This paper presents an approach that generates the overall trajectory of mobile manipulators for a complex mission consisting of several sub-tasks. Parametric dynamic movement primitives (PDMPs) can quickly generalize the online motion of robot manipulation by learning multiple demonstrations in offline. However, regarding complex missions consisting of multiple sub-tasks, a large number of demonstrations are required for full generalization, which is impractical. In this paper, we propose a framework that reduces the number of demonstrations for a complex mission. In the proposed method, complex demonstrations are segmented into multiple unit motions representing sub-tasks, and one PDMP is formed per each segment, resulting in multiple PDMPs. The phase decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. In order to generalize the execution time and regional goal in each phase, the Gaussian process regression (GPR) is applied. Simulation results from two different scenarios confirm that the proposed framework not only effectively reduces the number of demonstrations but also improves generalization performance. The actual experiments also demonstrate that the mobile manipulators effectively perform complex missions through the proposed framework. Note to Practitioners—This paper presents an approach of learning from demonstration (LfD) to generalize complex movements of robots. Parametric dynamic movement primitives (PDMPs) compute styles of movements from multiple demonstrations. However, the complexity of the PDMP increases as the mission involves more sub-tasks. In this paper, we resolve this issue by segmenting the complex mission into multiple sub-tasks and configuring multiple PDMPs. This work effectively reduces the number of required demonstrations for PDMPs, moderates the complexity of the algorithm. Also, the proposed approach allows flexible sub-task sequencing. It enables the mission in an unlearned sequence or a new combination of sub-tasks. The proposed approach is validated in both simulation and experimental results. Our approach is applicable for complex missions whose sub-tasks are clearly identified

Multirobot simultaneous localization and mapping (SLAM) requires technical ingredients such as systematic construction of multiple SLAM systems and collection of information from each robot. In particular, map fusion is an essential process of multirobot SLAM that combines multiple local maps estimated by team robots into a global map. Fusion of multiple local maps is usually based on interloop detection that recognizes the same scene visited by multiple robots, or robot rendezvous where a member(s) of a robot team is observed in another member's images. This article proposes a collaborative monocular SLAM including a map fusion algorithm that utilizes rendezvous, which can happen when multirobot team members operate in close proximity. Unlike existing rendezvous-based approaches that require additional sensors, the proposed system uses a monocular camera only. Our system can recognize robot rendezvous using nonstatic features (NSFs) without fiducial markers to identify team robots. NSFs, which are abandoned as outliers in typical SLAM systems for not supporting ego-motion, can include relative bearing measurements between robots in a rendezvous situation. The proposed pipeline consists of the following: first, a feature identification module that extracts the relative bearing measurements between robots from NSFs consisting of anonymous bearing vectors with false positives, and second, a map fusion module that integrates the map from the observer robot with the maps from the observed robots using identified relative measurements. The feature identification module can operate quickly using the proposed alternating minimization algorithm formulated by two subproblems with closed-form solutions. The experimental results confirm that our collaborative monocular SLAM system recognizes rendezvous rapidly and robustly, and fuses local maps of team robots into a global map accurately.

In manufacturing CPT (Color Picture Tubes) used in TV sets and monitors, an ITC (Integrated Tube Components) adjustment process is involved. Automating the ITC adjustment process needs fast and robust visual sensing/measurement. Real time color purity and convergence measurement algorithms are described. Existing color purity measurement algorithms for ITC adjustment system often take too much time to detect color purity. Moreover, for convergence measurement, measurable data is not sufficient for accurate detection. To overcome these difficulties, real time vision system with 9 area cameras and many linear array cameras is developed. Area cameras are used for color purity measurements and linear array cameras are used for convergence measurements. To devise the purity measurement algorithm, the characteristics of CPT microscopic images are analyzed. To measure the exact color purity, several methods are selectively applied for calculating color purity depending on the conditions of CPT screen. For convergence measurement, special test CPT with no shadow mask and no black matrix is manufactured and a new measurement algorithm is developed using fuzzy inference and a priori knowledge taken from the specially manufactured CPT. The proposed algorithms are successfully applied as described in the experimental results.>