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A new approach to track bicycles from imagery sensor data is proposed. It is based on detecting ellipsoids in the images, and treat these pair-wise using a dynamic bicycle model. One important application area is in automotive collision avoidance systems, where no dedicated systems for bicyclists yet exist and where very few theoretical studies have been published. Possible conflicts can be predicted...
We present a near-optimal deterministic filter for systems that evolve on the unit circle. Unlike suboptimal filtering algorithms that rely on approximations of the system, the proposed approach preserves the non-linear system model. This leads to an explicit bound on the optimality gap in terms of the tracking error. Specifically, the optimality gap is bounded by a term that is fourth-order in the...
Formation control in uncertain environment is studied, including the control process noise and measurement noise. The decoupled Kalman filter based on discrete model is established. And the control performances based on absolute positioning sensor, relative positioning sensor or fusion of these two kinds of sensor are studied. Simulations show that absolute positioning sensor could assure the formation...
The objective of this paper is to compare the performance of the proposed measurement assisted partial resampling (MAPR) particle filter against the performance of the extended Kalman filter (EKF) within the context of a dynamic 6 DoF hydrodynamic system. In order to compare the respective performances of the above two filters in resolving a navigation solution, the filters are given a trajectory...
This paper develops methodologies and techniques for control architecture design, path tracking laws and posture estimation of a vision-based wheeled mobile robot (WMR). To solve the problem of position/orientation tracking control of the WMR, two kinematical predictive control laws are developed to manipulate the vehicle to asymptotically follow the desired trajectories. A Kalman filtering scheme...
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