Deception is a crucial strategy in adversarial scenarios, yet its application in multi-agent confrontations remains understudied. This paper investigates deception in a multi-robot Target-Attacker-Defender (MR-TAD) game, where Attackers aim to capture Targets while evading Defenders. To model deception effectively, we propose a hierarchical decision-making framework that integrates multi-agent reinforcement learning (MARL) for high-level deceptive strategies and optimal control for low-level motion control. Furthermore, we introduce a novel composite deception-oriented reward function, which combines hitting rewards, belief switch rewards, and position advantage rewards to facilitate the training of deceptive behaviors. Simulation results across varying numbers of robots demonstrate that incorporating deception significantly increases the success rate of Attackers, with an average improvement of over 70% compared to non-deceptive strategies. Additionally, real-world experiments with omnidirectional mobile robots further confirm the effectiveness of the proposed method. This study establishes a generalizable framework for modeling deception in multi-agent systems, with potential applications in various multi-agent scenarios.

Motivated by the unique state characteristics of the extended object and energy constraints in distributed sensor networks, this letter proposes a novel event-triggered hybrid consensus filter for distributed extended object tracking, achieving balanced estimation-communication performance. This parallel consensus mechanism processes three consensus operations on the prior information pair, novel information pair of kinematic state, and shape parameter information pair of extent state, enabling enhanced consensus and propagation of extended object characteristics across the network. To reduce data transmission while preserving estimation performance, the proposed event-triggered strategy contains three distinct transmission tests, performed in parallel on corresponding information pairs to evaluate information loss. Simulation results of a distributed extended object tracking case study demonstrate the superior performance of the proposed filter compared with conventional triggered filters. This work establishes an innovative effective parallel consensus mechanism for distributed extended object tracking.

This article presents a novel approach to learning-based distributed model predictive control (LDMPC) for nonconvex optimization problems which aims to enhance the distributed system’s consensus and avoid collision. Selecting the objective function of a distributed model predictive control (DMPC) system over a finite horizon to maximize performance and ensure safety is a challenging problem. The current work of this article is to introduce a function approximator that integrates DMPC and reinforcement learning (RL) through policy iteration (PI) to reconstruct the terminal cost function and reformulate the finite time nonconvex optimization problem. This work decouples the constraints and objective functions in the optimization process between multiple agents and introduces an improved alternating direction multiplier method (ADMM) as an consensus optimization solution of LDMPC. Moreover, the convergence, feasibility, and stability properties of our algorithm are proved in this article. The numerical example shows that the method updates can be performed distributively without inconsistency and demonstrates the effectiveness and safety of the LDMPC.

This study proposes a general multi-class hierarchical random network with an arbitrary number of agents and an arbitrary connecting success probability to analyze the minimum communication costs with the minimum consensus iterations in distributed multi-agent networks. The proposed hierarchical random networks facilitate a flexible network topology that is well-suited for implementing consensus filters and verifying network connectivity. Further, the study validates the connectivity conditions, determines the upper bound of diameters, and calculates the minimum number of expected network connections for the proposed multi-class hierarchical random network. Total network connections, communication costs, and root mean square errors of consensus fused estimations in the proposed multi-class hierarchical networks are compared via a 2-D target tracking scenario. The simulation result demonstrates that the proposed multi-class hierarchical consensus filter can achieve maneuvering target tracking while avoiding the polynomial increment of communication costs.

The problem of multiagent encirclement with multiobstacle collision avoidance (EMOCA) has been challenging since it is difficult to balance the tradeoff between surrounding a mobile target and avoiding obstacles simultaneously. To address the EMOCA problem, we proposed a novel policy-guided reinforcement learning (RL) method, namely, multiregulator-assisted RL for encirclement control (MRA-RLEC) which leverages the jump-start learning and curriculum learning (CL) mechanism to enhance training efficiency. MRA-RLEC divides the complex encirclement task into a sequence of subtasks, progressively increasing in difficulty. In this process, multiple regulators are utilized to adjust various training aspects, including encirclement condition, obstacle avoidance, and the transition from guide to learned policy execution. Besides, a global encirclement reward decomposition (GERD) method is presented to alleviate reward sparsity, and we design a bidirectional communication protocol to reduce communication. Extensive experiments are carried out to showcase the robustness and superiority of our method, and the practical applicability of MRA-RLEC is demonstrated through experiments conducted in the robot operating system 2 (ROS2)-based simulation platform, Gazebo, using a self-designed omnidirectional vehicle model.

—paper addresses communication bandwidth reduction and energy efficiency enhancement of a peer-to-peer sensor network for distributed target detection and tracking. A distributed Bernoulli filter with event-triggered communication is developed where each node broadcasts only local posteriors that achieve significant information gain. Specifically, for the cases where the Bernoulli density is no-target or single-target, the corresponding event-triggered strategies are constructed, respectively, in which the information discrepancy is measured via the Jeffreys divergence, and the triggering threshold is determined by the local information confidence coefficient. In addition, the presented method is combined with flooding protocol for internode communication, and weighted conservative fusion approaches are used to fuse the target existence probabilities and spatial distributions. Finally, simulation results demonstrate the effectiveness and superiority of the proposed approach.

A distributed multi-sensor control method is presented for multi-target tracking. The problem is formulated as auctioned partially observed Markov decision processes (auctioned POMDPs), which is a tractable approach to approximate the solutions in a distributed manner. To ensure adequate coverage of the multi-sensor system, a sparsity-promoting objective function is also designed to reduce overlapping sensing areas, balancing a tradeoff between the control reward and sensor coverage. Simulation results demonstrate that the proposed distributed method achieves comparable tracking performance to the state-of-art centralized approach. Furthermore, the proposed sparsity-promoting objective function outperforms the conventional Cauchy-Schwarz divergence (CSD) in discovery performance.

An event-triggered consensus filter is proposed in this letter for state estimation in distributed sensor networks based on the hybrid consensus on measurement and consensus on information scheme. For bandwidth reduction and energy saving, an event-triggered transmission strategy is developed in which each node selectively transmits only the most relevant data so as to reduce data transmission while preserving the filtering performance. Two different transmission tests are performed in parallel, respectively on the prior and on the likelihood information pair, to evaluate the information loss (measured in terms of Kullback-Leibler divergence) that would be incurred if the current values were replaced by the predicted ones according to the last transmitted data. Simulation results on a distributed target tracking case-study demonstrate outperformance of the proposed filter with respect to conventional triggered filters.

An adaptive consensus filter for sensor networks with unknown process and measurement noise statistics is proposed in this letter. The variational Bayes(VB) approach is exploited to get local estimates of unknown noise covariances with prior inverse Wishart distributions. A distributed averaging approach on exponential-class densities is applied for consensus on the natural parameters of the unknown predicted error covariance. Consensus on measurements is performed in parallel and the two consensus outcomes are fused. Simulation results demonstrate the effectiveness of the proposed adaptive consensus filter compared to conventional, non-adaptive, consensus filters.

This paper deals with the optimum dynamic positioning control problem for marine ships in the presence of actuator gain uncertainties and unknown environmental disturbances. The proposed approach is formulated as two modules, i.e., the guidance part and the control part. By utilizing the improved extremum seeking algorithm, the optimum-seeking guidance is developed in this note to generate the reasonable heading guidance for dynamic positioning ships. The main purpose of this design is to ensure the closed-loop system running efficiently and environment-friendly in practice. Combined with the proposed guidance principle, a robust neural control algorithm is developed based on the dynamic surface control, neural networks, and the robust neural damping technique. In this algorithm, the strong couplings of state variables and the gain uncertainty of actuators are tackled, and the system uncertainties are compensated requiring less (or no) information of the hydrodynamic structure, the actuator model and the external disturbances. Considerable effort is made to guarantee the semiglobal uniform ultimate bounded stability by employing the Lyapunov theory. The advantages of the proposed control scheme could be summarized as two points. First, the control approach is with the properties of optimization and energy-saving, which is meaningful for applying the theoretical algorithm. Second, the pitch ratio of thrusters is selected as the control inputs of interest, which is measurable in the practical plant. These characteristics would facilitate the implementation of the algorithm in engineering. Two examples are provided to verify the performance of the proposed scheme.

In the field of infrared remote sensing, the problem of IR small target detection is still an important component part. Concerning infrared dim small target (IRDST) detection, firstly the infrared image is processed with DWT method to get the wavelet coefficients image, but the distribution characteristics, such as scales, frequencies, orientations of wavelet coefficients in different sub-bands are various, so wavelet image denoised by a single threshold criterion can not give a satisfying estimation. Based on this motivation, a spatially adaptive multi-model de-noising strategy (SAMMDS) based IRDST detection method is proposed in this paper, which can adjust thresholding strategy according to the distribution of noise in different scales and directions. Spatially adaptive BayesShrink (SABS) thresholding, traditional BayesShrink (BS) thresholding and generalized cross validation (GCV) thresholding are all adopted here to process each sub-band separately. After reconstructing the denoised wavelet image, a simple global thresholding is used to separate the background and target finally. Experimental results demonstrate that the proposed algorithm performs better than other typical wavelet methods for small target detection with various complex backgrounds.

To assess the performance of image quality metrics (IQMs), some regressions, such as logistic regression and polynomial regression, are used to correlate objective ratings with subjective scores. However, some defects in optimality are shown in these regressions. In this correspondence, monotonic regression (MR) is found to be an effective correlation method in the performance assessment of IQMs. Both theoretical analysis and experimental results have proven that MR performs better than any other regression. We believe that MR could be an effective tool for performance assessment in the IQM research.

Abstract IP autoconfiguration of the mobile node addresses is important in the practical usage of most mobilead hocnetworks (MANETs). This paper proposes domain‐based autoconfiguration framework (DACF), a novel approach for the efficient address autoconfiguration of MANETs. To construct a hierarchy of addresses, this framework defines a loose domain structure where nodes in the same domain may roam to different locations after they are configured. This framework uses the passive Duplicate Address Detection(PDAD) but the proposed domain structure is able to reduce the initial conflict probability and accelerate the conflict resolution significantly. To evaluate the correctness and efficiency of the proposed framework, we also present an exemplified full‐functioned implementation of the proposed framework. Through the detailed analysis and simulation, we believe the proposed scheme provides a promising autoconfiguration framework for large‐scale MANETs. Copyright © 2008 John Wiley & Sons, Ltd.