Adaptive navigation of autonomous vehicles using evolutionary algorithms

Autonomous vehicles must be able to navigate freely in a constrained and unknown environment while performing a desired task. To increase its autonomy, a vehicle must be provided by sophisticated software navigators. Traditionally, navigators build a convenient model of the vehicle's environment and plan feasible paths by reasoning about what actions must be performed to control the vehicle in that environment. This paper presents a genetic algorithm for adaptive navigation of a robot-like simulated vehicle. The proposed algorithm evolves feasible paths by performing an adaptive search on populations of candidate actions. The performance of the algorithm is demonstrated on problems with vehicles moving in two-dimensional grids and compared with that of a simple greedy algorithm and a random search technique.