Event-Triggered Model Predictive Adaptive Dynamic Programming for Road Intersection Path Planning of Unmanned Ground Vehicle

Autonomous driving of unmanned ground vehicle (UGV) at road intersection is a challenging task due to the complicated traffic conditions. In this paper, an event-triggered model predictive adaptive dynamic programming (MPADP) algorithm is proposed for path planning of UGV at road intersection. Following the critic-actor scheme of adaptive dynamic programming (ADP), cost function approximation and control policy generation are combined to formulate MPADP. The infinite horizon cost function of ADP is stacked over predictive horizon of model predictive control (MPC), and then the infinite horizon cost function is converted to the finite horizon-stacked cost function in MPADP. By minimizing the approximation error within predictive horizon, the approximation accuracy is enhanced. Considering the limitation of energy consumption, the event-triggered mechanism is designed based on the mismatch of cost function approximation. Three triggering conditions are designed, and the corresponding boundedness of approximation error is proved. Simulation results illustrate the effectiveness, efficiency and feasibility in application of the event-triggered MPADP method for path planning at road intersection.

Automated summary

An event-triggered MPADP algorithm is proposed in this paper for path planning of UGV at road intersection, enhancing approximation accuracy by minimizing the approximation error within predictive horizon. An event-triggered mechanism is designed based on the mismatch of cost function approximation, considering the limitation of energy consumption, with three triggering conditions and corresponding boundedness of approximation error.