# Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments

arXiv: https://arxiv.org/abs/2409.14972 · HTML: https://mlsanity.com/paper/2409.14972/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 20 of its 58 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 20 of 20)

- [Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning](https://mlsanity.com/paper/1609.07845/) (2016)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [Learning Navigation Behaviors End-to-End with AutoRL](https://mlsanity.com/paper/1809.10124/) (2018)
- [MeGA: Hybrid Mesh-Gaussian Head Avatar for High-Fidelity Rendering and Head Editing](https://mlsanity.com/paper/2404.19026/) (2024)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset](https://mlsanity.com/paper/2406.13113/) (2024)
- [Research on Autonomous Driving Decision-making Strategies based Deep Reinforcement Learning](https://mlsanity.com/paper/2408.03084/) (2024)
- [Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning](https://mlsanity.com/paper/2408.16068/) (2024)
- [Systematic Rectification of Language Models via Dead-end Analysis](https://mlsanity.com/paper/2302.14003/) (2023)
- [Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation](https://mlsanity.com/paper/2409.09324/) (2024)
- [Twitter Sentiment Analysis of Covid Vacciness](https://mlsanity.com/paper/2311.04479/) (2023)
- [Machine Learning-Based Research on the Adaptability of Adolescents to Online Education](https://mlsanity.com/paper/2408.16849/) (2024)
- [Optimizing Distributed Networking with Big Data Scheduling and Cloud Computing](https://mlsanity.com/paper/2311.04482/) (2023)
- [Rhyme-aware Chinese lyric generator based on GPT](https://mlsanity.com/paper/2408.10130/) (2024)
- [Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm](https://mlsanity.com/paper/2408.12672/) (2024)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)
- [Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields](https://mlsanity.com/paper/2406.18311/) (2024)
- [Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC](https://mlsanity.com/paper/2407.12887/) (2024)
- [Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection](https://mlsanity.com/paper/2406.06558/) (2024)
- [Learning low-dimensional state embeddings and metastable clusters from time series data](https://mlsanity.com/paper/1906.00302/) (2019)

## Built on by (most cited first, 17 of 17)

- [Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies](https://mlsanity.com/paper/2410.21170/) (2024)
- [Data Augmentation Through Random Style Replacement](https://mlsanity.com/paper/2504.10563/) (2025)
- [EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition](https://mlsanity.com/paper/2410.09954/) (2024)
- [Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning](https://mlsanity.com/paper/2410.19769/) (2024)
- [LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting](https://mlsanity.com/paper/2410.15286/) (2024)
- [In-Trajectory Inverse Reinforcement Learning: Learn Incrementally Before An Ongoing Trajectory Terminates](https://mlsanity.com/paper/2410.15612/) (2024)
- [Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm](https://mlsanity.com/paper/2411.06720/) (2024)
- [IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose](https://mlsanity.com/paper/2411.12676/) (2024)
- [Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation](https://mlsanity.com/paper/2501.02414/) (2025)
- [Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method](https://mlsanity.com/paper/2504.07983/) (2025)
- [TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model](https://mlsanity.com/paper/2410.15283/) (2024)
- [GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction](https://mlsanity.com/paper/2411.06725/) (2024)
- [Optimized CNNs for Rapid 3D Point Cloud Object Recognition](https://mlsanity.com/paper/2412.02855/) (2024)
- [Performance Review on LLM for solving leetcode problems](https://mlsanity.com/paper/2502.15770/) (2025)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection](https://mlsanity.com/paper/2411.19071/) (2024)
- [Construction and optimization of health behavior prediction model for the elderly in smart elderly care](https://mlsanity.com/paper/2412.02062/) (2024)

## Abstract (arXiv)

At present, in most warehouse environments, the accumulation of goods is complex, and the management personnel in the control of goods at the same time with the warehouse mobile robot trajectory interaction, the traditional mobile robot can not be very good on the goods and pedestrians to feed back the correct obstacle avoidance strategy, in order to control the mobile robot in the warehouse environment efficiently and friendly to complete the obstacle avoidance task, this paper proposes a deep reinforcement learning based on the warehouse environment, the mobile robot obstacle avoidance Algorithm. Firstly, for the insufficient learning ability of the value function network in the deep reinforcement learning algorithm, the value function network is improved based on the pedestrian interaction, the interaction information between pedestrians is extracted through the pedestrian angle grid, and the temporal features of individual pedestrians are extracted through the attention mechanism, so that we can learn to obtain the relative importance of the current state and the historical trajectory state as well as the joint impact on the robot's obstacle avoidance strategy, which provides an opportunity for the learning of multi-layer perceptual machines afterwards. Secondly, the reward function of reinforcement learning is designed based on the spatial behaviour of pedestrians, and the robot is punished for the state where the angle changes too much, so as to achieve the requirement of comfortable obstacle avoidance; Finally, the feasibility and effectiveness of the deep reinforcement learning-based mobile robot obstacle avoidance algorithm in the warehouse environment in the complex environment of the warehouse are verified through simulation experiments.