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This paper presents an attention-based, deep learning framework that converts robot camera frames with dynamic content into static frames to more easily apply simultaneous localization and mapping (SLAM) algorithms. The vast majority of SLAM methods have difficulty in the presence of dynamic objects appearing in the environment and occluding the area being captured by the camera. Despite past attempts to deal with dynamic objects, challenges remain to reconstruct large, occluded areas with complex backgrounds. Our proposed Dynamic-GAN framework employs a generative adversarial network to remove dynamic objects from a scene and inpaint a static image free of dynamic objects. The Dynamic-GAN framework utilizes spatial-temporal transformers, and a novel spatial-temporal loss function. The evaluation of Dynamic-GAN was comprehensively conducted both quantitatively and qualitatively by testing it on benchmark datasets, and on a mobile robot in indoor navigation environments. As people appeared dynamically in close proximity to the robot, results showed that large, feature-rich occluded areas can be accurately reconstructed with our attention-based deep learning framework for dynamic object removal. Through experiments we demonstrate that our proposed algorithm has up to 25% better performance on average as compared to the standard benchmark algorithms. 

In modern industrial manufacturing processes, robotic manipulators are routinely used in the assembly, packaging, and material handling operations. During production, changing end-of-arm tooling is frequently necessary for process flexibility and reuse of robotic resources. In conventional operation, a tool changer is sometimes employed to load and unload end-effectors, however, the robot must be manually taught to locate the tool changers by operators via a teach pendant. During tool change teaching, the operator takes considerable effort and time to align the master and tool side of the coupler by adjusting the motion speed of the robotic arm and observing the alignment from different viewpoints. In this paper, a custom robotic system, the NeXus, was programmed to locate and change tools automatically via an RGB-D camera. The NeXus was configured as a multi-robot system for multiple tasks including assembly, bonding, and 3D printing of sensor arrays, solar cells, and microrobot prototypes. Thus, different tools are employed by an industrial robotic arm to position grippers, printers, and other types of end-effectors in the workspace. To improve the precision and cycle-time of the robotic tool change, we mounted an eye-in-hand RGB-D camera and employed visual servoing to automate the tool change process. We then compared the teaching time of the tool location using this system and compared the cycle time with those of 6 human operators in the manual mode. We concluded that the tool location time in automated mode, on average, more than two times lower than the expert human operators.