We present the integration of a Non-Axiomatic Reasoning System (NARS) with mobile robots for planning and decision making. NARS enables robots to effectively handle uncertainty in real-time with complete sensor and actuator integration, thereby ensuring adaptability to evolving scenarios. We discuss essential parts of the logic, the architecture and working principles of NARS, and the integration of NARS as a ROS node. A case study is provided demonstrating the system’s proficiency to carry out a garbage collection task in an open-air environment by operating a mobile robot with manipulator arm, and we demonstrate its ability to learn about the place-dependent accumulation of garbage items. Case study also reveals that our approach performs more effectively on the overall task than the Belief-Desire-Intention model we compared with.

Stigmergy is a mechanism that allows the coordination of actions within the same agent or across different agents by means of traces left in the environment. We propose a stigmergetic approach to robot navigation in which a robot sets values in a hexagonal grid of RFID tags buried under the floor. This approach only requires minimal resources on the robot. The RFID floor will eventually contain a distance map that can guide the robot to a given goal (or set of goals) without the use of any localization system. The same map can be used or improved by other robots or by the same robot at later times. We define algorithms for building the RFID-floor map and for navigating on this map, we prove the convergence of the map building algorithm, and we show an empirical validation of our results using a small robot in a domestic environment.