Influence of Human Limb Motion Speed in a Collaborative Hand-over Task

Melchiorre, Matteo; Scimmi, Leonardo Sabatino; Mauro, Stefano; Pastorelli, Stefano Paolo

doi:10.5220/0006864703490356

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…This means that the robot should give and receive the objects in a similar way as humans do. The human-like motion will result in a more comfortable collaboration with robots [82,83], through the robot speed and reaction adaptability [84,85]. Moreover, non-verbal communication could be used through physical properties for a human intention understanding [86].…”

Section: Object Handovermentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

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Section: Object Handovermentioning

confidence: 99%

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Castro

Silva

Santos

2021

Sensors

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“…This capability plays an important role in motion planning, making possible to avoid collision between the robot links and obstacles while driving the end-effector, with a defined orientation, along a certain path. In this case, the local control strategies and collision avoidance algorithms of previous works can be used [28][29][30][31]. They allow to dynamically define the robot trajectory with joint velocity sets, resulting from attractive-repulsive potential fields.…”

Section: Robotic Armmentioning

confidence: 99%

Design of a UGV Powered by Solar Energy for Precision Agriculture

et al. 2020

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In this paper, a novel UGV (unmanned ground vehicle) for precision agriculture, named “Agri.q,” is presented. The Agri.q has a multiple degrees of freedom positioning mechanism and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. In particular, the integration of a 7 DOFs (degrees of freedom) manipulator and a mobile frame results in a reconfigurable workspace, which opens to samples collection and inspection in non-structured environments. Moreover, Agri.q mounts an orientable landing platform for drones which is made of solar panels, enabling multi-robot strategies and solar power storage, with a view to sustainable energy. In fact, the device will assume a central role in a more complex automated system for agriculture, that includes the use of UAV (unmanned aerial vehicle) and UGV for coordinated field monitoring and servicing. The electronics of the device is also discussed, since Agri.q should be ready to send-receive data to move autonomously or to be remotely controlled by means of dedicated processing units and transmitter-receiver modules. This paper collects all these elements and shows the advances of the previous works, describing the design process of the mechatronic system and showing the realization phase, whose outcome is the physical prototype.

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“…Moreover, a fixed link length approach is defined to reduce the noises of the skeleton joints. The proposed methods have been developed to increase the performance of collision avoidance and hand over algorithms described in [13][14][15]. They have the potentiality to be applied using different models and extended to wider human body regions.…”

Section: Introductionmentioning

confidence: 99%

Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics

Palmieri

Melchiorre

Scimmi

et al. 2020

Mechanisms and Machine Science

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The rising interest in collaborative robotics leads to research solutions in order to increase robot interaction with the environment. The development of methods that permit robots to recognize and track human motion is relevant for safety and collaboration matters. A large quantity of data can be measured in real time by Microsoft Kinect®, a well-known low-cost depth sensor, able to recognize human presence and to provide postural information by extrapolating a skeleton. However, the Kinect sensor tracks motion with relatively low accuracy and jerky behavior. For this reason, the effective use in industrial applications in which the measurement of arm velocity is required can be unsuitable. The present work proposes a filtering method that allows the measurement of more accurate velocity values of human arm, based on row data provided by the Kinect sensor. The estimation of arm motion is achieved by a Kalman filter based on a kinematic model and by the imposition of fixed lengths for the skeleton links detected by the sensor. The development of the method is supported by experimental tests. The achieved results suggest the practical applicability of the developed algorithms.

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Influence of Human Limb Motion Speed in a Collaborative Hand-over Task

Cited by 8 publications

References 17 publications

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Design of a UGV Powered by Solar Energy for Precision Agriculture

Human Arm Motion Tracking by Kinect Sensor Using Kalman Filter for Collaborative Robotics

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