Swarm Robots Team

（群ロボットチーム）

■ Research Topic

   1) Decentralized Coordination for Robot swarms

   2) Consensus building for Robot swarms

   3) Sensor Networks with RFID

■ Members

• Current members

• Old members

■ Research Introduction

• Adaptive Self-configuration

We study a motion planning framework for a large number of autonomous robots that enables the robots to configure themselves adaptively into an area of an arbitrary geometry. A locally interacting geometric technique provides a unique solution that allows the robots to converge to the uniform distribution by forming an equilateral triangle with their two neighbors. The basic idea underlying the proposed solution is that robots can be thought of as liquid particles that change their relative positions conforming to the shape of the container they occupy. Specifically, it is assumed that robots are not allowed to have the identification number, a pre-determined leader, a common coordinate system, and communication capabilities. Under such minimal conditions, the convergence of the algorithm is mathematically proved and verified through extensive simulations. The results validate the feasibility of applying the algorithm to self-configuration of mobile sensors across the constrained environment

• Adaptive Flocking

We study a distributed approach for adaptive flocking of swarms of mobile robots that enables to navigate autonomously in complex environments populated with obstacles. Based on the observation of the swimming behavior of a school of fish, we propose an integrated algorithm that allows a swarm of robots to navigate in a coordinated manner, split into multiple swarms, or merge with other swarms according to the environment conditions. We prove the convergence of the proposed algorithm using Lyapunov stability theory. We also verify the effectiveness of the algorithm through extensive simulations, where a swarm of robots repeats the process of splitting and merging while passing around multiple stationary and moving obstacles. The simulation results show that the proposed algorithm is scalable, and robust to variations in the sensing capability of individual robots. It is also confirmed that the algorithm is scalable and robust to variations in the sensing capability of individual robots.

• Development of Dual Rotating Infrared (DRIr) sensor system

We are developing a new target acquisition and tracking system using infrared sensors that enables to easily deploy and/or generate formation of geometric patterns. In cooperative robot swarms, robots are required to move toward a desired position by tracking either a leader or a neighbor robot. Based on the observation of the foraging behavior of bats, we propose an efficient and simple yet reliable approach to tracking a moving robot by combining various modes of scanning observation. For this, the prototype dual rotating infrared (DRIr) sensors are developed to make it possible to self-modulate the scanning angle and frequency. Specifically, a pair of DRIr sensors is mounted on the front and rear edge of each mobile robot for the real-time distance measurement in all directions. Now, utilizing the DRIr sensors, we experimentally implement and validate the self-deployment algorithm with the mobile robot swarms.

• Past research: Decentralized formation control for small-scale robot teams with anonymity

We studied a formation control framework for small-scale mobile robot teams that could adjust their formation to adapt to various situations. We proposed the self-organizing strategy, built on the following assumptions; anonymity, disagreement on common coordinate systems, and no pre-selected leader. Given arbitrarily distributed states of unknown robots, the proposed framework facilitated a self-organized movement of the team through five phases, including computation of common origin, leader selection, setting common direction, acquiring common coordinates, and issuing IDs. Based on these features, we decomposed the problem of formation control into three functions, pattern generation, flocking, and pattern switching. Specifically, we proposed two formation control approaches. The leader-referenced approach used the selected leader as the reference point for the position of the remaining followers. In contrast, the neighbor-referenced approach enabled each robot to maintain position with respect to their neighbor. We also proposed hybrid formation control, in which the advantages of each method could be applied to specific situations. Both approaches are verified using an in-house simulator and physical mobile robots.

Our formation control approaches for a self-organizing robot team offered robustness against individual failures and flexibility in adapting to changing environments. In addition, the movement of individual robots could converge toward their target position. Two fundamental contributions of this work are: (1) a wide variety of formations can be made in a decentralized way, adapting to an environment only by observing other robots that are anonymous and (2) the same or similar formations can be recovered in spite of a lack of some participating members resulting from individual failures. Implementation on real robots could be accomplished without high quality sensors and equipment. This allows us to organize a team with simple, economical units which we can easily deploy even in hazardous environments.