Adaptive navigation for autonomous robots

In many robotic exploration missions, robots have to learn specific policies that allow them to: (i) select high level goals (e.g., identify specific destinations), (ii) navigate (reach those destinations), (iii) and adapt to their environment (e.g., modify their behavior based on changing environmental conditions). Furthermore, those policies must be robust to signal noise or unexpected situations, scalable to more complex environments, and account for the physical limitations of the robots (e.g., limited battery power and computational power).In this paper we evaluate reactive and learning navigation algorithms for exploration robots that must avoid obstacles and reach specific destinations in limited time and with limited observations. Our results show that neuro-evolutionary algorithms with well-designed evaluation functions can produce up to 50% better performance than reactive algorithms in complex domains where the robot’s goals are to select paths that lead to seek specific destinations while avoiding obstacles, particularly when facing significant sensor and actuator signal noise.

Research highlights
► Unique state and action spaces are developed to provide reactionary environment interpretation without the need of localization.
► Neuro-evolution policy search is used to map states to actions with compact and efficient artificial neural network function approximation.
► A rule-based navigation algorithm based on known robot capabilities and empirical observations is used as comparative baseline.
► Neuro-evolution produces successful, adaptive navigation behavior robust to sensor and actuator signal noise.
► The learned policy is taken from simulation and tested in real robot hardware to ensure the policy is abstract enough to be applicable in the real-world.