A fully autonomous active sensor-based exploration concept for shape-sensing robots

A technique is developed for the automatic, online generation of trajectories for robotic exploration of unknown objects. For manipulators and end effectors with an arbitrary assortment of sensing apparatus, especially tactile, the Kalman filtering approach is given for the estimation of unknown surface parameters. A cost function is found based on the inverse error covariance matrix for the parameter estimates. It is shown that this cost function can be written in terms of tangent vectors on the surface of the object. The cost can therefore be minimized over possible end effector velocities, resulting in optimally efficient exploration of the surface. Given an estimator and system model, the exploration path automatically generated in this way endows the robot with the ability to seek out geometric surface features that provide the most utility for the improved convergence of the parameter estimate. Computer simulations for a nine-dimensional surface parameter vector show that the algorithm is fast, accurate, and completely independent of the parametrization chosen for the analytic surface models