Enhanced situational awareness in autonomous mobile robots using context-based mapping (October 2012)

Smith, Charles V.; Doran, Michael; Daigle, Roy J.; Thomas, Thomas G.

doi:10.1109/cogsima.2013.6523836

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…For example, while Edge gateways process the on-board sensor information from the robots, external monitoring cameras and other sensors can be used to increase the accuracy of localization algorithms or assure collision avoidance in cases of sensor failure or other local problems that might appear in the robot and go undetected by the Edge gateway. This results in an enhanced situational awareness [22] where both externally and internally collected information about a robot is available in the Fog layer. By introducing additional services in the Fog layer, we can enhance overall system robustness and its fault tolerance.…”

Section: Fog Layermentioning

confidence: 99%

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Sarker

Queralta

Gia

et al. 2019

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

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Indoor mobile robots are widely used in industrial environments such as large logistic warehouses. They are often in charge of collecting or sorting products. For such robots, computation-intensive operations account for a significant percentage of the total energy consumption and consequently affect battery life. Besides, in order to keep both the power consumption and hardware complexity low, simple micro-controllers or single-board computers are used as onboard local control units. This limits the computational capabilities of robots and consequently their performance. Offloading heavy computation to Cloud servers has been a widely used approach to solve this problem for cases where large amounts of sensor data such as real-time video feeds need to be analyzed. More recently, Fog and Edge computing are being leveraged for offloading tasks such as image processing and complex navigation algorithms involving non-linear mathematical operations. In this paper, we present a system architecture for offloading computationally expensive localization and mapping tasks to smart Edge gateways which use Fog services. We show how Edge computing brings computational capabilities of the Cloud to the robot environment without compromising operational reliability due to connection issues. Furthermore, we analyze the power consumption of a prototype robot vehicle in different modes and show how battery life can be significantly improved by moving the processing of data to the Edge layer.

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Section: Fog Layermentioning

confidence: 99%

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Sarker

Queralta

Gia

et al. 2019

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

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“…We do not require such an algorithm to determine context, as at our current stage, we only focused on impacting the navigational behavior based on the context, and so the modifications in behavior are a result of the navigational algorithms. In [12], we see a tour guide robot that develops a context-based map in order to improve the tours provided. Our system does not perform SLAM, and again, it relies on the context to be determined based on the fusion algorithms.…”

Section: Previous and Relatedmentioning

confidence: 99%

Contextual Awareness in a WSN/RFID Fusion Navigation System

2014

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We present insight into how contextual awareness can be derived from, and improve, a fusion algorithm combing a WSN and a passive RFID for autonomous mobile robot navigation. Contextual awareness of not where the robot is, but rather the context in which it exists in relation to the environment and human user serves to improve accuracy in navigation, alters the speed of the robot, and modifies its behavior. The WSN system, using a virtual potential field, provides fast general navigation in open areas and the RFID provides precision navigation near static obstacles and in narrow areas. We verified the effectiveness of our approaches through navigational and guidance experiments.

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“…However, for autonomous operations in human workspaces, the robot itself is responsible for the safety of its actions. Situation awareness capabilities are required to make it able to effectively operate in unstructured environments without assistance [6], [7], [8]. In this study, we focus on these on-board situation awareness capabilities for service robots.…”

Section: Introductionmentioning

confidence: 99%

FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection

İnceoğlu

Aksoy

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of physical robotobject interaction may prevent success in object manipulation. Therefore, execution of a manipulation action may result in an undesirable outcome involving accidents or damages to the objects or environment. Situation awareness becomes important in such cases to enable the robot to (i) maintain the integrity of both itself and the environment, (ii) recover from failed tasks in the short term, and (iii) learn to avoid failures in the long term. For this purpose, robot executions should be continuously monitored, and failures should be detected and classified appropriately. In this work, we focus on detecting and classifying both manipulation and post-manipulation phase failures using the same exteroception setup. We cover a diverse set of failure types for primary tabletop manipulation actions. In order to detect these failures, we propose FINO-Net [1], a deep multimodal sensor fusion based classifier network. Proposed network accurately detects and classifies failures from raw sensory data without any prior knowledge. In this work, we use our extended FAILURE dataset [1] with 99 new multimodal manipulation recordings and annotate them with their corresponding failure types. FINO-Net achieves 0.87 failure detection and 0.80 failure classification F1 scores. Experimental results show that proposed architecture is also appropriate for real-time use.

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Enhanced situational awareness in autonomous mobile robots using context-based mapping (October 2012)

Cited by 4 publications

References 5 publications

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Offloading SLAM for Indoor Mobile Robots with Edge-Fog-Cloud Computing

Contextual Awareness in a WSN/RFID Fusion Navigation System

FINO-Net: A Deep Multimodal Sensor Fusion Framework for Manipulation Failure Detection

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