It is generally difficult to measure complex shapes such as stairs with high accuracy for indoor environment scanning by the robot. Therefore, we consider the three-dimensional (3D) reconstruction of stairs using Structure from Motion (SfM) and Multi-View Stereo (MVS) which perform 3D reconstruction from images acquired by the robot vision. In this study, we verify whether it is possible to acquire a 3D reconstruction result of stairs by using images shot while ascending and descending the stairs as input to the reconstruction method. To calculate the accuracy of the reconstruction result, we use 3D computer graphics software to generate artificial image data to be applied to the 3D reconstruction. Experimental results show that 3D reconstruction results of the stairs are more accurate by applying both images shot when ascending and descending stairs to the 3D reconstruction methods.

Efficient knowledge sharing, computation load minimization, and collision-free movement are very important issues in the field of multi-robot automation. Several cloud robot architectures have been investigated to fulfill these requirements. However, the performance of the cloud-robot architectures created to date are suboptimal due to the lack of efficient data management for multi-robotic systems. With this point in mind, this paper proposes an efficient cloud multi-robot framework with cloud database model for mobile robot applications to facilitate multi-robot management, communication, and resource sharing. In this proposed architecture, the cloud framework is comprised with cloud data analysis, cloud database management, and cloud service management. The data analysis serves different data processing and decision-making tasks for generating the next robot action based on robot sensors’ data with the help of a data access components layer. A multistage cloud database model distributes, stores, and accesses different categories of data related to robot sensors and environments. And cloud service facilitates multi-robot management, communication, and resource sharing in the cloud framework. Additionally, as a use case, a cloud-based convolutional neural network (CNN) model is introduced for learning and recognizing robot application data. The obtained results of our tests indicate that the proposed cloud-robot architecture provides efficient computation power, communications, and knowledge sharing for managing multi-mobile robot systems.

Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.

Logical errors in source code can be detected by probabilities obtained from a language model trained by the recurrent neural network (RNN). Using the probabilities and determining thresholds, places that are likely to be logic errors can be enumerated. However, when the threshold is set inappropriately, user may miss true logical errors because of passive extraction or unnecessary elements obtained from excessive extraction. Moreover, the probabilities of output from the language model are different for each task, so the threshold should be selected properly. In this paper, we propose a logic error detection algorithm using an RNN and an automatic threshold determination method. The proposed method selects thresholds using incorrect codes and can enhance the detection performance of the trained language model. For evaluating the proposed method, experiments with data from an online judge system, which is one of the educational systems that provide the automated judge for many programming tasks, are conducted. The experimental results show that the selected thresholds can be used to improve the logic error detection performance of the trained language model.