Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to competently interact with diverse road users in diverse scenarios remains largely unsolved. Learning methods have much to offer towards solving this problem. But they require a realistic multi-agent simulator that generates diverse and competent driving interactions. To meet this need, we develop a dedicated simulation platform called SMARTS (Scalable Multi-Agent RL Training School). SMARTS supports the training, accumulation, and use of diverse behavior models of road users. These are in turn used to create increasingly more realistic and diverse interactions that enable deeper and broader research on multiagent interaction. In this paper, we describe the design goals of SMARTS, explain its basic architecture and its key features, and illustrate its use through concrete multi-agent experiments on interactive scenarios. We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving. Our code is available at https://github.com/huawei-noah/SMARTS.
In this paper, an optimization method for designing MR clutches is studied. The proposed method optimizes the geometrical dimensions of an MR clutch, hence its mass, for given output torque and electrical input power. The main idea behind this optimization is that the input power and output torque are two parameters that are normally known to the designer prior to the design of an MR clutch and considering these parameters in the optimization as fixed values has a practical significance. Having presented the optimization method, we compare the characteristics of three different MR clutch configurations in order to demonstrate the effectiveness of the proposed method. A comparison between the drum, single-disk and multi-disk configurations of MR clutches is performed. Using the proposed method one can select a suitable configuration as well as the geometrical dimensions for an MR clutch that best suits the requirements of each individual design.
In this paper, a new open-loop model for a magnetorheological-based actuator is presented. The model consists of two parts relating the output torque of the actuator to its internal magnetic field, and the internal magnetic field to the applied current. Each part possesses its own hysteretic behavior. The first part uses a novel nonlinear adaptive model that relates the internal magnetic field to the applied current. The second part uses an open-loop Bingham model to relate the output torque to an internal magnetic field. The model facilitates accurate control of the actuator using its input current. It also eliminates the need for force/torque sensors for providing feedback signals. The accuracy of the constructed model is validated through simulations. The model is assessed against a widely accepted hysteresis modeling approach, known as the Preisach model and its advantages are highlighted. Experimental results using the prototyped actuation mechanism further verify the accuracy of the model and demonstrate its effectiveness.Index Terms-Adaptive modeling, hysteresis, magnetorheological fluids (MRFs), smart actuators.
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