This paper presents a model predictive approach for collision avoidance of car-like robots. An optimal problem is formulated in terms of cost minimization under constraints. Information on each robot can be incorporated online in the nonlinear model predictive framework and kinematic constraints are treated by Karush-Kuhn-Tucker(KKT) condition. For distributed collision avoidance of multiple robots with two levels of a communication network, performances are compared. In comparison with different types of communication, how much information the robots share can cause difference in the performance. More successful collision avoidance was possible when the robots share enough amount of information.
This study proposes an adaptive control algorithm for lateral motion of a UGV (Unmanned Ground Vehicle) using an NN (Neural Networks). The lateral motion of the UGV can be corrupted with various uncertainties such as side slip. In order to compensate the performance degradation of the UGV under various uncertainties, an NN-based adaptive control is designed by utilizing a virtual control concept. Since both the drift and input gain terms are uncertain, the proposed method adapts the whole terms related to the difference between the nominal and real systems. To avoid a singularity problem with the adaptive control, the affine property of the UGV dynamic model is utilized and the overall closed-loop stability is analyzed rigorously. Finally, numerical simulations using Carsim are performed to validate the effectiveness of the proposed scheme.
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