Computer Vision and Route Planning for Humanoid Robots

Our research focuses on vision-based route planning for the NAO humanoid robot. The robot is required to visually observe a scene and decide the shortest possible route for visiting the points of interest in that scene. A robust method for processing image information is used to determine the locations to be visited. We use a perspective projection algorithm to map points from a camera image to locations in three-dimensional space. A camera calibration algorithm finds the distance from the camera to the image plane. Linear regression is used to obtain the equations of camera calibration lines. Thresholds and binary masks are used to distinguish locations in the camera image. Connected component algorithms are used to label and group objects. We use brute force optimization to solve the path planning problem. A matrix containing distances between all pairs of objects is computed, and then a brute force search is used to find the shortest path between those objects. In case the number of objects is greater than about 10, brute force is not computationally feasible, and so artificial intelligence algorithms are used to find the shortest path.https://engagedscholarship.csuohio.edu/u_poster_2014/1016/thumbnail.jp

Regrasp Planning using 10,000s of Grasps

This paper develops intelligent algorithms for robots to reorient objects. Given the initial and goal poses of an object, the proposed algorithms plan a sequence of robot poses and grasp configurations that reorient the object from its initial pose to the goal. While the topic has been studied extensively in previous work, this paper makes important improvements in grasp planning by using over-segmented meshes, in data storage by using relational database, and in regrasp planning by mixing real-world roadmaps. The improvements enable robots to do robust regrasp planning using 10,000s of grasps and their relationships in interactive time. The proposed algorithms are validated using various objects and robots

Algorithm Engineering in Robust Optimization

Robust optimization is a young and emerging field of research having received a considerable increase of interest over the last decade. In this paper, we argue that the the algorithm engineering methodology fits very well to the field of robust optimization and yields a rewarding new perspective on both the current state of research and open research directions. To this end we go through the algorithm engineering cycle of design and analysis of concepts, development and implementation of algorithms, and theoretical and experimental evaluation. We show that many ideas of algorithm engineering have already been applied in publications on robust optimization. Most work on robust optimization is devoted to analysis of the concepts and the development of algorithms, some papers deal with the evaluation of a particular concept in case studies, and work on comparison of concepts just starts. What is still a drawback in many papers on robustness is the missing link to include the results of the experiments again in the design