In a multirobot system, using shared symbols for objects in the environment is a prerequisite for collaboration. Sharing symbols requires that each agent has anchored a symbol with an internal, sensor level representation, as well as that these symbols match between the agents. The problem can be solved easily when the internal representations can be communicated between the agents. However, with heterogeneous embodiments the available sensors are likely to differ, making it impossible to share the internal representations directly. We propose the use of pointing gestures to align symbols between a heterogeneous group of robots. We describe a planning framework that minimizes the required effort for anchoring representations across robots. The framework allows planning for both the gesturing and observing agents in a decentralized fashion. It considers both implicit sources of failure, such as ambiguous pointing, as well as costs required by actions. Simulation experiments demonstrate that the resulting planning problem has a complex solution structure with multiple local minima. Demonstration with a heterogeneous two-robot system shows the practical viability of this approach.

This paper addresses the idea of establishing symbolic communication between mobile robots through gesturing. Humans communicate using body language and gestures in addition to other linguistic modalities like prosody and text or dialog structure. This research aims to develop a pointing gesture detection system for robot to robot communication scenarios to grant robots an ability to convey object identity information without global localization of the agents. The detection is based on RGB-D and a NAO humanoid robot is used as the pointing agent in the experiments. The presented algorithms are based on PCL library. The results indicate that real-time detection of pointing gesture can be performed with little information about the embodiment of the pointing agent and that an observing agent can use the gesture detection to perform actions on the pointed targets.

This paper presents a physical human-robot interaction (pHRI) interface, which enables the user to control a walking humanoid robot through physical contact. A human operator guides the robot in parent-child-like behavior by exerting force onto the hand of the robot. In contrast to a conventional approach of pHRI in which force/torque measurements are applied, the proposed solution is based on sensorless force control. Furthermore, we demonstrate an extension to the generic interface by implementing a number of gait control algorithms. This paper also evaluates user-acceptable robot responses while guiding a humanoid robot walking. User opinion on the presented control solution is evaluated through usability testing conducted among prospective users. The obtained results indicate that the developed interfaces present an effective solution to the problem of sensorless guidance of a walking robot.

In this paper we present a novel methodology for detection and tracking of facial features like eyes, nose and mouth in image sequences. The proposed methodology is intended to support natural interaction with autonomously navigating robots that guide visitors in museums and exhibition centers and, more specifically, to provide input for the analysis of facial expressions that humans utilize while engaged in various conversational states. For face and facial feature region detection and tracking, we propose a methodology that combines appearance-based and feature-based methods for recognition and tracking, respectively. For the stage of face tracking the introduced method is based on least squares matching (LSM), a matching technique able to model effectively radiometric and geometric differences between image patches in different images. Thus, compared with previous research, the LSM approach can overcome the problems of variable scene illumination and head in-plane rotation. Another significant characteristic of the proposed approach is that tracking is performed on the image plane only wherever laser range information suggests so. The increased computational efficiency meets the real time demands of human-robot interaction applications and hence facilitates the development of relevant systems.

This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and avoid moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility to detect and track different-shaped moving objects. We propose a novel and adaptive technique where potential moving objects are classified and learned in real-time using a fuzzy ART neural network algorithm. Experimental results indicate that our method can effectively distinguish and track moving targets in cluttered indoor environments, while at the same time learning their shape.

A major issue in the field of mobile robotics today is the detection and tracking of moving objects (DATMO) from a moving observer. In dynamic and highly populated environments, this problem presents a complex and computationally demanding task. It can be divided in sub-problems such as robotpsilas relative motion compensation, feature extraction, measurement clustering, data association and targetspsila state vector estimation. In this paper we present an innovative approach that addresses all these issues exploiting various probabilistic and deterministic techniques. The algorithm utilizes real laser-scanner data to dynamically extract moving objects from their background environment, using a time-fading grid map method, and tracks the identified targets employing a joint probabilistic data association with interacting multiple model (JPDA-IMM) algorithm. The resulting technique presents a computationally efficient approach to already existing target-tracking research for real time application scenarios.

Many localization techniques today rely on absolute landmark measurements to efficiently track the robot's position in space. Although absolute landmarks are essential for correctly estimating its position, they might be rare in an unknown environment. This means that the robot would have to traverse long distances without any outside reference point resulting in system degradation. In case of a robot team though, each robot can rely on both absolute or relative position measurements between robots. This paper describes an approach for a multi-robot localization system, based on a single centralized extended Kalman filter (CEKF) to track the position and orientation changes of a group of robots. Moreover, it is shown that if the robots in the group collect only relative measurements the system suffers from observability problem. It is proven that an increasing number of mobile robots capable of relative measurements only, reduces the observability problem and compensates for the need of external absolute landmarks thus providing efficient localization.