ND-NCD: Environmental Characteristics Recognition and Novelty Detection for Mobile Robots Control and Navigation

Mobile robot applications usually perform a path planning and its execution considers a previous known map. On the other hand, some application must explore the environment, defining a path from a source to a destination point, without knowing the environment map. The environment exploration, path planning towards a goal and navigation control tasks should be done at the same time. This study proposes a new method for mobile robot control and navigation based on the environmental characteristics recognition and novelty detection, named ND-NCD (Novelty Detection with Normalized Compression Distance). This method can be used as a key component in environment exploration and topological mapping tasks. In a previous work, a Genetic Algorithm (GA) for exploratory path planning was implemented to create a topological map (graph) from the source to the destination point, generating a set of actions which the robot must perform to achieve the goal. Each action was associated to a different reactive behavior specifically designed for characteristic places of the environment, such as corridors, curves or intersections. The proposed method, ND-NCD is used to recognize such different environmental characteristics, allowing to activate/associate the adequate actions whenever the method recognizes a context change (new context). This allows us to integrate the GA based environment exploration method together with the robot control reactive behaviors, which can be properly selected and switched according to the environmental characteristics detected/discovered by the ND-NCD. The ND-NCD uses the robot perception (e.g. laser sensor) to detect novelty and to recognize already known characteristics, thus allowing an incremental representation of the environment structures. The experiments were performed in the Player/Stage simulator and in a real indoor environment. ND-NCD performance is compared with a Neural Network trained to recognize context changes in the same environment. The results indicate that ND-NCD is a promising approach to be used in exploration and navigation control for mobile robots with the advantage of detecting a context change just knowing an initial state (corridor) from the environment. The proposed method does not need to be trained previously in order to know all the states (supervised training), being able to incrementally discover the different environment configurations.