Xingguang Peng

Verified

Xingguang verified their affiliation via an institutional email.

Learn more

Verified

Xingguang verified their affiliation via an institutional email.

Learn more

• Professor
• Professor at Northwestern Polytechnical University

A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce th...

We investigate collective dynamics in a binary mixture of programmable robots in experiments and simulations. While robots of the same species align their motion direction, interaction between species is distinctly nonreciprocal: species A aligns with B and species B antialigns with A. This nonreciprocal interaction gives rise to the emergence of c...

Homogeneous swarm robots are of significant research interest due to their robustness, flexibility, and scalability in completing complex tasks across various applications. This paper focuses on trapping heterogeneous targets using swarm robots, with emphasis on their different strengths. These targets consist of weak, strong, and group-moving indi...

Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveragi...

Collective motion, such as milling, flocking, and collective turning, is a common and captivating phenomenon in nature, which arises in a group of many self-propelled individuals using local interaction mechanisms. Recently, vision-based mechanisms, which establish the relationship between visual inputs and motion decisions, have been applied to mo...

Swarm systems consist of a large number of interacting individuals, which exhibit complex behavior despite having simple interaction rules. However, crafting individual motion policies that can manifest desired collective behaviors poses a significant challenge due to the intricate relationship between individual policies and swarm dynamics. This p...

Milling is a collective behavior that is useful in a variety of real scenarios, but how to regulate milling behavior simply and efficiently is still a challenging problem. This letter introduces a novel method for controlling milling behavior in real-world robots, where both the direction and radius of the milling pattern can be continuously adjust...

Vision is a critical sensing modality for underwater robots. However, a limited field of view (FoV) significantly hampers vision-based distributed swarm control, making it difficult for the swarm to maintain cohesion and preventing the emergence of typical collective behaviors. In this study, we present a novel model to address this challenge. By i...

Plenty of local interaction mechanisms have been proposed to achieve collective behaviors in swarm robotics. However, these mechanisms require robots to explicitly obtain the velocity of their neighbors as the sensory input to make motion decisions. This further poses great challenges in real-world applications of swarm robotics. In this letter, in...

In swarm robotics, multi-target trapping usually relies on global information or explicit communication, posing a challenge for robots to autonomously self-organize and trap multiple targets with only local perceptual data. We present a self-regulated density-based approach for self-organized multi-target trapping. This method employs density-based...

Heterogeneity is a common feature in natural swarms, such as variations in mobility, perception ability, etc., among particles. In this study, we investigate the collective behavior considering both dynamic heterogeneity and time delay heterogeneity, based on a model with symmetric attractive forces. Three patterns emerge from our model: translatin...

This article presents a distributed control strategy for herding groups of evaders towards a predefined goal region using a team of robotic herders. In herding problems, evaders tend to move away from each other to increase their coverage regions. This makes it challenging to develop control solutions since the wandering evaders need to be collecte...

The dynamics of swarm robotic systems are complex and often nonlinear. One key issue is to design the controllers of a large number of simple, low-cost robots so that emergence can be observed. This paper presents a sensor and computation-friendly controller for swarm robotic systems inspired by the mechanisms observed in algae. The aim is to achie...

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in a...

As one of the most common and spectacular manifestations of coordinated behavior, collective motion is the spontaneous emergence of the ordered movement in a system consisting of many self-propelled agents, e.g., flocks of birds, schools of fish, herds of animals, and human crowds. Despite extensive studies on collective motions, a systems-level un...

In cross-domain scenarios, the simultaneous presence of multiple sensing delays exerts a profound influence on collective behavior. Motivated by this, our paper presents a system based on self-propelled particles that consists of two swarms containing two intra-swarm sensing delays and an inter-swarm sensing delay. Three state emerges from the syst...

•A Vicsek-like model with heading estimation via focal observation is proposed. •The noise of observation can induce a continuous phase transition. •The PDF of noise is essential to determining the type of phase transition. •The kinetic of the system is affected by noise amplitude and weight.

Collective behavior is a form of movement that emerges to the whole through simple rules of interaction defined between individuals. The essence of the formation of collective behaviors is the exchange of information between individuals. From the air, to the land, to the sea, the formation of swarm system is always accompanied by the exchange of in...

With the evolution of unmanned multi-agent systems, research on multi-agent confrontation has been more important. And how to deal with multi-agent system attacks for effective self-organized defense becomes an urgent problem. The previous studies of multi-agent systems confrontation have focused more on multi-agent objective assignment methods and...

With the development of swarm intelligence and low-cost unmanned systems, the offence and defense of a swarm have become essential issues in defense and security technologies. A swarm of drones can be used to attack some high-value units (HVUs), such as bases or fuel tanks. Moreover, some moving HVUs such as cargo ships are also greatly threatened...

Herding a flock is a type of shepherding behaviour where a group of robots guide a flock to a predefined goal region. Existing literature for herding assumes that the flocks are coherent; however, this limits the practicality of these approaches. In fact, herding animals prefer to move away from each other toward more abundant pasture regions. Such...

In this paper we revisit the self-organized collective behavior of swarms with density field interaction that is inspired from the model of smoothed particle hydrodynamics. For a homogeneous swarming system a novel collective fission behavior is seen to emerge where equal to or more than two sub-clusters spontaneously come from a single connected c...

Plenty of empirical evidence on biological swarms reveal that interaction between individuals is selective. Each individual’s neighbor is selected based on one or more featured factors. Based on the self-propelled model, we develop a general probability neighbor selection framework to study the effect of four typical featured factors (i.e., distanc...

In transfer learning (TL) for multiagent reinforcement learning (MARL), most popular methods are based on action advising scheme, in which skilled agents directly transfer actions, i.e., explicit knowledge, to other agents. However, this scheme requires an inquiry-answer process, which quadratically increases the computational load as the number of...

The swarm system is a kind of multi-agent system where amazing “emergence” phenomenon can be observed. In recent years, biologists have made great effort to model the self-organizing rules of collective behaviors. By analyzing the data collected from biological swarm systems, a plenty of models have been built to reproduce and explain the collectiv...

Depth-averaged current velocities (DACVs) play an important role in marine scientific research, especially for navigation and path planning of autonomous underwater gliders (AUG). In this paper, we propose a DACV prediction model based on variational mode decomposition (VMD), sparrow search algorithm (SSA) and least squares support vector machine (...

Many real-world optimization tasks suffer from noise. So far, the research on noise-tolerant optimization algorithms is still restricted to low-dimensional problems with less than 100 decision variables. In reality, many problems are high-dimensional. Cooperative coevolutionary (CC) algorithms based on a divide-and-conquer strategy are promising in...

Knowledge transfer is widely adopted in accelerating multiagent reinforcement learning (MARL). To accelerate the learning speed of MARL for learning-from scratch agents, in this paper, we propose a Stationary and Scalable knowledge transfer approach based on Experience Sharing (S2ES). The mainframe of our approach is structured into three component...

Identifying the interaction of search variables of black-box optimization problem is beneficial for optimization task. However, very little research pay attention to the quality of information source, i.e. what information is beneficial for identifying the interactions between variables. In this paper, we propose a new method that utilizes multiple...

Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into differ...

Inspired by the morphogenesis of biological organisms, gene regulatory network-based methods have been used in complex pattern formation of swarm robotic systems. In this article, obstacle information was embedded into the gene regulatory network model to make the robots trap targets with an expected pattern while avoiding obstacles in a distribute...

This paper present an active search navigation model for AUV used geophysical field information in natal homing mission. We consider that the animals homing behavior can be regarded as a ability of search for destination multi-parameter geophysical field without any priori map. Here,a hypothesis is proposed that natal homing can be generalized as a...

Inspired by the morphogenesis of biological organisms, gene regulatory network (GRN) based methods have been used in complex pattern formation of swarm robotic systems. In this paper, obstacle information was embedded into the GRN model to enhance the robots trap targets with a expected pattern while avoiding the obstacles in a distributed manner....

Cooperative co-evolutionary algorithm (CCEA) decomposes a problem into several subcomponents and optimizes them separately. This divide-and-conquer feature endows CCEAs with the capability of distributed and high-efficiency problem solving. However, traditional CCEAs trend to converge to Nash equilibrium rather than the global optimum due to inform...

For low-speed underwater vehicles, the ocean currents has a great influence on them, and the changes in ocean currents is complex and continuous, thus whose impact must be taken into consideration in the path planning. There are still lack of authoritative indicator and method for the cooperating path planning. The calculation of the voyage time is...

Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by limited neighbors’ information in the real environment, we propose a GRN-based algorithm with asymmetric information for swarm-robot pattern formation. Through this algorithm, the neig...

In order to counteract the pathologies of cooperative coevolutionary algorithms(CCEAs) caused by information loss when dealing with problem decomposition, the information compensation strategy is investigated with respect to the dynamic nature of the landscapes of the CCEAs. A dynamic multi-population evaluation based anti-pathology CCEA is propose...

This paper considers a novel algorithm for the routing problem of autonomous underwater vehicles (AUVs) in order to deliver customized sensor packages to mission targets at scattered positions. We aim to utilize a set of AUVs to serve all the targets for exactly once on the premise of individual limited sensor packages loading ability while guarant...

Dependence on the prior magnetic map become one of the key problems which restrict the development of the geomagnetic navigation. This paper inspired from the animal navigation behavior which dispense with the priori geomagnetic map. First, we generalize the bio-inspired navigation process as a multi-objective problems. Then, present a stress evolu...

This chapter aims to solve the online path planning (OPP) and dynamic target assignment problems for the multiple unmanned aerial combat vehicles (UCAVs) anti-ground attack task using evolutionary algorithms (EAs). For the OPP problem, a model predictive control framework is adopted to continuously update the environmental information for the plann...

The fission behavior of flocks manifests as the autonomous splitting of a coherent group into multiple subgroups under external stimulus. The traditional flocking algorithms based on the interaction rules of attraction, repulsion and alignment have the properties of state consensus which encumber the process of group splitting. In this paper, a nov...

Online path planning (OPP) for unmanned aerial vehicles (UAVs) is a basic issue of intelligent flight and is indeed a dynamic multi?objective optimization problem (DMOP). In this paper, an OPP framework is proposed in the sense of model predictive control (MPC) to continuously update the environmental information for the planner. For solving the DM...

Intelligent flight is a key technology for an unmanned aerial vehicle (UAV) to react to the changing environment. Online path planning (OPP) is a basic issue for intelligent flight and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, we use an OPP scheme in the sense of model predictive control to continuously update...

In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-M...

Online path planning (OPP) is the basic issue of some complex mission and is indeed a dynamic multi-objective optimization problem (DMOP). In this paper, we use an OPP scheme in the sense of model predictive control (MPC) to continuously update the environmental information for the planner. For solving the DMOP involved in the MPC-like OPP a dynami...

This paper focuses on the effect of population diversity to environment identification-based memory scheme (EI-MMS) which heuristically compensates population diversity through the storage and retrieving process of historic information. We introduced several diversity compensation measures and combined them with EI-MMS based univariate marginal dis...

A memory enhanced estimation of distribution algorithm (M-EDA) is proposed to solve binary-coded dynamic optimization problems (DOPs), in which a probability model is treated as the basic memory element and reused in new environments. A memory management scheme based on environment identification method is designed and the population diversity is c...

Formation control problem is an important issue in formation flying of unmanned combat aerial vehicles (UCAVs). A dynamic genetic algorithm based on particle filter (PFDGA)was proposed to solve this dynamic optimal control problem. Within this algorithm, the genetic algorithm (GA) and the particle filter (PF) are properly combined together. The GA...

Coordinated planning is a key technology for multiple Unmanned Combat Aerial Vehicles (UCAVs) control problem. In this paper, the velocity of each UCAV is planned to coordinate the sequence, in which the UCAVs pass their critical waypoints. The coordinated planning problem is formulated as a multi-objective optimal one. The objective consists in mi...

The multiple Unmanned Aerial Vehicles (UAVs) reconnaissance problem with stochastic observation time (MURSOT) is modeled by modifying the typical vehicle routing problem with stochastic demand (VRPSD). The objective consists in optimizing mission duration, total time and the quantity of UAVs. This multi-objective optimization problem is solved usin...