A Hybrid ACO/PSO Control Algorithm for Distributed Swarm Robots Yan Meng and Ọlọrundamilọla Kazeem Department of Electrical and Computer Engineering Stevens Institute of Technology, New Jersey, USA yan.meng@stevens.edu, okazeem@stevens.edu Juan C. Muller Department of Computer Science and Mathematics New Jersey City University, New Jersey, USA jmuller@njcu.edu Abstract— In this paper, we present a hybrid Ant Colony Optimization/Particle Swarm Optimization (ACO/PSO) control algorithm for distributed swarm robots, where each robot can only communicate with its neighbors within its communication range. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. This hybrid ACO/PSO architecture adopts the feedback mechanism from environment of ACO and the adaptive interplay among agents of PSO to create a dynamic optimization system, and it is well-suited for a large scale distributed multi-agent system under dynamic environments. Furthermore, a pheromone- edge pair propagation funneling method is developed to reduce the communication overhead among robots. The simulation results concretely demonstrate the robustness, scalability, and individual simplicity of the proposed control architecture in a swarm robot system with real-world constraints. I. INTRODUCTION The main challenges for swarm robots are to create intelligent agents that adapt their behaviors based on interaction with the environment and other robots, to become more proficient in their tasks over time, and to adapt to new situations as they occur. Such ability is crucial for developing robots in human environments. Swarm robots are often observed to display many of the attributes, such as robustness, adaptability, flexibility, and self-organization, which are typical in collective intelligent system in general. Typical problem domains for the study of swarm-based robotic systems include foraging [1], box-pushing [2], aggregation and segregation [3], formation forming [4], cooperative mapping [5], soccer tournaments [6], site preparation [7], sorting [8], and collective construction [9]. All of these systems consist of multiple robots or embodied simulated agents acting autonomously based on their own individual decisions. However, not all of these control architectures are scalable to a large number of robots. For instance, most approaches rely on extensive global communication for cooperation of swarm robots, which may yield stressing communication bottlenecks. Furthermore, the global communication requires high-power onboard transceivers in a large scale environment. However, most swarm robots are only equipped very limited sensing and communication capability. An alternative paradigm to tackle the scalability issue for swarm robots while maintaining robustness and individual simplicity is through Swarm Intelligence (SI), which is an innovative computational and behavioral metaphor for solving distributed problems by taking its inspiration from the behavior of social insects swarming, flocking, herding, and shoaling phenomena in vertebrates, where social insect colonies are able to build sophisticated structures and regulate the activities of millions of individuals by endowing each individual with simple rules based on local perception. The abilities of such natural systems appear to transcend the abilities of the constituent individual agents. In most biological cases studies so far, robust and coordinated group behavior has been found to be mediated by nothing more than a small set of simple local interactions between individuals, and between individuals and the environment. The SI-based approaches emphasize self-organization, distributedness, parallelism, and exploitation of direct (peer- to-peer) or indirect (via the environment) local communication mechanisms among relatively simple agents. Reynold [10] built a computer simulation to model the motion of a flock of birds, called boids. He believes the motion of the boids, as a whole, is the result of the actions of each individual member that follow some simple rules. Ward et al. [11] evolved e-boids, groups of artificial fish capable of displaying schooling behavior. Spector et al. [12] used a genetic programming to evolve group behaviors for flying agents in a simulated environment. The above mentioned works suggest that artificial evolution can be successfully applied to synthesize effective collective behaviors. And the swarm-bot [13] developed a new robotic system consisting of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other depends on different environments and applications. Payton et al. [14] proposed pheromone robotics, which was modeled after the chemical insects, such as ants, use to communicate. Instead of spreading a chemical landmark in the environment, they used a virtual pheromone to spread information and create gradients in the information space. By using these virtual pheromones, the robots can send and receive directional communications to each other. The major contribution of this paper is that a SI-based coordination paradigm, i.e., a hybrid Ant Colony Optimization (ACO)/Particle Swarm Optimization (PSO), is proposed to achieve an optimal group behavior for large number of small-scale robots. Each robot adjusts its movement behavior based on a target utility function, which is defined as the fitness value of moving to different areas using the onboard sensing inputs and shared information through local communication. Similar to [14], inspired by 273 Proceedings of the 2007 IEEE Swarm Intelligence Symposium (SIS 2007) 1-4244-0708-7/07/$20.00 ©2007 IEEE