Improving Safety in Collaborative Robot Tasks

Mandal, Avijit; Sharma, Divyasheel; Sukhwani, Mohak; Jetley, Raoul; Sarkar, Santonu

doi:10.1109/indin41052.2019.8972103

Cited by 3 publications

(4 citation statements)

References 12 publications

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“…Supervised learning in vision AI for identification and sorting of objects [19], [20] and vision-based pose estimation [21] are example of use of AI for robot perception. Unsupervised Learning approaches have been used in industrial settings to augment safe human robot collaborations [2], [3]. End-to-end systems that learn robotic grasping [22], [23], [24] can assist robots to pick up the parts that it has never seen before.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots

Sukhwani,

Duggal,

Zahrai

2021

Preprint

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In this paper, we present a model for semantic memory that allows machines to collect information and experiences to become more proficient with time. After a semantic analysis of the data, information is stored in a knowledge graph which is used to comprehend instructions, expressed in natural language, and execute the required tasks in a deterministic manner. This imparts industrial robots cognitive behavior and an intuitive user interface, which is most appreciated in an era, when collaborative robots are to work alongside humans. The paper outlines the architecture of the system together with a practical implementation of the proposal.

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Section: Related Workmentioning

confidence: 99%

“…AI based techniques for modeling, forecasting and predicting machine behavior play crucial role in present day Industrial setups. With advancements in industrial connectivity and availability of rich sensory data, AI based techniques can achieve noticeable improvements in both efficiency and flexibility [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots

Sukhwani,

Duggal,

Zahrai

2021

Preprint

Self Cite

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“…Collaborative robots are designed to augment humans' abilities and act as an extension in work in complex industrial environments. To enhance the interactions, collaborative robotic architectures attempt to recreate human cognition and intend to reach a higher degree of autonomy by hierarchic control system design, environment sensing, and information fusion [29], [35].…”

Section: Related Workmentioning

confidence: 99%

Episodic Memory Model for Learning Robotic Manipulation Tasks

Behbahani,

Chhatpar,

Zahrai

et al. 2021

Preprint

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Machine learning, artificial intelligence and especially deep learning based approaches are often used to simplify or eliminate the burden of programming industrial robots. Using these approaches robots inherently learn a skill instead of being programmed using strict and tedious programming instructions. While deep learning is effective in making robots learn skills, it does not offer a practical route for teaching a complete task, such as assembly or machine tending, where a complex logic must be understood and related sub-tasks need to be performed. We present a model similar to an episodic memory that allows robots to comprehend sequences of actions using single demonstration and perform them properly and accurately. The algorithm identifies and recognizes the changes in the states of the system and memorizes how to execute the necessary tasks in order to make those changes. This allows the robot to decompose the tasks into smaller sub-tasks, retain the essential steps, and remember how they have been performed.

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“…For instance, elderly individuals and patients feel it convenient to command against facilities in smart homes [2]. Collaborative robots might work safer with human workers in factories [3,4]. Medical facilities in hospitals would become more intelligent in assisting medical doctors to cure patients [5,6].…”

Section: Introductionmentioning

confidence: 99%

3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display

Yeamkuan

Chamnongthai

2021

Sensors

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This paper proposes a three-dimensional (3D) point-of-intention (POI) determination method using multimodal fusion between hand pointing and eye gaze for a 3D virtual display. In the method, the finger joint forms of the pointing hand sensed by a Leap Motion sensor are first detected as pointing intention candidates. Subsequently, differences with neighboring frames, which should be during hand pointing period, are checked by AND logic with the hand-pointing intention candidates. A crossing point between the eye gaze and hand pointing lines is finally decided by the closest distance concept. In order to evaluate the performance of the proposed method, experiments with ten participants, in which they looked at and pointed at nine test points for approximately five second each, were performed. The experimental results show the proposed method measures 3D POIs at 75 cm, 85 cm, and 95 cm with average distance errors of 4.67%, 5.38%, and 5.71%, respectively.

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Improving Safety in Collaborative Robot Tasks

Cited by 3 publications

References 12 publications

Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots

Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots

Episodic Memory Model for Learning Robotic Manipulation Tasks

3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display

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