Image-to-image translation based on deep learning has attracted interest in the robotics and vision community because of its potential impact on terrain analysis and image representation, interpretation, modification, and enhancement. Currently, the most successful approach for generating a translated image is a conditional generative adversarial network (cGAN) for training an autoencoder with skip connections. Despite its impressive performance, it has low accuracy and a lack of consistency; further, its training is imbalanced. This paper proposes a balanced training strategy for image-to-image translation, resulting in an accurate and consistent network. The proposed approach uses two generators and a single discriminator. The generators translate images from one domain to another. The discriminator takes the input of three different configurations and guides both the generators to generate realistic images in their corresponding domains while ensuring high accuracy and consistency. Experiments are conducted on different datasets. In particular, the proposed approach outperforms the cGAN in realistic image translation in terms of accuracy and consistency in training.

SUMMARYMobile robots in field environment travel not only on even terrain but also on uneven or sloped terrain. Practical methods for preventing turnover of the mobile robot are essential since the turnover of the mobile robot is very perilous. This paper proposes an efficient algorithm for preventing turnover of a mobile robot on uneven terrain by controlling linear acceleration and rotational velocity of the mobile robot. The concept of the modified zero moment point (ZMP) is proposed for evaluating the potential turnover of the mobile robot. Also, the turnover stability indices for linear acceleration and rotational velocity are defined with the modified ZMP. The turnover stability space (TSS) with turnover stability indices is presented to control the mobile robot in order to avoid turnover effectively. Finally, the feasibility and effectiveness of the proposed algorithm are verified through simulations conducted on a three-wheeled mobile robot.

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Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were based on either attribute-independent or attribute-dependent approaches. The attribute-independent approach, in which the modification is performed in the latent representation, has performance limitations because it requires paired data for changing the desired attributes. In contrast, the attribute-dependent approach is effective because it can modify the required features while maintaining the information in the given image. However, the attribute-dependent approach is sensitive to attribute modifications performed while preserving the face identity, and requires a careful model design for generating high-quality results. To address this problem, we propose a fine-tuned attribute modification network (FTAMN). The FTAMN comprises a single generator and two discriminators. The discriminators use the modified image in two configurations with the binary attributes to fine tune the generator such that the generator can generate high-quality attribute-modification results. Experimental results obtained using the CelebA dataset verify the feasibility and effectiveness of the proposed FTAMN for editing multiple facial attributes while preserving the other details.