A Nonlinear Model Predictive Control Algorithm for Obstacle Avoidance in Autonomous Ground Vehicles within Unknown Environments

A nonlinear model predictive control algorithm is developed for obstacle avoidance in high-speed, large-size autonomous ground vehicles (AGVs) that perceive the environment only through information provided by on-board sensors. The mission of the AGV is to move from its initial configuration to the goal configuration safely. The resulting trajectory should be collision-free and the AGV should be dynamically safe. As a starting point, the scenario where the vehicle moves on a flat surface at a constant speed is considered. The nonlinear MPC algorithm generates steering commands for completing the mission while enforcing safety constraints. The first safety constraint is avoiding obstacles. This is fulfilled by constraining the position of the AGV inside a safe region established from sensor data. The second safety constraint is ensuring dynamical safety. This is translated into avoiding single tire lift-off, which is implemented by limiting the steering angle within a range obtained using a 14 DoF vehicle dynamics model. At each sampling time, at least one multi-phase optimal control problem (OCP) is formulated and solved on-line. The safe region is partitioned into multiple sub-regions, which can then be specified without using piecewise functions. The fact that the optimal trajectory traverses the sub-regions sequentially and hence the position constraints are different from phase to phase makes the OCP multi-phase. The multi-phase OCP is transcribed into a nonlinear programming problem using the hp-pseudospectral method, and solved using the interior-point method. Simulations of an AGV approaching multiple obstacles show the effectiveness of the proposed algorithm.