Development of typical collision reactions in combination with algorithms for external impacts identification

Mikhel, Stanislav; Popov, Dmitry; Mamedov, Shamil; Klimchik, Alexandr

doi:10.1016/j.ifacol.2019.11.177

Cited by 3 publications

(5 citation statements)

References 10 publications

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“…The interval used for classification is represented with the blue shaded area between the envelope bounds. If condition (17) was not satisfied, the difference is shaded in red.…”

Section: Time-window Impact Classificationmentioning

confidence: 99%

“…In this figure, the area enclosed by the envelope shaded in blue is used for classification. If this envelope encompasses 0, as described in (17), the impact is classified as expected. If not, the impact is classified as unexpected.…”

Section: Time-window Impact Classificationmentioning

confidence: 99%

“…However, for the classification and reaction phases, state-of-the-art approaches focus on pHRI [2], [8], [13], [14], resulting in robot-environment impacts being classified as unintentional or as intentional to stop the robot from, e.g., initiating a hand-guiding teaching phase. Furthermore, current classification methods rely on machine learning to discriminate among different types of collisions [8], [9], [15], [16], [17], where the pHRI intention is captured implicitly. For the emerging field of impact-aware robotics, the focus must instead be placed on comparing the planned postimpact behavior with the observed post-impact behavior of the robot-object-environment interactions.…”

Section: Introductionmentioning

confidence: 99%

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Aim-Aware Collision Monitoring: Discriminating Between Expected and Unexpected Post-Impact Behaviors

Proper,

Kurdas,

Abdolshah

et al. 2023

IEEE Robot. Autom. Lett.

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“…The interval used for classification is represented with the blue shaded area between the envelope bounds. If condition (17) was not satisfied, the difference is shaded in red.…”

Section: Time-window Impact Classificationmentioning

confidence: 99%

Section: Time-window Impact Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aim-Aware Collision Monitoring: Discriminating Between Expected and Unexpected Post-Impact Behaviors

Proper,

Kurdas,

Abdolshah

et al. 2023

IEEE Robot. Autom. Lett.

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“…Magrini and De Luca in their work [26] implemented a finite state machine with 3 basic robot states: idle state, null space redundancy state and high compliance mode. In our previous works [27], [28] a similar finite state machine was proposed for switching between six different scenarios of robot behavior.…”

Section: Classification and Reactionmentioning

confidence: 99%

Multi-Scenario Contacts Handling for Collaborative Robots Applications

Popov

Mikhel

Yagfarov

et al. 2021

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

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The goal of this work is to propose a way of dealing with physical interactions for collaborative robots that will ensure the safety of a human operator and improve the performance of a common task by implementing multiple robot behavior scenarios. In this scope, all collisions of a robotic arm are detected and analyzed to chooses an appropriate reaction strategy. The points of contact on the robot's surface for each collision are estimated, the external forces are identified and collisions are classified by the set of predefined categories. Based on these categories and the current robot state, the algorithm chose an appropriate behavior scenario.All presented algorithms are based only on proprioceptive sensors information and were tested in a simulated environment and on the real collaborative robots KUKA iiwa and Universal Robots UR10e. The result for contact localization showed 4 cm mean accuracy, the classification algorithm was able to identify collisions with hard and soft objects with 98% accuracy for KUKA iiwa 14.

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“…In its simplest form pHRI boils down to monitoring robots' collisions with the environment or humans and stopping it if a collision has been detected. A more sophisticated realization of pHRI in terms of software should include collision localization (Haddadin et al, 2017 ; Mikhel et al, 2019 ), collision reaction strategies (Haddadin et al, 2008 ), relevant control techniques such as force/impedance control, and real-time motion planning (De Santis et al, 2008 ). In this paper, we focus on a simple form of pHRI which can be used independently—in situations when a robot is performing a task and a human happens to be in its way, or in situations when a human, by deliberately coming into contact with a robot, can prevent it from hurting itself, others, or damage the environment—or as a part of more sophisticated pHRI used for supportive, collaborative, or cooperative interactions (Haddadin and Croft, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Practical Aspects of Model-Based Collision Detection

Mamedov

Mikhel

2020

Front. Robot. AI

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Recently, with the increased number of robots entering numerous manufacturing fields, a considerable wealth of literature has appeared on the theme of physical human-robot interaction using data from proprioceptive sensors (motor or/and load side encoders). Most of the studies have then the accurate dynamic model of a robot for granted. In practice, however, model identification and observer design proceeds collision detection. To the best of our knowledge, no previous study has systematically investigated each aspect underlying physical human-robot interaction and the relationship between those aspects. In this paper, we bridge this gap by first reviewing the literature on model identification, disturbance estimation and collision detection, and discussing the relationship between the three, then by examining the practical sides of model-based collision detection on a case study conducted on UR10e. We show that the model identification step is critical for accurate collision detection, while the choice of the observer should be mostly based on computation time and the simplicity and flexibility of tuning. It is hoped that this study can serve as a roadmap to equip industrial robots with basic physical human-robot interaction capabilities.

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Development of typical collision reactions in combination with algorithms for external impacts identification

Cited by 3 publications

References 10 publications

Aim-Aware Collision Monitoring: Discriminating Between Expected and Unexpected Post-Impact Behaviors

Aim-Aware Collision Monitoring: Discriminating Between Expected and Unexpected Post-Impact Behaviors

Multi-Scenario Contacts Handling for Collaborative Robots Applications

Practical Aspects of Model-Based Collision Detection

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