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With the CPU times of 4.7 ms and 175.8 ms, we have P = [00:0080 0:0023 0 0:0030 0 0:0449 0:0220 0 0:0359] T z max (0P ) = 0:5159 < 1:Hence, the grasp is force closure. V. CONCLUSION AND FUTURE WORKA shortcut is found to simplify Liu's ray-shooting based algorithm for force-closure test [4]. The optimal objective value of the LP problem (2) with respect to t = 0P is the ratio of d2 to d1 ; that is, z max (0P ) = d 2 =d 1 . If z max (0P ) < 1, then the grasp is force closure; otherwise, it is not. Consequently, we can skip the steps of computing Q, d1 , and d2 . Having the geometric insight into the maximum z max (0P ), we can apply it to optimal grasp planning as a force-closure index. As this work goes beyond the topic of the paper, it is decent to leave it for the future. ACKNOWLEDGMENTThe authors wish to thank the editors and the reviewers for fair ratings of this paper and helpful suggestions for revision. REFERENCES[1] J. K. Salisbury and B. Roth, "Kinematic and force analysis of articulated hands," ASME Abstract-This paper presents a new map specifically designed for robots operating in large environments and possibly in higher dimensions. We call this map the hierarchical atlas because it is a multilevel and multiresolution representation. For this paper, the hierarchical atlas has two levels: at the highest level there is a topological map that organizes the free space into submaps at the lower level. The lower-level submaps are simply a collection of features. The hierarchical atlas allows us to perform calculations and run estimation techniques, such as Kalman filtering, in local areas without having to correlate and associate data for the entire map. This provides a means to explore and map large environments in the presence of uncertainty with a process named hierarchical simultaneous localization and mapping. As well as organizing information of the free space, the map also induces well-defined sensor-based control laws and a provably complete policy to explore unknown regions. The resulting map is also useful for other tasks such as navigation, obstacle avoidance, and global localization. Experimental results are presented showing successful map building and subsequent use of the map in large-scale spaces.Index Terms-Concurrent mapping and localization, generalized Voronoi diagram, Kalman filtering, mobile robots, simultaneous localization and mapping (SLAM), topological maps.

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The aim of this work was to improve the classification of green asparagus in an agro-export company by way of a Computer Vision System (CVS). Thus, an image analysis application was developed in the MATLAB® environment to classify green asparagus according to the absence of white spots and the width of the product. The CVS performance was compared with a manual classification using the error in the classification as the quality indicator; the yield from the raw material (%) and line productivity (kg/h) as the production indicators; and the net present value (USD) and internal rate of return (%) as the economic indicators. The CVS classified the green asparagus with 2% error; improved the yield from the raw material from 43% to 45%, and line productivity from 5 to 10 kg/h; and increased the net present value by 102,609.00 USD, yielding an Internal Rate of Return of 156.3%, much higher than the Opportunity Cost of the Capital (8.6%). Hence the classification of green asparagus by a CVS is an efficient and profitable alternative to manual classification.