Full-body collision detection and reaction with omnidirectional mobile platforms: a step towards safe human–robot interaction

Kim, Kwan‐Suk; Llado, Travis; Sentis, Luis

doi:10.1007/s10514-015-9464-x

Cited by 22 publications

(6 citation statements)

References 27 publications

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“…However, for devices that require force feedback, it is not only necessary to determine whether the model is in a collision state but also to determine a specific collision position, because the magnitude and direction of the feedback force are directly related to the collision position. At this time, the judgment target should be simplified to the following composition: for the polyhedron of the geometric model (Poly), it is necessary to judge whether the instrument collides with the polyhedron node path on the organ and what collision occurs and then extract the corresponding information from the colliding polyhedron [ 19 ], to calculate the magnitude of the feedback force. Both methods have their own advantages and disadvantages.…”

Section: Collision Detection and Force Feedback Algorithm In Virtumentioning

confidence: 99%

Study on Collision Detection and Force Feedback Algorithm in Virtual Surgery

Luo

2021

Journal of Healthcare Engineering

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The development of virtual reality technology is expected to solve traditional surgical training. The lack of methods has brought revolutionary advances in technology. The virtual surgery system based on collision detection and force feedback can enable the operator to have stronger interaction, which is an exploration of the feature of touch in virtual reality technology. Reality is an important indicator of the virtual surgical system. This article improves the realism of the system from the visual and tactile senses and uses the surrounding ball collision detection and force feedback algorithms to build a realistic surgical platform. In the virtual surgery training system, the introduction of force feedback greatly improves the sense of presence during virtual surgery interaction. The operator can feel the softness and hardness of different tissues and organs through the force feedback device. Virtual reality is an interdisciplinary comprehensive technology that has been widely used in military, film, medical, and gaming fields. Virtual reality can simulate the objective world and display it visually, making people feel immersive. Virtual surgery provides surgeons with a recyclable surgical practice platform and can help doctors perform preoperative rehearsals and predict the results of surgery. The design of collision detection and force feedback algorithms is a prerequisite to ensure the immersion and transparency of the virtual surgical training system. This article mainly introduces the collision detection and force feedback algorithm research in virtual surgery, with the intention of providing some ideas and directions for the development of virtual surgery. This paper proposes two collision detection algorithms, space decomposition method and hierarchical bounding box method, and three force feedback algorithms including spring mass point algorithm, Runge–Kutta method, and Euler method to construct virtual surgery collision detection and force feedback. Experiment with the Overall System Architecture. This paper proves through experimental results that the average collision detection time after the application of the improved collision detection and force feedback algorithm in the virtual surgery system is more than 80.7% less than the traditional method, which greatly improves the detection speed.

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Section: Collision Detection and Force Feedback Algorithm In Virtumentioning

confidence: 99%

Study on Collision Detection and Force Feedback Algorithm in Virtual Surgery

Luo

2021

Journal of Healthcare Engineering

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“…Where current state of the art has focused mostly in the first phase only (pre-collision) [22][23][24][25]27]. While detection, isolation, and identification of collisions for mobile robots is still a complex problem addressed in few works [43,44]. Moreover, reaction and post-collision response is yet very unexplored [45].…”

Section: Hazard Deriving From Physical Contactmentioning

confidence: 99%

Safety Concerns Emerging from Robots Navigating in Crowded Pedestrian Areas

Salvini

Paez-Granados

Billard

2021

Int J of Soc Robotics

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The slogan “robots will pervade our environment” has become a reality. Drones and ground robots are used for commercial purposes while semi-autonomous driving systems are standard accessories to traditional cars. However, while our eyes have been riveted on dangers and accidents arising from drones falling and autonomous cars’ crashing, much less attention has been ported to dangers arising from the imminent arrival of robots that share the floor with pedestrians and will mix with human crowds. These robots range from semi or autonomous mobile platforms designed for providing several kinds of service, such as assistant, patrolling, tour-guide, delivery, human transportation, etc. We highlight and discuss potential sources of injury emerging from contacts of robots with pedestrians through a set of case studies. We look specifically at dangers deriving from robots moving in dense crowds. In such situations, contact will not only be unavoidable, but may be desirable to ensure that the robot moves with the flow. As an outlook toward the future, we also offer some thoughts on the psychological risks, beyond the physical hazards, arising from the robot’s appearance and behaviour. We also advocate for new policies to regulate mobile robots traffic and enforce proper end user’s training.

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“…We show that the robot could successfully slide against the obstacle without exceeding the limit on contact force. Although current implementation relies on the assumption of single contact, this could be extended with higher sensing resolution such as multi-contact sensing in mobile manipulators through artificial skin [23], or through other sensing methods [24]. The source code and simulations with other robot types is available at: https://github.com/epfllasa/sliding-ds-control [25].…”

Section: Introductionmentioning

confidence: 99%