INTEGRATION OF MULTIPLE SENSOR SPACES WITH LIMITED SENSING RANGE AND REDUNDANCY

Yuichi Kobayashi, Eisuke Kurita, and Manabu Gouko

Keywords

Robot motion learning, limitedsensing range, multiple sensors, redundant observation

Abstract

Robot sensors sometimes do not have a complete view of a scene, because of occlusion or limited-sensing range. It is a challenge to accurately navigate such robots without integrating the information from multiple sensors in the world coordinate. This paper presents a motion-generation method for a robot having multiple sensors with limited sensing ranges. The proposed method introduces an extension of action-observation mapping to outside the sensing range of a sensor on the basis of diffusion-based learning of Jacobian matrices between control input and observation variables. Multiple observation spaces can be integrated by finding a correspondence among the virtually extended observation spaces. When a target observation variable is given to the robot, using an extended observation space, it can generate a motion from an observation space toward the target having another observation space. In addition, using a nonlinear surface-approximation framework, a dimension-reduction method is presented to deal with the case in which sensors provide redundant information on the robot’s configuration. The proposed framework is verified by two robot tasks; reaching motion toward the floor using a manipulator and navigation of the mobile robot around a wall. In both cases, the observation space of a camera with a limited view was extended and appropriate motion trajectories were obtained.

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