658 research outputs found

    Bridge maintenance robotic arm: Capacitive sensor for obstacle ranging in particle laden air

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    This paper describes an Adaptive Capacitive Sensor Network for Obstacle Ranging (ACSOR) that is intended to provide entire arm encompassing obstacle range data for a robotic arm conducting the task of sandblasting a bridge. A multi-channel capacitive sensor capable of dynamic obstacle ranging in air heavily laden with lead contaminated sandblasting refuse has been developed. Experimental results have shown the ACSOR's working range to be 50cm, that it is relatively immune from airborne lead contaminated sandblasting refuse and that it is capable of ranging an obstacle 21cm away whilst fitted to a robotic arm moving at 2cm/s with an obstacle range error of less than 1cm

    Capacitive Object Ranging and Material Type Classifying Sensor

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    Capacitive sensor to detect fallen humans in conditions of low visibility

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    This paper examines the potential for a capacitive sensor to be used as part of a system to detect fallen humans at very close range. Previous research suggests that a robotic system incorporating a low cost capacitive sensor could potentially distinguish between different materials. The work reported in this paper stemmed from an attempt to determine the true extent to which such a system might reliably differentiate between fallen humans and other objects. The work is motivated by the fact that there are several different emergency circumstances in which such a system might save lives if it could reliably detect immobile humans. These scenarios include situations where older people have fallen and are unable to move or raise an alert, and circumstances where people have been overcome by smoke in a burning building. Current sensing systems are typically unsuitable in conditions of low visibility such as smoke filled rooms. This analysis focused specifically on the potential for a robot equipped with a capacitive sensing system to identify an immobile human in a low visibility emergency scenario. It is concluded that further work would be required to determine whether this type of capacitive sensing system is genuinely suitable for this task

    Path Planning of Industrial Manipulators for Dynamic Obstacles using a New Sensory System

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    Industrial manipulators perform repetitive and dangerous tasks. They are widely used, however present a source for accidental collisions with human operators. Therefore, they require large isolated spaces heavily taxing factory real-estate. Thus, there exists a need to create a safe cooperative working space shared by both manipulators and humans. The purpose of this research is to provide such an environment by integrating a safety mat-style sensory system, with an implementation of a potential field trajectory planning algorithm. The safety mat sensor has been designed and constructed in a cost effective means acting as a proof of concept for future industrial applications. Both the safety mat and potential field algorithm have been integrated with a CRS F3 manipulator for conducting validation experiments. We have found that our implementation of the potential field algorithm can successfully avoid single, and multiple obstacles detected by the mat. Moreover, collision avoidance is achieved for both static and dynamic obstacles. Finally, our implementation of the potential field algorithm is capable of preventing local minima entrapment of the manipulator, a problem affecting past implementations

    Plane-to-Plane Positioning by Proximity-based Control

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    International audienceIn this paper, we consider a multi-sensor arrangement of proximity sensors that forms a proximity array. A general modeling methodology is considered within the framework of Sensor-based Control. It incorporates multiple sensor signals from the proximity array by giving primary emphasis on the interaction screw. To prove its effectiveness, modeling approach is applied to the task of plane-to-plane positioning. We discuss the development of two sensor-based task functions for the specific task considered. The validity of the methodology is provided using relevant experimental results

    Lawn Buddy

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    Grass lawns are a common hallmark of the American home. In 2019, a survey from the National Association of Landscape Professionals found that 81% of all Americans owned a lawn [1]. Mowing is a time consuming and costly chore that is part of maintaining a grass lawn. The manual labor in mowing a lawn can exceed 40 hours per year [2]. People commonly incur weekly costs on mowing services to save time. Traditional gas powered mowers are physically demanding and use engines that need routine maintenance. They operate loudly enough that users should be wearing hearing protection and the noise commonly disturbs neighbors. Air pollution from gas emissions are also a concern for both the user and for the environment. An electric autonomous mower utilizes rechargeable batteries for power. Without a gas-engine, it is significantly quieter and requires little maintenance. No user is necessary to push the mower and there are no gas emissions. The mower returns to a docking station for charging its self-contained battery and leaves to mow as scheduled by the user. Collision avoidance and object detection are part of the mower’s completely autonomous navigation. These features allow the user to spend their time freely after the initial setup. Additional functionality such as blade mowing patterns and routine check-up reminders can be controlled through a wireless remote interface
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