Model Predictive Path Planning for an Autonomous Ground Vehicle in Rough Terrain

An autonomous ground vehicle (AGV) in rough terrain typically experiences uncertain environment. Because the uncertainty makes overall performance of autonomous navigation degrade, the AGV requires a suitable path to maintain or improve the performance against the uncertainty. In order to handle this problem, this study proposes a model predictive path planning algorithm by employing a passivity-based model predictive control (MPC) optimization setup. The model predictive path planning method is formulated as a finite optimization problem with an objective function and several constraints. In the cost function, environment perception result about the AGV’s own neighborhood is included and the only traversable region has low cost value. To reflect dynamic characteristics of the AGV, the proposed method utilizes dynamic and kinematic models of the AGV as equality constraints and limited range of states and control input as inequality constraints. In addition, the stability of the path planning method is improved by a passivity constraint. The solution of the optimization problem is obtained using the particle swarm optimization (PSO) method. Finally, field tests are conducted to validate the performance of the proposed algorithm, and satisfactory results of the autonomous navigation were obtained.