Obstacle avoidance maneuver using optimal path generator embedded model predictive control

Abstract

Research and development are actively conducted for recent advances in the self-driving and unmanned vehicles, along with further efficiency and safety. In such fields, obstacle avoidance is frequently addressed in control applications through the concept of active safety technology. This paper investigates the real-time optimal obstacle avoidance control that the optimal path generator embedded model predictive controller. By offline computation, a large number of optimal obstacle avoidance control solutions are calculated to generate the supervised learning data for the deep-learning neural network used for the optimal path generator. Then, constructed optimal path generator is embedded in the model predictive controller to generate the reference command for the tracking task during the optimization process. The utility of the proposed controller is evaluated by both the numerical simulation and experimental testing of the obstacle avoidance maneuver of the micro-unmanned vehicle. Results show that the proposed real-time optimal controller successfully performs obstacle avoidance.

Keywords

• optimal control
• deep learning
• nonlinear feedback
• model predictive control
• tracking control
• micro ground vehicle
• lane change