The aim of this article is to analyze the effect of kinematic parameters on a novel proposed on-line motion planning algorithm for an articulated vehicle based on Model Predictive Control. The kinematic parameters that are going to be investigated are the vehicle's velocity, the maximum allowable change in the articulated steering angle, the safety distance from the obstacles and the total number of obstacles in the operating arena. The proposed modified path planning algorithm for the articulated vehicle belongs to the family of Bug-Like algorithms and is able to take under consideration, the mechanical and physical constraints of the articulated vehicle, as well as its full kinematic model. During the on-line motion planning algorithm, the MPC controller controls the lateral motion of the vehicle, through the rate of the articulation angle, while driving it accurately and safely over the on-line formulated desired path. The efficiency of the proposed combined path planning and control scheme is being evaluated under numerous simulated test cases, while exhaustive simulations have been made for analyzing the dependency of the proposed framework on the kinematic parameters.
In this article, a complete analysis towards the development of a switching modeling and control framework for an articulated vehicle, under the effect of varying slip angles will be presented. The established nonlinear kinematic model, of the nonholonomic articulated vehicle, will be transformed into an error dynamics model, which in the sequel will be linearized around multiple nominal slip angle cases. The proposed control architecture will consist of a switching control scheme, based on multiple model predictive controllers, for the articulated vehicle under varying slip angles. The controllers will be developed in order to improve the performance of the articulated vehicle's path tracking, while compensating the varying slippage effect. The current measured slip angle is being considered as the switching rule and a corresponding switching control scheme is being defined, being able to apply constraints on the states, the control signal and the output variables. Both the non-slip and slip models will be derived to highlight the significance of accounting for slips in path following control and their significant effect on deteriorating the performance of the overall control scheme when not considered. Multiple simulation results will be presented to prove the efficacy of the overall suggested scheme.
In this article, a novel on-line path planning algorithm for an articulated vehicle, moving in a partially known and sensory based reconstructed environment, and relying on Model Predictive Control will be presented. The proposed algorithm belongs to the family of bug like path planning algorithms and has the capability to take under consideration the real dynamics of the articulated vehicle. Based on: a) an a priori knowledge of the current and the goal points, and b) a partial sensory based awareness of the surrounding environment, the algorithm is able to tune online the articulated steering angle in order to drive the front and the rear parts of the vehicle from avoiding collision with obstacles, while converging to the goal point. The proposed path planning algorithm is able to produce on-line the next reference waypoint, solving the local and sub-optimal problem, while in the sequel a Model Predictive Controller is being utilized for creating the proper control signal, the rate of the articulated angle based on an error dynamics kinematic model of the vehicle. Multiple simulation results are being presented that prove the efficiency of the suggested scheme.
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