A new actuation method for one-link flexible arms is presented. The endpoint control of a flexible arm has been known as a nonminimum phase system due to the noncollocated sensor and actuator. By relocating the actuator near the endpoint, the system can be modified to approximate a minimum phase system. In order to implement this, transmission mechanisms are developed which transform the actuator torque to a combination of force and torque and transmit them to an appropriate point on the arm link. Exact pole-zero configurations are analyzed with regard to the location of the actuation point and the type of actuator used. Guidelines for design of the transmission mechanisms and the actuation points are developed with respect to the operation bandwidth, stability and controllability. A prototype flexible arm is designed based on the design guidelines and open-loop and closed-loop tests are performed to verify the effectiveness.
For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.
A concurrent design method of mechanical structure and control is developed for two-link high speed robots. An integrated design approach to achieve high speed positioning is explored, in which comprehensive design parameters describing arm link geometry, actuator locations, and feedback gains are optimized with respect to the settling time of the system. First, a two-link, nonrigid arm is analyzed and a simple dynamic model representing rapid positioning processes is obtained. Optimal feedback gains minimizing the settling time are obtained as functions of structural parameters involved in the dynamic model. The structural parameters are then optimized using a nonlinear programming technique in order to obtain an overall optimal performance. Based on the optimal design, a prototype high speed robot is built and tested. The resultant arm design shows an outstanding performance, which is otherwise unattainable if the structure and control are designed separately.
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