https://github.com/autonomousrobots2026/modular_drl_scheduler References [1] K. Zhu, T. Zhang, “Deep RL for mobile robots in cluttered environments,” Autonomous Robots , vol. 46, pp. 345–360, 2022. [2] J. Schulman et al., “Proximal policy optimization,” arXiv:1707.06347 , 2017. [3] M. Quigley et al., “ROS: an open-source robot operating system,” ICRA workshop, 2009. [4] S. Thrun et al., Probabilistic Robotics , MIT Press, 2005. [5] L. Chen, “Graph-based task allocation for multi-robot systems,” IEEE T-RO , vol. 39, no. 2, pp. 891–907, 2023. LetPub notation: This paper is a simulated example for illustrative purposes. No actual submission to Autonomous Robots has occurred. For real author guidelines, see https://www.springer.com/journal/10514.
Autonomous Robots (Springer) Status: Submitted – Under Review (LetPub ID: AUTO-2026-0417) Abstract The deployment of autonomous robots in unstructured environments—such as disaster zones, dense forests, or planetary surfaces—requires robust navigation and real-time task allocation under uncertainty. This paper presents a novel modular framework that integrates deep reinforcement learning (DRL) with a dynamic graph-based task scheduler. Unlike end-to-end policies, our system separates perception (LiDAR + RGB), local path planning (SAC algorithm), and global task allocation (Hungarian algorithm with receding horizon). Experiments in both simulation (Habitat 2.0, Gazebo) and physical trials (Clearpath Jackal robots) show a 34% improvement in task completion rate and a 41% reduction in collision frequency compared to baseline DRL methods. Ablation studies confirm the modular design’s generalizability across unseen obstacle densities. We release the code and simulation environment for reproducibility. autonomous robots letpub
L. Chen¹, M. Kowalski², S. Patel¹ ¹Department of Robotics, Tsinghua University, Beijing, China ²Institute of Autonomous Systems, Warsaw University of Technology, Poland https://github
Autonomous robots · Deep reinforcement learning · Task allocation · Modular navigation · Unstructured environments 1. Introduction Autonomous robots have transitioned from controlled laboratories to real-world applications: search and rescue, precision agriculture, and underground mining. However, three fundamental challenges persist: (i) partial observability in dynamic environments, (ii) coupling between low-level control and high-level mission planning, and (iii) sample inefficiency of monolithic learning approaches. 46, pp
Recent works (e.g., [1,2]) have applied end-to-end DRL to mobile robots, but they often fail when task objectives change (e.g., from “go to point A” to “inspect three zones”). Conversely, classical SLAM + planning pipelines are brittle under perceptual aliasing.