Advanced Robotics

Cho, Chang-Nho; Kim, Joon-Hong; Kim, Young-Loul; Song, Jae-Bok; Kyung, Jin-Ho

doi: 10.1080/01691864.2012.685259pmid: N/A

Abstract Industrial and service robots often physically interact with humans, and thus, human safety during these interactions becomes significantly important. Several solutions have been proposed to guarantee human safety, and one of the most practical, efficient solutions is the collision detection using generalized momentum and joint torque sensors. This method allows a robot to detect a collision and react to it as soon as possible to minimize the impact. However, the conventional collision detection methods cannot distinguish between intended contacts and unexpected collisions, and thus they cannot be used during certain tasks such as teaching and playback or force control. In this paper, we propose a novel collision detection algorithm which can distinguish intended contacts and unexpected collisions. In most cases, the external force during a collision shows a noticeably faster rate of change than that during an intended contact, and using this difference, the proposed observer can distinguish one from the other. Several experiments were conducted to show that the proposed algorithm can effectively distinguish intended contacts and unexpected collisions.

Zeng, Lingqi; Bone, Gary M.

doi: 10.1080/01691864.2012.703166pmid: N/A

Abstract In many service applications, mobile robots need to share their work areas with obstacles. Avoiding moving obstacles with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. An enhanced virtual force field-based mobile robot navigation algorithm (termed EVFF) is presented for avoiding moving obstacles with unpredictable direction changes. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions and an improved method for selecting the sense of the detour force to better avoid moving obstacles. For several challenging obstacle configurations, the EVFF algorithm is compared with five state-of-the-art navigation algorithms for moving obstacles. The navigation system with the new algorithm generated collision-free paths consistently. Methods for solving local minima conditions are proposed. Experimental results are also presented to further verify the avoidance performance of this algorithm.

Aleotti, Jacopo; Caselli, Stefano

doi: 10.1080/01691864.2012.703167pmid: N/A

Abstract According to neuro-psychology studies, 3D shape segmentation plays an important role in human perception of objects because when an object is perceived for grasping it is first parsed in its constituent parts. This capability is missing in current robot planning systems, which are therefore hindered in their ability to plan part-specific grasps suitable for the current task. In this paper, a novel approach for part-based grasping is presented that combines 3D shape segmentation, programing by human demonstration and manipulation planning. The central advantage over previous approaches is the use of a topological method for shape segmentation enabling both object categorization and robot grasping according to the affordances of an object. Manipulation tasks are demonstrated in a virtual reality environment using a data glove and a motion tracker, and the specific parts of the objects where grasping occurs are learned and encoded in the task description. Tasks are then planned and executed in a robot environment targeting semantically relevant parts for grasping. Planning in the robot environment can be generalized to objects that are similar to the ones used for task demonstration, i.e. objects that belong to the same category. Results obtained in 3D simulation confirm that the proposed approach finds with less effort grasps appropriate for the requested task.

Kelaiaia, Ridha; Zaatri, Abdelouahab; Company, Olivier

doi: 10.1080/01691864.2012.703168pmid: N/A

Abstract The optimal design of parallel kinematic machines goes through two fundamental stages. The first one concerns the structural synthesis. It enables, a priori, to determine the choice of families of the most adapted architectures according to the desired applications such as flight simulators, machine-tools, medical applications, etc. This can be done by applying several techniques such as: screw theory, Lie groups, graph theory, finite element method, etc. The second one concerns the dimensional synthesis and aims to determine the dimensions of the architecture that has been selected during the structural synthesis. This stage remains a major task because the criteria of performance of a given architecture are strongly dependent on its sizing. In this paper, we present a dimensioning methodology of the architectural parameters of the 6-dof UPS (U: universal joint, P: prismatic joint, and S: ball-and-socket joint) parallel manipulators (the positions of the attachment points of the actuators on the base and mobile plate as well as the radius of the base and the mobile plate). The problem will be formulated as a multiobjective optimization problem (MOOP) by taking into account simultaneously several criteria of performance such as the workspace, kinetostatic performances, stiffness, and dynamic dexterity. The SPEA-II genetic algorithm is adopted to solve this type of MOOP.

Park, Young-Bin; Suh, Il Hong

doi: 10.1080/01691864.2012.703169pmid: N/A

Abstract This paper presents a visual recognition method to identify types of corridor segments such as T-junctions, L-junctions, and dead ends using vanishing point-based visual features and a two-layer recognition framework. This approach is useful for efficient robot navigation in the sense that a mobile robot is able to recognize the corridor segment type before reaching it, allowing the robot to make navigation decisions in advance. Furthermore, owing to our novel visual features made by using nonvertical vanishing points satisfying Manhattan world assumption, it is more probable for a mobile robot to recognize corridor segment types under partial occlusion by human. Experimental results have also been provided to demonstrate the validity of the proposed approach in real world environments.