A customizable anthropomorphic telepresence robot (CATR) is an emerging medium that might have the highest degree of social presence among the existing mediated communication mediums. Unfortunately, there are problems with teleoperating a CATR, and these problems can deteriorate the gesture motion in a CATR. These problems are the disruption during decoupling, discontinuity due to the unstable transmission and jerkiness due to the reactive collision avoidance. From the review, none of the existing interfaces can simultaneously fix all of the problems. Hence, a novel framework with the perception-link behavior model (PLBM) was proposed. The PLBM adopts the distributed spatiotemporal representation for all of its input signals. Equipping it with other components, the PLBM can solve the above problems with some limitations. For instance, the PLBM can retrieve missing modalities from its experience during decoupling. Next, the PLBM can handle up to a high level of drop rate in the network connection because it is dealing with gesture style and not pose. For collision prevention, the PLBM can tune the incoming gesture style so that the CATR can deliberately and smoothly avoid a collision. In summary, the framework consists of PLBM being able to increase the user’s presence on a CATR by synthesizing expressive user gestures.
Optic Flow-Based Reactive Collision Prevention for MAVs Using the Fictitious Obstacle Hypothesis
