## 1 Introduction
This is primarily because mobile robots in these environments are required to complete tasks within strict time constraints, making constant re-planning computationally infeasible. Reinforcement Learning (RL) [17,18] has been widely adopted for modeling complex environmental dynamics and learning optimal control policies through interaction. Numerous Deep Reinforcement Learning (DRL) methods [19,20] have been developed for planning in dynamic environments. [PERSON] et al. [21] proposed a hierarchical Deep Learning (DL)-based control framework to plan optimal maneuver trajectories and guide the robot with uncertainties. However, their proposed motion planning model was trained offline and the learning cost was expensive. In our previous work [22], we proposed an FSA-MDP controller by integrating scLTL with a Markov Decision Process (MDP) to plan a global path for the mobile robot considering the uncertainties of jamming attackers. However, this approach still relies on real-time re-planning mechanisms when environmental changes occur. Recent studies have demonstrated that addressing uncertainties at the planning level is not only effective but also more practical for real-world deployment [13]. On the one hand, computational costs are significantly reduced, as continuous re-planning is no longer required. On the other, it helps to prevent the robot from becoming trapped in local optima, improving the overall task success rate. In human-shared environments, ensuring safe operation of mobile robots is both fundamental and critical. At the same time, achieving a high task success rate is essential for improving the efficiency of systems such as warehouses and automated factories. Motivated by these considerations and inspired by the principle of addressing uncertainties at the planning level, we propose a mobile robot controller solving the path planning and task controlling problems in human-shared environments. An RL-based double layer algorithm is proposed to plan a global safe path for a robot performing multiple pickup and delivery tasks in an environment with uncertainties. The key contributions of this study are summarized as follows:  
1. We estimate environmental uncertainties through stochastic simulations and propose a hierarchical mobile robot controller that integrates scLTL and RL. Unlike conventional approaches that rely on real-time obstacle avoidance, our method explicitly handles environmental uncertainties at the planning level. 2. A novel RL-based double-layer algorithm is developed. Unlike traditional product- or encoder-type algorithms that use scLTL and RL, the proposed algorithm separates the learning problem into high-level and low-level layers. This decomposition enables the robot to compute an optimal path in human-shared environments with significantly reduced learning costs. 3. At the higher level, an FSA-RL learner is proposed on the basis of FSA transitions to find an optimal global strategy for multiple tasks. Notably, we extend the deterministic state transitions of the FSA to a stochastic formulation, allowing the controller to better adapt to dynamic environments. 4. At the lower level, we construct an MDP learner that improves upon the classic MDP by redefining the reward function to explicitly account for environmental uncertainties at the planning level. This enhancement enables the generation of an optimal local path under dynamic and uncertain conditions. The rest of this paper is organized as follows: Section 2 presents a review of related works; Section 3 briefly reviews the scLTL specifications; Section 4 introduces the problem formulation; the proposed safe planner is presented in Section 5, followed by the simulation results and discussions in Section 6; finally, a brief conclusion is provided in Section 7.