Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e.g., inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

Copper-Beryllium alloys have excellent wear resistance and high mechanical properties, they also possess good electrical and thermal conductivity, making these alloys very popular in a wide variety of industries, such as aerospace, in the fabrication of tools for hazardous environments and to produce injection molds and mold inserts. However, there are some problems in the processing of these alloys, particularly when these are subject to machining processes, causing tools to deteriorate quite rapidly, due to material adhesion to the tool’s surface, caused by the material’s ductile nature. An assessment of tool-wear after machining Cu-Be alloy AMPCOLOY 83 using coated and uncoated tools was performed, offering a comparison of the machining performance and wear behavior of solid-carbide uncoated and DLC/CrN multilayered coated end-mills with the same geometry. Multiple machining tests were conducted, varying the values for feed and cutting length. In the initial tests, cutting force values were registered. The material’s surface roughness was also evaluated and the cutting tools’ edges were subsequently analyzed, identifying the main wear mechanisms and how these developed during machining. The coated tools exhibited a better performance for shorter cutting lengths, producing a lower degree of roughness on the surface on the machined material. The wear registered for these tools was less intense than that of uncoated tools, which suffered more adhesive and abrasive damage. However, it was observed that, for greater cutting lengths, the uncoated tool performed better in terms of surface roughness and sustained wear.

This is a copy of the published version, or version of record, available on the publisher's website. This version does not track changes, errata, or withdrawals on the publisher's site.