Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

Appearance-based localization for mobile robots using digital zoom and visual compass

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally

Improving situation awareness of a single human operator interacting with multiple unmanned vehicles: first results

In the context of the supervision of one or several unmanned vehicles by a human operator, the design of an adapted user interface is a major challenge. Therefore, in the context of an existing experimental set up composed of a ground station and heterogeneous unmanned ground and air vehicles we aim at redesigning the human-robot interactions to improve the operator's situation awareness. We base our new design on a classical user centered approach

Categorization of indoor places by combining local binary pattern histograms of range and reflectance data from laser range finders

This paper presents an approach to categorize typical places in indoor environments using 3D scans provided by a laser range finder. Examples of such places are offices, laboratories, or kitchens. In our method, we combine the range and reflectance data from the laser scan for the final categorization of places. Range and reflectance images are transformed into histograms of local binary patterns and combined into a single feature vector. This vector is later classified using support vector machines. The results of the presented experiments demonstrate the capability of our technique to categorize indoor places with high accuracy. We also show that the combination of range and reflectance information improves the final categorization results in comparison with a single modality

Asynchronous displays for multi-UV search tasks

Synchronous video has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable as in the case of interplanetary robotics. We identify two basic problems for controlling multiple robots using synchronous displays: operator overload and information fusion. Synchronous displays from multiple robots can easily overwhelm an operator who must search video for targets. If targets are plentiful, the operator will likely miss targets that enter and leave unattended views while dealing with others that were noticed. The related fusion problem arises because robots' multiple fields of view may overlap forcing the operator to reconcile different views from different perspectives and form an awareness of the environment by "piecing them together". We have conducted a series of experiments investigating the suitability of asynchronous displays for multi-UV search. Our first experiments involved static panoramas in which operators selected locations at which robots halted and panned their camera to capture a record of what could be seen from that location. A subsequent experiment investigated the hypothesis that the relative performance of the panoramic display would improve as the number of robots was increased causing greater overload and fusion problems. In a subsequent Image Queue system we used automated path planning and also automated the selection of imagery for presentation by choosing a greedy selection of non-overlapping views. A fourth set of experiments used the SUAVE display, an asynchronous variant of the picture-in-picture technique for video from multiple UAVs. The panoramic displays which addressed only the overload problem led to performance similar to synchronous video while the Image Queue and SUAVE displays which addressed fusion as well led to improved performance on a number of measures. In this paper we will review our experiences in designing and testing asynchronous displays and discuss challenges to their use including tracking dynamic targets. © 2012 by the American Institute of Aeronautics and Astronautics, Inc

Face tracking using a hyperbolic catadioptric omnidirectional system

In the first part of this paper, we present a brief review on catadioptric omnidirectional systems. The special case of the hyperbolic omnidirectional system is analysed in depth. The literature shows that a hyperboloidal mirror has two clear advantages over alternative geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the image resolution is uniformly distributed along the mirror’s radius [2]. In the second part of this paper we show empirical results for the detection and tracking of faces from the omnidirectional images using Viola-Jones method. Both panoramic and perspective projections, extracted from the omnidirectional image, were used for that purpose. The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection of the image. In order to avoid losing or duplicating detections, the panoramic projection was extended horizontally. Duplications were eliminated based on the ROIs established by previous detections. After a confirmed detection, faces were tracked from perspective projections (which are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt of each virtual camera was determined by the ROIs previously computed on the panoramic image. The results show that, when using a careful combination of the two projections, good frame rates can be achieved in the task of tracking faces reliably

Semantic labeling of places using information extracted from laser and vision sensor data

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating the interaction withhumans. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a map in a more intuitive way. In this work, we firrst propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. Our method uses AdaBoost to boost simple features extracted from range data and vision into a strong classifier. We present two main applications of this approach. Firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden Markov model. Secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation procedure. We finally show how to apply associative Markov networks (AMNs) together with AdaBoost for classifying complete geometric maps. Experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments

Semantic labeling of places

Indoor environments can typically be divided into places with different functionalities like corridors, kitchens, offices, or seminar rooms. We believe that such semantic information enables a mobile robot to more efficiently accomplish a variety of tasks such as human-robot interaction, path-planning, or localization. In this paper, we propose an approach to classify places in indoor environments into different categories. Our approach uses AdaBoost to boost simple features extracted from vision and laser range data. Furthermore,we apply a Hidden Markov Model to take spatial dependencies between robot poses into account and to increase the robustness of the classification. Our technique has been implemented and tested on real robots as well as in simulation. Experiments presented in this paper demonstrate that our approach can be utilized to robustly classify places into semantic categories