In this paper, a cascade control architecture for a brake-by-wire system suitable for motor racing applications is described. The system is composed of an electromechanical actuator, i.e., an electric motor, a transmission, a master cylinder, and a traditional hydraulic brake (pipe and caliper). Starting from a control-oriented model, a cascade control is proposed. An inner-loop controls the position; an outer the pressure. The outer loop features an adaptation mechanism to cope with the intrinsic time-varying nonlinearity of the position–pressure relationship. The stability and robustness of the pressure loop are proven. Extensive experimental validation, conducted on an instrumented motorbike on a test circuit by a professional rider, shows the performance of the system
Occupancy grid maps are by far the most used spatial representation of the environment for robot navigation. This paper proposes a simple and effective way to improve the occupancy grid accuracy by superimposing a small oscillation to the robot motion when a predefined path is given. The method is especially suited for range sensors with long range capabilities but poor angular resolution. The innovative solid state LiDAR technology is an example of such sensor configuration and is used in this work for the experimental evaluation of the presented dithering technique. Experimental results quantitatively demonstrated that the proposed oscillating motion is effective especially in speeding up the detection of corridor like clearances in the environment.
Despite the enormous progress of the last years, urban environments still represent a challenge for robot autonomous navigation. This paper focuses on the problem of detecting street pole-like obstacles using a monocular camera. Such obstacles, due to their thin structure, may be difficult to be detected by common active sensors like lasers. This is even more critical for innovative solid state LiDARs like the one employed in this work because, at the actual state, they are characterized by very low angular resolutions. The approach described here is based on identifying poles as long vertical structures in the image and in locating them with respect to the robot using a Kalman filter based depth estimation. This information can then be fused with the information coming from LiDARs realizing a complete obstacle detection module.
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