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Using Complete Coverage Path Planning (CCPP), a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs to be replanned when the path might become invalid. In previous CCPP methods, when the neighbours of the current position are obstacles or have been visited, it is challenging for the robot to escape from the deadlocks with the least extra time cost. In this study, a novel CCPP algorithm is proposed to deal with deadlock problems in a dynamic environment. A priority template inspired by the short memory model could reduce the number of deadlocks by giving the priority of directions. Simultaneously, a global backtracking mechanism guides the robot to move to the next unvisited area quickly, taking the use of the explored global environmental information. What's more, the authors extend their CCPP algorithm to a multi-robot system with a market-based bidding process which could deploy the coverage time. Experiments of apartment-like scenes show that the authors' proposed algorithm can guarantee an efficient collision-free coverage in dynamic environments. The proposed method performs better than related approaches on coverage rate and overlap length. 2 Assumptions, definitions and notations Some notations, assumptions and definitions in this paper are described as follows: 2.1 Assumptions (i) The robot obtains information on a limited range and locates itself accurately or within a tolerable error, uncertainties via its on-board sensors (e.g. radar, laser-scanner and sonar), multi-sensory fusion and simultaneous localisation and mapping are beyond our scope. (ii) The robot can move to eight potential directions (front, back, left, right, front left, front right, back left, and back right) as a real CAAI Transactions on Intelligence Technology

Recently, unsupervised methods for monocular visual odometry (VO), with no need for quantities of expensive labeled ground truth, have attracted much attention. However, these methods are inadequate for long-term odometry task, due to the inherent limitation of only using monocular visual data and the inability to handle the error accumulation problem. By utilizing supplemental low-cost inertial measurements, and exploiting the multi-view geometric constraint and sequential constraint, an unsupervised visual-inertial odometry framework (UnVIO) is proposed in this paper. Our method is able to predict the per-frame depth map, as well as extracting and self-adaptively fusing visual-inertial motion features from image-IMU stream to achieve long-term odometry task. A novel sliding window optimization strategy, which consists of an intra-window and an inter-window optimization, is introduced for overcoming the error accumulation and scale ambiguity problem. The intra-window optimization restrains the geometric inferences within the window through checking the photometric consistency. And the inter-window optimization checks the 3D geometric consistency and trajectory consistency among predictions of separate windows. Extensive experiments have been conducted on KITTI and Malaga datasets to demonstrate the superiority of UnVIO over other state-of-the-art VO / VIO methods. The codes are open-source.

To coupe with the difficulties in the process of inspection and classification of defects in Printed Circuit Board (PCB), other researchers have proposed many methods. However, few of them published their dataset before, which hindered the introduction and comparison of new methods. In this paper, we published a synthesized PCB dataset containing 1386 images with 6 kinds of defects for the use of detection, classification and registration tasks. Besides, we proposed a reference based method to inspect and trained an end-to-end convolutional neural network to classify the defects. Unlike conventional approaches that require pixel-by-pixel processing, our method firstly locate the defects and then classify them by neural networks, which shows superior performance on our dataset.

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