In this paper, we study the decentralized coverage control problem for an environment using a group of autonomous mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated into the controller design. Furthermore, a collision avoidance component is added in the kinematic controller in order to guarantee a collision free coverage of the area. The convergence of the network to the optimal sensing configuration is proven with a Lyapunov-type analysis. Numerical simulations are provided approving the effectiveness of the proposed method through several experimental scenarios.

The wireless robotic sensor network (WRSN) is a combination of a mobile robot and wireless sensor networks. In WRSN, robots perform high-level missions such as human rescue, exploration in dangerous areas, and maintenance and repair of unmanned networks in cooperation with surrounding sensor nodes. In such a network, robots should move to the accident site as soon as possible. This paper proposes a distance-aware robot routing (DAR) algorithm, which focuses on how to pick the shortest path for the mobile robot by considering characteristics different from packet routing. Simulations are performed to demonstrate the benefits of using the proposed algorithm.

This paper presents an unsupervised learning-based method for selection of feature points and object category classification without previous setting of the number of categories. Our method consists of the following procedures: 1)detection of feature points and description of features using a Scale-Invariant Feature Transform (SIFT), 2)selection of target feature points using One Class-Support Vector Machines (OC-SVMs), 3)generation of visual words of all SIFT descriptors and histograms in each image of selected feature points using Self-Organizing Maps (SOMs), 4)formation of labels using Adaptive Resonance Theory-2 (ART-2), and 5)creation and classification of categories on a category map of Counter Propagation Networks (CPNs) for visualizing spatial relations between categories. Classification results of static images using a Caltech-256 object category dataset and dynamic images using time-series images obtained using a robot according to movements respectively demonstrate that our method can visualize spatial relations of categories while maintaining time-series characteristics. Moreover, we emphasize the effectiveness of our method for category classification of appearance changes of objects.

Detecting emergency situation is very important to a surveillance system for people like elderly live alone. A vision-based emergency response system with a paramedic mobile robot is presented in this paper. The proposed system is consisted of a vision-based emergency detection system and a mobile robot as a paramedic. A vision-based emergency detection system detects emergency by tracking people and detecting their actions from image sequences acquired by single surveillance camera. In order to recognize human actions, interest regions are segmented from the background using blob extraction method and tracked continuously using generic model. Then a MHI (Motion History Image) for a tracked person is constructed by silhouette information of region blobs and model actions. Emergency situation is finally detected by applying these information to neural network. When an emergency is detected, a mobile robot can help to diagnose the status of the person in the situation. To send the mobile robot to the proper position, we implement mobile robot navigation algorithm based on the distance between the person and a mobile robot. We validate our system by showing emergency detection rate and emergency response demonstration using the mobile robot.

Robotic systems operating in the real-world have to cope with unforeseen events by determining appropriate decisions based on noisy or partial knowledge. In this respect high functional robots are equipped with many sensors and actuators and run multiple processing modules in parallel. The resulting complexity is even further increased in case of cooperative multi-robot systems, since mechanisms for joint operation are needed. In this paper a complete and modular framework that handles this complexity in multi-robot systems is presented. It provides efficient exchange of generated data as well as a generic scheme for task execution and robot coordination.

This paper presents an autonomous navigation system for a mobile robot using randomly distributed passive RFID tags. In the case of randomly distributed RFID tags, it is difficult to provide the precise location of the robot especially in the area of sparse RFID tag distribution. This, combined with the wide turning radius of the robot, can cause the robot to enter a zigzag exploration path and miss the goal. In RFID-based navigation, the key is to reduce both the number of RFID tags and the localization error for practical use in a large space. To cope with these, we utilized the Read time, which measures the reading time of each RFID tag. With this, we could estimate accurately the localization and orientation without using any external sensors or increasing the RFID tags. The average estimation errors of 7.8 cm in localization and 11 degrees in orientation were achieved with 102 RFID tags in the area of 4.2 m by 6.2 m. Our proposed method is verified with the path trajectories produced during navigation compared with conventional approaches.

This paper presents a novel approach for robot localization using landmark maps. With recent progress in SLAM researches, it has become crucial for a robot to obtain and use large-size maps that are incrementally built by other mapper robots. Our localization approach successfully works with such incremental and large-size maps. In literature, RANSAC map-matching has been a promising approach for large-size maps. We extend the RANSAC map-matching so as to deal with incremental maps. We combine the incremental RANSAC with an incremental LSH database and develop a hybrid of the position-based and the appearance-based approaches. A series of experiments using radish dataset show promising results.

For a group of wirelessly networked robots, called "a robot swarm," to accomplish a unified task as a group, it is necessary to generate a set of common coordinates among all member robots and to notify each member robot of its heading direction in the generated common coordinates. However, when the member robots are not equipped with sensors to identify their locations or bearings, they can use only a ranging capability based in the wireless communication protocol being used to network them as a tool to generate a set of common coordinates among them. This paper presents the detailed principles of a method for generating a set of common coordinates/heading direction for a robot swarm with only ranging capability which we have proposed so far. After showing the theoretical Cramer-Rao lower-bound on the location estimation error variance, we demonstrate several computer simulation results for the proposed method with Received Signal Strength Indication (RSSI)-based ranging.

This paper describes an interactive experimental environment for autonomous soccer robots, which is a soccer field augmented by utilizing camera input and projector output. This environment, in a sense, plays an intermediate role between simulated environments and real environments. We can simulate some parts of real environments, e.g., real objects such as robots or a ball, and reflect simulated data into the real environments, e.g., to visualize the positions on the field, so as to create a situation that allows easy debugging of robot programs. The significant point compared with analogous work is that virtual objects are touchable in this system owing to projectors. We also show the portable version of our system that does not require ceiling cameras. As an application in the augmented environment, we address the learning of goalie strategies on real quadruped robots in penalty kicks. We make our robots utilize virtual balls in order to perform only quadruped locomotion in real environments, which is quite difficult to simulate accurately. Our robots autonomously learn and acquire more beneficial strategies without human intervention in our augmented environment than those in a fully simulated environment.

We have introduced a new ultrasonic-based localization method that requires only one ultrasonic receiver to locate transmitters. In our previous reports [1],[2], we conducted several fundamental experiments, and proved the feasibility and accuracy of our system. However the performance in a more realistic environment has not yet been evaluated. In this paper, we have extended our localization system into a robot tracking system, and conducted experiments where the system tracked a moving robot. Localization was executed both by our proposed method and by the conventional TOA method. The experiment was repeated with different density of receivers. Thus we were able to compare the accuracy and the scalability between our proposed method and the conventional method. As a result 90-percentile of the position error was from 6.2 cm to 14.6 cm for the proposed method, from 4.0 cm to 6.1 cm for the conventional method. However our proposed method succeeded in calculating the position of the transmitter in 95% out of total attempts of localization with sparse receivers (4 receivers in about 5 m 5 m area), whereas the success rate was only 31% for the conventional method. From the result we concluded that although the proposed method is less accurate it can cover a wider area with sparse receivers than the conventional method. In addition to the dynamic tracking experiments, we also conducted some localization experiments where the robot stood still. This was because we wanted to investigate the reason why the localization accuracy degraded in the dynamic tracking. According to the result, the degradation of accuracy might be due to the systematic error in localization which is dependent on the geometric relationship between the transmitter and the receiver.

This paper presents a motion planning method for a bimanual robot for executing assembly tasks. The method employs an adaptive modeling which can automatically generate an assembly model and modify the model during actual assembly. Bimanual robotic assembly is modeled at the task-level using contact states of workpieces and their transitions. The lower-level velocity commands of the workpieces are automatically derived by solving optimization problem formulated with assembly constraints, position of the workpieces, and kinematics of manipulators. Motion requirements of the workpieces are transformed to motion commands of the bimanual robot. The proposed approach is evaluated with experiments on peg-in-hole assembly with an L-shaped peg.

This paper presents 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.

Service robots need to be able to recognize and identify objects located within complex backgrounds. Since no single method may work in every situation, several methods need to be combined and robots have to select the appropriate one automatically. In this paper we propose a scheme to classify situations depending on the characteristics of the object of interest and user demand. We classify situations into four groups and employ different techniques for each. We use Scale-invariant feature transform (SIFT), Kernel Principal Components Analysis (KPCA) in conjunction with Support Vector Machine (SVM) using intensity, color, and Gabor features for five object categories. We show that the use of appropriate features is important for the use of KPCA and SVM based techniques on different kinds of objects. Through experiments we show that by using our categorization scheme a service robot can select an appropriate feature and method, and considerably improve its recognition performance. Yet, recognition is not perfect. Thus, we propose to combine the autonomous method with an interactive method that allows the robot to recognize the user request for a specific object and class when the robot fails to recognize the object. We also propose an interactive way to update the object model that is used to recognize an object upon failure in conjunction with the user's feedback.

A new type of humanoid robot arm which can coexist and be interactive with human beings are looked for. For the purpose of implementation of human smooth and fast movement to a pneumatic robot, the author used a humanoid robot arm with pneumatic agonist-antagonist actuators as endoskeletons which has control mechanism in the stiffness of each joint, and the controllability was experimentally discussed. Using Kitamori 's method to experimentally decide the control gains and using I-PD controller, three joints of the humanoid robot arm were experimentally controlled. The damping control algorithm was also adopted to the wrist joint, to modify the speed in accordance with the power. The results showed that the controllability to step-wise input was less than one degree in error to follow the target angles, and the time constant was less than one second. The simultaneous input of command to three joints was brought about the overshoot of about ten percent increase in error. The humanoid robot arm can generate the calligraphic motions, moving quickly at some times but slowly at other times, or particularly softly on some occasions but stiffly on other occasions at high accuracy.

We propose a new type of direction-of-arrival estimation method for robot audition that is free from strict head related transfer function estimation. The proposed method is based on statistical pattern recognition that employs a ratio of power spectrum amplitudes occurring for a microphone pair as a feature vector. It does not require any phase information explicitly, which is frequently used in conventional techniques, because the phase information is unreliable for the case in which strong reflections and diffractions occur around the microphones. The feature vectors we adopted can treat these influences naturally. The effectiveness of the proposed method was shown from direction-of-arrival estimation tests for 19 kinds of directions: 92.4% of errors were reduced compared with the conventional phase-based method.

Recently there has been a great deal of research on using mobility in sensor networks to assist their sensing tasks. In this paper, we propose a policy-based session control protocol for Multi-Robot Sensor Networks (MRSNs) called Billiards. In a MRSN, all messages are transported by the physical motion of participants (mobile nodes) in the network. When a large volume of data or continuous data is required to be transferred, there exists a problem determining how the data is fragmented and how the mobile nodes are formed for carrying the data to the destination. To overcome the issues, we propose a suitable method of session control which is determined based on a state of surrounding mobile nodes such as number, maximum-velocity and buffer-size. Billiards also takes a system policy of delay minimization into consideration. In this paper, we describe the protocol and model of Billiards and analyze the model. We evaluated the performance of Billiards utilizing mobile robots which are equipped with MICA2 mote and comparing with non optimized method. The experimental results demonstrate that Billiards achieves less delay than non optimized method at every velocity and buffer-size of each robot.

This work is concerned with the problem of robot localization using standard RFID tags as landmarks and an RFID reader as a landmark sensor. A main advantage of such an RFID-based localization system is the availability of landmark ID measurement, which trivially solves the data association problem. While the main drawback of an RFID system is its low spatial accuracy. The result in this paper is an improvement of the localization accuracy for a standard short-range RFID sensor. One of the main contributions is a proposal of a machine learning approach in which multiple classifiers are trained to distinguish RFID-signal features of each location. Another contribution is a design tool for tag arrangement by which the tag configuration needs not be manually designed by the user, but can be automatically recommended by the system. The effectiveness of the proposed technique is evaluated experimentally with a real mobile robot and an RFID system.

A new type of sound source segregation method using robot-mounted microphones, which are free from strict head related transfer function (HRTF) estimation, has been proposed and successfully applied to three simultaneous speech recognition systems. The proposed segregation method is executed with sound intensity differences that are due to the particular arrangement of the four directivity microphones and the existence of a robot head acting as a sound barrier. The proposed method consists of three-layered signal processing: two-line SAFIA (binary masking based on the narrow band sound intensity comparison), two-line spectral subtraction and their integration. We performed 20 K vocabulary continuous speech recognition test in the presence of three speakers' simultaneous talk, and achieved more than 70% word error reduction compared with the case without any segregation processing.

Pneumatic pressure, which is easy enough to be handled in comparison with hydraulic pressure and is endowed with high safety, is available for a power source of a robot arm to be utilized in concert with human beings to do various types of work. But pneumatic pressure is so low in comparison with hydraulic pressure that an air cylinder having a diameter long enough and stroke wide enough is required to obtain great output power. In this study, therefore, the investigation was made with layout of air cylinders and transmission mechanisms of the motion power directed toward the driving joints to be followed by development of a new humanoid robot arm with seven degrees of freedom in which air cylinders are compactly incorporated. To be concrete with this, contrivance was made with an endoskeleton structure allowing almost all of the structure materials of the individual arm joints to be shared by the air cylinder with incorporation of the air cylinder in the axes of the upper arm joint and forearm joints by paying attention to the fact that the cylinder itself has high strength. The evaluation experiments driving the robot arm referred to above were conducted by means of I-PD control. The results suggested that the mechanism of the robot with seven degrees of freedom having pneumatic actuators proposed in this study is useful as the humanoid robot arm. The quick and accurate motions were accomplished with I-PD control which is relatively easy to be dealt with but not suitable for non-linear actuator system.

Designable task allocation systems which consist of identical agents using stochastic automata are suggested. From the viewpoint of the group response and the individual behavior, the performances of a simple model and an improved one are compared numerically. Robots experiments are performed to compare between the two models.