Creation and analysis of a scenario based universal sensory driver layer with real-time fault tolerant properties

Sensor fusion and sensor integration is becoming an increasingly popular approach in dealing with complex sensor systems in autonomous mobile robots (AMR). However, the procedure for the sensor integration and sensor fusion is a non-trivial process. This paper presents a scenario based approach to sensor fusion based on the Autonomous Evolution of sensory and actuator Driver layers through Environmental Constraints (AEDEC) [1]. Using the scenario based approach, the programmer’s work of creating a sensory driver will be eliminated by having the AMR learn the driver on is own. In the process of creating each scenario, sensor fusion is automatically implemented. If sensors change or even if the sensor configuration changes, the driver can be updated by having the AMR relearn the driver over again. Due to the tabular structure of the scenario based sensory drivers, malfunctioning sensors can not only be detected, but the driver can automatically adapt to the malfunctioning sensor in real-time. Furthermore, different AMRs trained using AEDEC architecture will have similar interpretations of its environment. This is guaranteed by having the AMR learn the driver in the same highly structured training environment. The behavioral coding is simplified by eliminating any reference to hardware dependent parameters. Finally, the level of abstraction and the consistency of the highly structured environment will allow for code portability.