& This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.Target collection using autonomous robots can be broadly applied to a variety of fields such as product transferring in manufacturing factory, rubbish cleaning in office, and bomb searching on battle field, etc. Such robots should be able to cope with the large amount of uncertainties existing in physical environment which is often dynamic and unpredictable. The task requires the robot to achieve certain goals without bumping into obstacles based on sensor readings. However, the goal-based world model is often difficult to be precisely predefined and sometimes the obstacles are not static. Moreover, the sensor information is imprecise and unreliable in most cases. Therefore, the autonomous robot controller needs to be endowed with the