Dynamic object localization via a proximity sensor network

In this paper we describe a proximity sensor network (PSN) consisting solely of inexpensive intensity-based electro-optical proximity sensors embedded in a robotic end-effector. By coupling the PSN to an extended Kalman filter, and by resolving the sensors´ dependence on ambient light, the object´s reflective properties and the angle between sensor beam and the object´s surface, we succeed in estimating a cylindrical moving object´s unknown planar trajectory. Experiments show robust position and velocity estimation of the moving object, despite noisy sensor data and unmodelled object dynamics. Such a system should be directly applicable to sensor-based control approaches for dynamic grasping and manipulation in robotics and automation