Operant conditioning model in autonomous navigation

To solve the navigation problem for mobile robots, we present a model based on the operant conditioning mechanism (OCM). 8 elements consist of the model, including state set, action set, learning mechanism and system entropy etc. As the core of the model, the learning mechanism is in accordance with operant conditioning principles, which makes agents learn the actions with reward and avoid the actions without reward. We test the model´s function in several ways and change the simulation platform and environment map. The results in both experiments show that the proposed model is effective.