A Multi Agent Distributed Sensing Architecture with Application to Planetary Cliff Exploration

Sujan, Vivek A.; Dubowsky, Steven; Huntsberger, Terrance L.; Aghazarian, Hrand; Cheng, Yang; Schenker, Paul S.

doi:10.1007/11008941_26

Cited by 3 publications

(7 citation statements)

References 23 publications

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“…First, a low pass filter is applied to the image to remove the higher frequency effects. Second, an adaptive decimation process Sujan et al, 2003;Sujan and Meggiolaro, 2005) smoothes out patterns that exceed neighboring pattern variation. This is achieved by measuring the rate of change of pattern variance across the image.…”

Section: Figmentioning

confidence: 99%

Information based indoor environment robotic exploration and modeling using 2-D images and graphs

2006

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As the autonomy of personal service robotic systems increases so has their need to interact with their environment. The most basic interaction a robotic agent may have with its environment is to sense and navigate through it. For many applications it is not usually practical to provide robots in advance with valid geometric models of their environment. The robot will need to create these models by moving around and sensing the environment, while minimizing the complexity of the required sensing hardware. Here, an information-based iterative algorithm is proposed to plan the robot's visual exploration strategy, enabling it to most efficiently build a graph model of its environment. The algorithm is based on determining the information present in sub-regions of a 2-D panoramic image of the environment from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The set of nodes and the images taken at each node are combined into a graph to model the environment. By tracing its path from node to node, a service robot can navigate around its environment. This method is particularly well suited for flat-floored environments. Experimental results show the effectiveness of this algorithm.

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Section: Figmentioning

confidence: 99%

Information based indoor environment robotic exploration and modeling using 2-D images and graphs

2006

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“…First, a low pass filter is applied to the image to remove the higher frequency effects. Second, an adaptive decimation process, described in Sujan et al (2003) and Sujan et al (2004), smoothes out patterns that exceed neighboring pattern variance. This is achieved by measuring the rate of change of pattern variance across the image.…”

Section: Child Node Identificationmentioning

confidence: 99%

“…The proposed algorithm, named here IB-SLAM (Information-Based Simultaneous Localization and Mapping), is unique in that it utilizes the quantity of information (entropy) of the environment in order to predict high information yielding viewpoints from which to continue exploring the environment. This has been shown to result in a significantly more efficient and robust exploration process as compared to other conventional approaches (Sujan et al 2003, Sujan et al 2004). …”

Section: Introductionmentioning

confidence: 99%

A new technique in mobile robot simultaneous localization and mapping

Sujan

Meggiolaro

Belo

2006

Sba Controle & Automação

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In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite. KEYWORDS:Service robots, visual mapping, selflocalization, information theory, Mellin transform. RESUMOUsualmente não é possível fornecer a priori a robôs móveis autônomos um mapa detalhado de seu ambiente de trabalho. Nestes casos, o robô precisa criar um modelo geométrico preciso movendo-se pelo ambiente e utilizando seus sensores. Neste trabalho, um algoritmo iterativo é proposto para planejar a estratégia de exploração de robôs móveis autôno-mos, permitindo-os construir de forma eficiente um modelo do ambiente em forma de grafo sem a necessidade de sensores de alto custo. Neste algoritmo, o conteúdo de informação presente em sub-regiões de uma imagem panorâmica 2-D do ambiente é determinada a partir da posição atual do robô usando uma única câmera fixada em sua estrutura. Usando uma métrica baseada na teoria da informação de Shannon, o algoritmo determina, a partir da imagem 2-D, localizações potenciais para novos nós do grafo, a partir dos quais serão tomadas novas imagens panorâmicas para prosseguir com a exploração. Uma transformada de Mellin é usada para guiar o robô de volta a um nó previamente explorado. Este processo continua até que todo o ambiente tenha sido explorado em um nível de detalhes pré-especificado. A eficácia do algoritmo é verificada experimentalmente através da exploração de um ambiente interno por um agente robótico móvel dispondo apenas de um conjunto limitado de sensores. PALAVRAS-CHAVE:Robôs móveis, mapeamento visual, auto-localização, teoria da informação, transformada de Mellin.

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“…Step 4 ing agent the algorithm consists of four steps, described as follows (see Figure 3; Sujan et al 2003). …”

Section: Overviewmentioning

confidence: 99%

“…Absolute localization is used in this application, although relative localization has been applied in the past (Howard, Matarić, and Sukhatme 2003;Sujan et al 2003). Searching for the target (which forms the absolute origin) by moving the robot sensors can be carried out in many ways (Luo and Kay 1989;Tarabanis, Allen, and Tsai 1995).…”

Section: System Initializationmentioning

confidence: 99%

Efficient Information-based Visual Robotic Mapping in Unstructured Environments

Sujan

Dubowsky

2005

The International Journal of Robotics Research

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In field environments it is often not possible to provide robot teams with detailed a priori environment and task models. In such unstructured environments, robots will need to create a dimensionally accurate three-dimensional geometric model of its surroundings by performing appropriate sensor actions. However, uncertainties in robot locations and sensing limitations/occlusions make this difficult. A new algorithm, based on iterative sensor planning and sensor redundancy, is proposed to build a geometrically consistent dimensional map of the environment for mobile robots that have articulated sensors. The aim is to acquire new information that leads to more detailed and complete knowledge of the environment. The robot(s) is controlled to maximize geometric knowledge gained of its environment using an evaluation function based on Shannon's information theory. Using the measured and Markovian predictions of the unknown environment, an information theory based metric is maximized to determine a robotic agent's next best view (NBV) of the environment. Data collected at this NBV pose are fused using a Kalman filter statistical uncertainty model to the measured environment map. The process continues until the environment mapping process is complete. The work is unique in the application of information theory to enhance the performance of environment sensing robot agents. It may be used by multiple distributed and decentralized sensing agents for efficient and accurate cooperative environment modeling. The algorithm makes no assumptions of the environment structure. Hence, it is robust to robot failure since the environment model being built is not dependent on any single agent frame, but is set in an absolute reference frame. It accounts for sensing uncertainty, robot motion uncertainty, environment model uncertainty and other critical parameters. It allows for regions of higher interest receiving greater attention by the agents. This algorithm is particularly well suited to unstructured environments, where sensor uncertainty and occlusions are significant. Simulations and experiments show the effectiveness of this algorithm.

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A Multi Agent Distributed Sensing Architecture with Application to Planetary Cliff Exploration

Cited by 3 publications

References 23 publications

Information based indoor environment robotic exploration and modeling using 2-D images and graphs

Information based indoor environment robotic exploration and modeling using 2-D images and graphs

A new technique in mobile robot simultaneous localization and mapping

Efficient Information-based Visual Robotic Mapping in Unstructured Environments

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