Robot haptics ultimately consists of a set of models which interpret and predict a robot’s physical interaction with the world. In this paper, we describe one approach to modeling support friction within a two-dimensional environment consisting of a single robot finger pushing objects on a table. Instead of explicitly modeling the friction distribution between the object and the table, we learn the mapping between pushes and the motion of the object using an online, memory-based model using local regression. The resulting manipulation map implicitly describes the support friction without a complex model. We also describe methods of acquiring object shape and localizing the object using a proximity sensor. Results are presented for objects with different friction distributions.

Human perception is based on differences. After all, nobody will ask you to hold an object and measure its weight to three significant figures and expect you to succeed. But comparing the weight of the object to another object (say, an apple) would be an easy task and an everyday occurrence. In this paper we explore support friction estimation for pushing operations. We present two difference-based techniques for measuring friction (a transition-based method and a work-based method) and test each using two different force sensors (a commercial sensor and an estimation method based on motor torque). Our results show that low-cost, noisy force sensors perform just as well as highly accurate sensors when used with our difference-based friction estimation techniques. Finally, we present an algorithm which determines if two objects have statistically similar support friction coefficients using a modified version of Student’s t-test.

This paper presents the design of an optical fiber proximity sensor for haptic exploration with a robotic finger. The sensor uses emitter and receiver optical fiber pairs to measure the intensity of light reflected off surrounding objects in a 2-D workspace. We present the design and construction a 32-point sensor array mounted within a 36 mm diameter finger and describe software techniques to process data acquired by an inexpensive web-cam. We experimentally characterize the sensor performance and demonstrate applications for haptic exploration such as pre-contact velocity reduction and non-contact contour following based on object curvature.

Magnetometry is widely used for the characterization of areas contaminated by unexploded ordnance (UXO). To successfully discriminate hazardous UXO from nonhazardous clutter requires accurate models of the ordnance response. This paper develops an ordnance library with 15 different items using total-field magnetic data collected over a test stand. The induced and remanent magnetizations were obtained by varying the three-dimensional orientation of each item and measuring the magnetic field on a horizontal plane in the dipolar regime. Replicate measurements using multiple specimens of the same ordnance returned very similar induced magnetizations. The fitted moments were used to estimate the detection depths for different sensor noise floors. A prolate spheroid with a 3.5 aspect ratio was used to provide a good approximation to the detection depths for many of the ordnance items. Assuming a 1-nT noise floor, these orientation-dependent detection depths ranged from 10 to 17 times of the object's diameter.

This paper describes a prototype robot and the necessary path planning and control for space inspection applications. The robot is the first generation of a free-flying robotic camera that will assist astronauts in constructing and maintaining the Space Station. The robot will provide remote views to astronauts inside the Space Shuttle and future Space Station, and to ground controllers. The paper describes a planar robot prototype autonomously moving about an air bearing table, and introduces a method for determining paths in three-dimensions for efficient fuel use. Finally, the paper describes the software simulation of the path planner with the future Space Station.