Naoki Tatebe

Deployment of wireless mesh network using RSSI-based swarm robots

This paper proposes a novel method for deploying a wireless mesh network (WMN) using a group of swarm robots equipped with wireless transceivers. The proposed method uses the rough relative positions of the robots estimated by their Radio Signal Strength Indicators (RSSIs) to deploy the WMN. The employed algorithm consists of three parts, namely, (1) a fully distributed and dynamic role decision method among the robots, (2) an adaptive direction control using the time difference of the RSSIs, and (3) a narrow corridor for the robots to pass by movement function along walls. In our study, we evaluated the performances of the proposed deployment method and a conventional method in a real environment using 12 real robots for simple deployment, and 10 real robots for passing the narrow corridor. The results of the performed experiments showed that (1) the proposed method outperformed the conventional method with regard to the deployment time, power consumption, and the distances traveled by the robots, and (2) the movement function along the walls is effective while passing a narrow corridor unlike any other function.

Energy-efficient construction algorithm for mobile mesh networks

In this paper, we propose an autonomous control method for the movement of distributed nodes in a mobile wireless mesh network. Our approach estimates the mutual position of nodes from the received signal strength indicator (RSSI), and constructs a network using a number of autonomous moving nodes to enable wireless communication. The proposed method controls the direction of node movement using two features: (1) the dynamic allocation of reference and moving nodes, and (2) the temporal variation of RSSI. To evaluate the effectiveness of our method, we employ three criteria. First, the area coverage ratio represents the degree of coverage of the target area with nodes such as sensors or communication devices. Second, the communication stability represents the latency and connectivity in the communication between nodes. Third, the energy consumption represents travel distance between nodes. Experimental results using a network simulator demonstrate that the proposed method achieves full area coverage using less than 3.8% of the energy required by the conventional method.

A self-localization estimation method with cooperating small robots Using camera positioning and movable landmarks

Recently, there has been extensive research on robot control using self-position estimation. Simultaneous Localization And Mapping (SLAM) is one approach to self-positioning estimation. In SLAM, robots use both autonomous position information from internal sensors and data from external landmarks. SLAM can improve the accuracy of position estimation with a large number of landmarks, but it involves a degree of uncertainty and has a high computational cost, because it requires detection and recognition of landmarks through image processing. To overcome this problem, we propose a new method involving the creation of maps and the measurement of position using two cooperating robots that serve as moving landmarks for each other. This makes it possible to solve problems of uncertainty and computational cost with two-dimensional markers, because a robot needs to find only a simple two-dimensional marker, rather than feature-points landmarks. In the proposed method, the robots have a two-dimensional marker of known shape and size and have a camera kept at the front of the robots sensing the markers to determine distance. The robots use this information to estimate each others positions and to control movement. To test the method experimentally, we used two real robots in an indoor environment. The result of the experiment revealed that the distance measurement and control error could be reduced to less than 3%.