Unified Modeling of Unconventional Modular and Reconfigurable Manipulation System

Customization of manipulator configurations using modularity and reconfigurability aspects is receiving much attention. Modules presented so far in literature deals with the conventional and standard configurations. This paper presents the 3D printable, light-weight and unconventional modules: MOIRs' Mark-2, to develop any custom `n'-Degrees-of-Freedom (DoF) serial manipulator even with the non-parallel and non-perpendicular jointed configuration. These unconventional designs of modular configurations seek an easy adaptable solution for both modular assembly and software interfaces for automatic modeling and control. A strategy of assembling the modules, automatic and unified modeling of the modular and reconfigurable manipulators with unconventional parameters is proposed in this paper using the proposed 4 modular units. A reconfigurable software architecture is presented for the automatic generation of kinematic and dynamic models and configuration files, through which, a designer can design, validate using visualization, plan and execute the motion of the developed configuration as required. The framework developed is based upon an open source platform called as Robot Operating System (ROS), which acts as a digital twin for the modular configurations. For the experimental demonstration, a 3D printed modular library is developed and an unconventional configuration is assembled, using the proposed modules followed by automatic modeling and control, for a single cell of the vertical farm setup

Memory based neural networks for end-to-end autonomous driving

Recent works in end-to-end control for autonomous driving have investigated the use of vision-based exteroceptive perception. Inspired by such results, we propose a new end-to-end memory-based neural architecture for robot steering and throttle control. We describe and compare this architecture with previous approaches using fundamental error metrics (MAE, MSE) and several external metrics based on their performance on simulated test circuits. The presented work demonstrates the advantages of using internal memory for better generalization capabilities of the model and allowing it to drive in a broader amount of circuits/situations. We analyze the algorithm in a wide range of environments and conclude that the proposed pipeline is robust to varying camera configurations. All the present work, including datasets, network models architectures, weights, simulator, and comparison software, is open source and easy to replicate and extend.Comment: 6 pages, 3 figures, Code available: https://github.com/JdeRobot/BehaviorMetrics and https://www.github.com/JdeRobot/DeepLearningStudi