feedback linearization method, we propose a new robust trajectory tracking controller for planar motion control, which incorporates a nominal linear controller to achieve the prescribed convergence and a robust compensator to restrain the effect of disturbances and uncertainties, while a well-tuned PID controller is developed for altitude regulation. The proposed robust control scheme is implemented on a miniature fixed-wing UAV and the flight tests for different scenarios(including both strong and light wind) are performed. The experimental results are shown to demonstrate the effectiveness and robustness of the robust control scheme.

This article considers a multiplayer reach-avoid game between two adversarial teams in a general convex domain which consists of a target region and a play region. The evasion team, initially lying in the play region, aims to send as many team members into the target region as possible, while the pursuit team with its team members initially distributed in both play region and target region, strives to prevent that by capturing the evaders. We aim at investigating a task assignment about the pursuer-evader matching, which can maximize the number of the evaders who can be captured before reaching the target region safely when both teams play optimally. To address this, two winning regions for a group of pursuers to intercept an evader are determined by constructing an analytical barrier which divides these two parts. Then, a task assignment to guarantee the most evaders intercepted is provided by solving a simplified 0-1 integer programming instead of a nondeterministic polynomial problem, easing the computation burden dramatically. It is worth noting that except the task assignment, the whole analysis is analytical. Finally, simulation results are also presented.

This paper considers a reach-avoid game on a rectangular domain with two defenders and one attacker. The attacker aims to reach a specified edge of the game domain boundary, while the defenders strive to prevent that by capturing the attacker. First, we are concerned with the barrier, which is the boundary of the reach-avoid set, splitting the state space into two disjoint parts: 1) defender dominance region (DDR) and 2) attacker dominance region (ADR). For the initial states lying in the DDR, there exists a strategy for the defenders to intercept the attacker regardless of the attacker's best effort, while for the initial states lying in the ADR, the attacker can always find a successful attack strategy. We propose an attack region method to construct the barrier analytically by employing Voronoi diagram and Apollonius circle for two kinds of speed ratios. Then, by taking practical payoff functions into considerations, we present optimal strategies for the players when their initial states lie in their winning regions, and show that the ADR is divided into several parts corresponding to different strategies for the players. Numerical approaches, which suffer from inherent inaccuracy, have already been utilized for multiplayer reach-avoid games, but computational complexity complicates solving such games and consequently hinders efficient on-line applications. However, this method can obtain the exact formulation of the barrier and is applicable for real-time updates.

Formation control problems for second-order unmanned aerial vehicle (UAV) swarm systems with switching topologies to achieve the time-varying formation and the predefined time-varying formation reference are investigated. The second-order UAV swarm systems considered are not only required to achieve the specified time-varying formation but also track the predefined time-varying formation reference, and the switching topologies are also taken into consideration. To achieve the predefined time-varying formation reference, a new time-varying formation protocol is proposed, and the formation problem is transformed into the consensus problem. Sufficient and necessary conditions for second-order UAV swarm systems modeled by double integrator to achieve the time-varying formation and the predefined time-varying formation reference are presented. Finally, a quadrotor formation platform consisting of three quadrotors is introduced. The simulation and experiment to demonstrate the efficiency of the proposed formation tracking theory are performed.

Output consensus problems for multi-agent systems under disturbances are investigated. Distributed dynamic consensus protocols, which estimate the state and the disturbances of each agent and distribute the estimations to other neighbor agents, are applied, and output consensus problems under disturbances are converted into output regulation problems. On this basis, a necessary and sufficient condition for output consensus in the form of a simultaneous stabilization condition and the solvability of regulator equations which have the same dimensions as the dynamic model of a single agent is shown, and an analytical expression of the output consensus function is obtained. Further, a sufficient condition for output consensualizability is given and a procedure for control protocol design is summarized. Finally, theoretical results are demonstrated by a numerical simulation example.

Output containment control problems for high-order linear time-invariant swarm systems are investigated. Firstly, output containment protocols are presented for leaders and followers respectively to make sure that the output dynamics property of leaders can be improved and the outputs of followers can converge to the convex hull formed by the outputs of leaders. Then output containment problems for swarm systems are transformed into stability problems, and sufficient conditions for swarm systems to achieve output containment are proposed. Moreover, an approach to determine the gain matrix in the output containment protocol is given, which has less calculation complexity. Finally, numerical simulations are presented to demonstrate theoretical results.

As an integral part of real-time vision system, there are two most important requirements for feature matching mechanisms: high computational efficiency for meeting the real-time demands, and high correct matching rate for ensuring the convergence and consistency of state estimation. Both of these are addressed and solved as an integrated whole by the efficient minimum-error active matching scheme proposed in this paper. Image processing is performed in a dynamically guided fashion by checking only parts of the image where positive matches are most probable. For achieving the global consensus matchings, rigorous analysis on how to minimize the matching errors in active matching by choosing an optimal search order is made. After that, practical feature matching algorithms are given, which have naturally absorbed the ideas of nearest neighbor (NN) and joint compatibility branch and bound (JCBB) approaches. Both statistical simulations and real-world experimental results have verified the proposed methods can perform better than the state-of-the-art algorithms, i.e. being able to obtain the best global consensus matchings with much lower computational cost.

In the matching tasks of tracking and geometrical vision, there are usually priors available on the absolute and/or relative image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search that can achieve global matching with much fewer image processing operations and lower overall computational cost. First, the concept of dynamic sequential search (DSS) is presented. Then, the problem of determining an optimal search order for DSS is investigated, when the probabilistic distribution of the features can be described by a multivariate Gaussian model. Based on the general formulas for sequentially updating the predicted positions of the features as well as their innovation covariance, the theoretic lower bound for the sum of the areas of the features search-regions is derived, and the necessary and sufficient condition for the optimal search order to approach this lower bound is presented. After that, an algorithm for dynamically determining a suboptimal search order is presented, with a computational complexity of O(n3), which is two magnitudes lower than those of the state-of-the-art algorithms. The effectiveness of the proposed method is validated by both statistical simulation and real-world experiments with a monocular visual SLAM (simultaneous localization and mapping) system. The results verify that the performance of the proposed method is better than the state-of-the-art algorithms, with both fewer image processing operations and lower overall computational cost.

This paper proposes a multi-component extended strong tracking filter (MESTer) for global localization. It is the first time strong tracking filter (STF) is introduced into robotics domain and is fundamentally extended to be suitable for fusing observations with arbitrary time-varying dimensionality, based on equivalent space transformation and extended orthogonality principle. The resulted extended strong tracking filter (ESTF) is then combined with a probabilistic multi-component evolving mechanism and finally forms the MESTer localization method. Real robot experiments and comparisons with existing methods show that MESTer has high convergence speed, computational efficiency and definite robustness to sensor noises, kidnapped robot problem, system nonlinearities, and symmetric environments.

This article presents an adaptive dynamic clustered particle filtering method for mobile robot global localization. The posterior distribution of robot pose in global localization is usually multimodal due to the symmetry of the environment and ambiguous detected features. Moreover, the multimodal distribution of the posterior varies as the robot moves and observations are obtained. Considering these characteristics, we use a set of clusters of particles to represent the posterior. These clusters are dynamically evolved corresponding to the varying posterior by merging the overlapping clusters and splitting the diffuse clusters or those whose particles gather to some sub-clusters inside. Further, in order to improve computational efficiency without sacrificing estimation accuracy, a mechanism for adapting the sample size of clusters is proposed. The theoretical lower bound of the number of particles needed to limit the estimation error is derived, based on the central limit theorem in multidimensional space and the statistic theory of ImportanceSampling (IS). Simulation results show the effectiveness of the proposed method, which is sufficient to achieve robust tracking of robot’s real pose and meanwhile significantly enhance the computational efficiency.

This paper presented a brief description for the gait planning of quadruped robot named Aibo ERS-7 which is a standard platform in the RoboCup 4-legged league. We approach a spline shaped locus to reduce the dimension of the parameter optimizing space and solve the problem of the significant bias between the planned locus and the real one. The result shows that the spline shaped locus is effective in finding the optimized locus shape in a short time. Finally, the robot achieves a gait faster than any previously known learned gait for Aibo

This paper proposes a design method of decentralized robust controllers for robots with joint backlash, flexibility and damping characteristics. For each joint subsystem, a robust tracking controller is designed in two steps: first, a nominal controller is designed for the nominal plant to get desired tracking performance, then a robust compensator is added to restrain the influence of the perturbation, that is the difference of the real plant from the nominal plant. The controller designed by the proposed method is a linear time-invariant one. It is shown that by applying the controller with a sufficiently wide frequency bandwidth robust output tracking property can be achieved in the contact phase while a new contact of the motor with the load in the correct direction is ensured in the backlash phase. An important feature of the method is that the controller parameters can be tuned on-line easily