Hong Shi

Bio: Hong Shi is an academic researcher from Peking University. The author has contributed to research in topics: Flocking (behavior) & Autonomous agent. The author has an hindex of 12, co-authored 18 publications receiving 615 citations. Previous affiliations of Hong Shi include Beijing Institute of Petrochemical Technology.

Virtual Leader Approach to Coordinated Control of Multiple Mobile Agents with Asymmetric Interactions

Abstract: This paper considers multiple mobile agents moving in the space with point mass dynamics. We introduce a set of coordination control laws that enable the group to generate the desired stable flocking motion. The control laws are a combination of attractive/repulsive and alignment forces, and the control law acting on each agent relies on the state information of its flockmates and the external reference signal. By using the control laws, all agent velocities asymptotically approach the desired velocity, collisions are avoided between the agents, and the final tight formation minimizes all agent global potentials. Moreover, we show that the velocity of the center of mass either is equal to the desired velocity or exponentially converges to it. Furthermore, when the velocity damping is taken into account, we can properly modify the control laws to generate the same stable flocking motion. Finally, for the case that not all agents know the desired common velocity, we show that the desired flocking motion can still be guaranteed. Numerical simulations are worked out to illustrate our theoretical results.

Swarming behavior of multi-agent systems

Abstract: We consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center in a finite time Moreover, we extend our results to more general attraction/repulsion functions Numerical simulations demonstrate that all agents will eventually enter into and remain in a bounded region around the swarm center which may exhibit complex spiral motion due to asymmetry of the coupling structure The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system

Flocking of multi-agent systems with a dynamic virtual leader

Abstract: This paper considers the flocking problem of a group of autonomous agents moving in Euclidean space with a virtual leader. We investigate the dynamic properties of the group for the case where the state of the virtual leader may be time-varying and the topology of the neighbouring relations between agents is dynamic. To track such a leader, we introduce a set of switching control laws that enable the entire group to generate the desired stable flocking motion. The control law acting on each agent relies on the state information of its neighbouring agents and the external reference signal (or ‘virtual leader’). Then we prove that, if the acceleration of the virtual leader is known, then each agent can follow the virtual leader, and the convergence rate of the centre of mass (CoM) can be estimated; if the acceleration is unknown, then the velocities of all agents asymptotically approach the velocity of the CoM, thus the flocking motion can be obtained. However, in this case, the final velocity of the group ...

Aggregation of foraging swarms

Abstract: In this paper, we consider an anisotropic swarm model with an attraction/repulsion function and study its aggregation properties It is shown that the swarm members will aggregate and eventually form a cohesive cluster of finite size around the swarm center We also study the swarm cohesiveness when the motion of each agent is a combination of the inter-individual interactions and the interaction of the agent with external environment Moreover, we extend our results to more general attraction/repulsion functions The model in this paper is more general than isotropic swarms and our results provide further insight into the effect of the interaction pattern on individual motion in a swarm system.

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination

Abstract: This paper reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

An Overview of Recent Progress in the Study of Distributed Multi-agent Coordination

Abstract: This article reviews some main results and progress in distributed multi-agent coordination, focusing on papers published in major control systems and robotics journals since 2006. Distributed coordination of multiple vehicles, including unmanned aerial vehicles, unmanned ground vehicles and unmanned underwater vehicles, has been a very active research subject studied extensively by the systems and control community. The recent results in this area are categorized into several directions, such as consensus, formation control, optimization, task assignment, and estimation. After the review, a short discussion section is included to summarize the existing research and to propose several promising research directions along with some open problems that are deemed important for further investigations.

Distributed Observers Design for Leader-Following Control of Multi-Agent Networks (Extended Version)

Abstract: This paper is concerned with a leader-follower problem for a multi-agent system with a switching interconnection topology. Distributed observers are designed for the second-order follower-agents, under the common assumption that the velocity of the active leader cannot be measured in real time. Some dynamic neighbor-based rules, consisting of distributed controllers and observers for the autonomous agents, are developed to keep updating the information of the leader. With the help of an explicitly constructed common Lyapunov function (CLF), it is proved that each agent can follow the active leader. Moreover, the tracking error is estimated even in a noisy environment. Finally, a numerical example is given for illustration.

Flocking of Multi-Agents With a Virtual Leader

Abstract: All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.