DRL Warehouse Robot Path Planning — 2026 Landscape
DRL Warehouse Robot Path Planning 2026
Deep reinforcement learning applied to warehouse robot path planning spans DQN, SAC, and multi-agent CTDE architectures. This dataset covers at least 8 formal patents and 50+ literature records from 2017 to 2026.
DRL Path Planning: From Navigation Baselines to Fleet-Level Control
Deep reinforcement learning for warehouse robot path planning applies neural networks — including DQN, Dueling Double DQN, SAC, DDPG, TD3, and PPO — within Markov Decision Process frameworks. Robots learn optimal navigation policies through environmental interaction, enabling autonomous obstacle avoidance and task execution across dynamic aisle environments.
The technology encompasses single-robot navigation, multi-robot fleet coordination, pick-and-place task learning, and dynamic storage location assignment. Sensor modalities across the dataset include LiDAR, RGB cameras, depth cameras, and IMU fusion. Key sub-domains include mapless navigation, hierarchical planning, curriculum-based training, and sim-to-real transfer.
Foundational constructs visible across the dataset include gridded environment representations, reward shaping strategies for sparse environments, prioritized and hindsight experience replay buffers, and centralized training with decentralized execution (CTDE) for multi-agent warehouse settings. These components form the engineering substrate for industrial deployment.
In retrieved records, Chinese research institutions account for 5 of 8 formal patents, with Harbin Institute of Technology Shenzhen and Anhui University each holding 2 patents. US and Korean entities — including Google, Samsung, Ford, and Korea University of Technology and Education — provide the counterbalance with active and pending filings in this dataset.
DRL Algorithm Clusters and Filing Timeline — Dataset Signals
The dataset reveals four dominant algorithm clusters — value-based DQN architectures, continuous-action policy gradient methods, hierarchical hybrid planners, and multi-agent CTDE systems — with filing and publication activity peaking in the 2022–2023 period.
Patent Count by Technology Cluster — DRL Path Planning (Dataset Snapshot)
In this dataset, value-based DQN architectures and hierarchical hybrid DRL-classical planners each account for the largest patent cluster shares, with CTDE multi-agent and continuous policy gradient methods representing the remaining filings.
↗ Click bars to exploreDRL Warehouse Robot Patent Filings by Year — Dataset Timeline
In this dataset, formal patent filing activity rises sharply from 2021 onward, reaching a peak cluster in 2022–2023, with continued activity through 2025–2026 from Samsung, Korea University of Technology and Education, and Shanghai Jiao Tong University.
↗ Click bars to exploreKey DRL Path Planning Application Domains Across Warehouse and Industrial Contexts
The dataset identifies four primary application domains — automated warehouse operations, industrial assembly lines, AGV navigation, and agricultural robotics — with intralogistics and warehouse automation accounting for 6 of 8 retrieved patents and a majority of literature records.
Automated Warehouse Operations
The most densely cited application domain in this dataset, covering AGV path optimization, multi-robot pick-and-delivery coordination, and dynamic storage location assignment. A 2022 study demonstrated a 6.3% reduction in transportation costs versus manual ABC-classification using a DRL agent trained on one year of historical warehouse data. Harbin Institute of Technology Shenzhen’s CN patents (2021, 2023) directly target multi-robot warehouse grid navigation using Dueling Double DQN with GRU and curriculum learning.
IntralogisticsIndustrial Manufacturing Assembly Lines
DRL-based path planning extends to robotic assembly, where manipulators plan collision-free trajectories through constrained workspaces. A 2022 literature result formalizes vehicle assembly line control as a parallel DRL problem minimizing cycle time across task-resource-workstation mappings. Korea University of Technology and Education’s 2025 US patents apply curriculum-based DRL to arm motion planning with difficulty-tiered target groupings in simulation.
Advanced ManufacturingAutonomous AGV Navigation Systems
Several dataset results address autonomous vehicle path planning in dynamic unknown environments, directly mapping to large distribution center AGV deployments. Google LLC’s 2024 EP patent covers end-to-end DRL navigation using DDPG with 1D LiDAR depth data across simulated and real robot navigation episodes. The 2023 APF-D3QNPER literature result explicitly frames warehouse AGV navigation as the motivating problem for its fused Artificial Potential Field and Dueling Double DQN algorithm.
AGV NavigationAgricultural and Specialized Robotics
A smaller cluster applies DRL path planning to agricultural harvesting robots, technically adjacent to warehouse manipulation due to shared manipulation challenges. A 2022 result applies TD3 with automatic goal generation to solve inverse kinematics for a series-parallel hybrid banana-harvesting robot arm, with techniques described as directly transferable to warehouse picking arms. This cluster illustrates the cross-domain applicability of warehouse-derived DRL manipulation methods.
Specialized RoboticsLeading Assignees in DRL Warehouse Robot Path Planning — Dataset Snapshot
In retrieved records, Chinese research institutions account for 5 of 8 formal patents, with Harbin Institute of Technology Shenzhen holding 2 CN patents on multi-robot warehouse DQN architectures and Anhui University holding 2 CN patents on target-network-free DRL path planning in this dataset. US corporate filers Google and Samsung represent the most recent active patent activity.
Top Assignees by Filing Count — DRL Path Planning in Retrieved Records
↗ Click bars to exploreHarbin Inst. Technology Shenzhen
Harbin Institute of Technology Shenzhen holds 2 CN patents (filed July 2021 and 2023) focused on multi-robot warehouse path planning in this dataset. Both patents combine Dueling Double DQN with Gated Recurrent Units (GRU) and use sub-goal waypoints set by regional congestion levels to assist multi-robot exploration. The 2023 patent extends this architecture with curriculum learning for implicit multi-robot cooperation in warehouse grid environments.
China — CNAnhui University
Anhui University holds 2 CN patents (both filed 2023) covering a target-network-free robot path planning method based on deep reinforcement learning in this dataset. The approach applies Dueling DQN with priority experience replay, eliminating the target network component to reduce training complexity. Both patents are active CN filings targeting robot navigation without the traditional target-network update mechanism.
China — CNFrontier Signals in DRL Warehouse Path Planning (2023–2026 Dataset)
Filings and publications from 2023 to 2026 in this dataset reveal five converging directions: curriculum learning standardization, DRL-based parameter tuning for classical planners, fleet-level actor-critic systems, LSTM-augmented dynamic environment handling, and maturing sim-to-real transfer infrastructure.
Curriculum Learning Transitioning to Standard Engineering Component
Multiple results from 2022–2025 integrate automatic curriculum learning (ACL) to manage training complexity. The 2022 intralogistics mapless navigation study uses NavACL-Q for distributed SAC training with dual LiDAR and RGB camera on an AGV validated in NVIDIA Isaac Sim. Korea University of Technology and Education’s 2025 US patents explicitly formalize difficulty-tiered curriculum groupings for robot arm motion planning, signaling ACL’s transition from research technique to expected engineering component.
DRL as Parameter Optimizer for Classical Path Planners
Shanghai Jiao Tong University’s 2026 US patent introduces a new category: using a DRL network not to replace classical planners but to dynamically tune their parameters — specifically step size and steering angle for a Reeds-Shepp curve-based path planner — in real time. The patent includes obstacle regional modeling and reward function construction for loading and parking path generation. This hybrid meta-optimization approach may prove more deployment-friendly than full end-to-end DRL navigation.
Value-Based DQN vs. Continuous Policy Gradient Methods for Warehouse Path Planning
Click any row to explore further.
| Dimension | Value-Based DQN Architectures | Continuous Policy Gradient Methods |
|---|---|---|
| Primary Algorithms | DQN, Dueling Double DQN (D3QN), D3QN + PER, DQN + GRU/LSTM | SAC, DDPG, TD3, PPO, A3C |
| Action Space | Discrete — move forward, turn, wait actions on gridded environments | Continuous — smooth velocity and arm trajectory control |
| Primary Warehouse Use Case | Multi-robot AGV fleet navigation in aisle grid networks | Pick-and-place manipulation, precise mobile base positioning |
| Representative Patent/Result | Harbin Institute of Technology Shenzhen CN patents (2021, 2023) — Dueling Double DQN + GRU with congestion-level sub-goal waypoints | AgileSoda Inc. US patent (2023) — DRL pick-and-place; 2022 intralogistics study — distributed SAC with NavACL-Q on dual LiDAR AGV |
| Training Enhancement | Prioritized Experience Replay (PER), Hindsight Experience Replay (HER), curriculum learning, GRU for temporal memory | Automatic Curriculum Learning (ACL/NavACL-Q), DDPG arm feedback loops, PPO policy clipping |
| Sensor Modalities | Gridded occupancy maps, obstacle and robot position states | LiDAR (1D and dual), RGB cameras, depth cameras, IMU fusion |
| Long-Horizon Navigation | Limited unaided — requires sub-goal waypoints or hybrid classical planner coupling | Limited unaided — DDPG combined with A* or PRM for long-range tasks (PRM-RL, 2018) |
| Sim-to-Real Validation | 2D simulation environments; Webots and Gazebo used across literature results | NVIDIA Isaac Sim (NavACL-Q 2022); robo-gym; aerial robot sim-to-real showing 38–50% success rate improvement |
Frequently Asked Questions — DRL Warehouse Robot Path Planning Patents
The most prevalent algorithms in retrieved records include Deep Q-Networks (DQN) and its variants — Dueling Double DQN (D3QN), D3QN with Prioritized Experience Replay, and DQN combined with GRU or LSTM recurrent units — for discrete navigation. Continuous-action methods including SAC, DDPG, TD3, and PPO appear in manipulation and precise navigation results. A3C appears in one CN patent from Shenyang Institute of Automation.
In retrieved records, Harbin Institute of Technology Shenzhen, Anhui University, and Korea University of Technology and Education each hold 2 patents. Google LLC (1 EP patent, 2024), Samsung Electronics (1 US patent, 2025), Ford Global Technologies (1 US patent, 2022), AgileSoda Inc. (1 US patent, 2023), and Shanghai Jiao Tong University (1 US patent, 2026) each hold one filing. This dataset contains 8 formal patents total.
CTDE stands for Centralized Training with Decentralized Execution. In this approach, agents share training information during the learning phase but execute their policies independently at deployment. According to the dataset, decentralized execution is essential for scalability in large warehouses where real-time centralized communication across a full robot fleet is impractical. It is described as the dominant approach for collision-free multi-robot navigation in retrieved results.
The Shanghai Jiao Tong University 2026 US patent is the latest filing date in this dataset. It covers a system and method for optimizing path exploration parameters — specifically step size and steering angle — for a Reeds-Shepp curve-based path planner using a DRL network. It includes obstacle regional modeling and reward function construction for loading and parking path generation. This represents a new category of DRL used to tune classical planner parameters rather than replace them entirely.
The 2022 intralogistics mapless navigation study validated a distributed SAC agent in NVIDIA Isaac Sim. A 2022 sim-to-real result for aerial robots demonstrated 38–50% success rate improvements over baseline DRL. Simulation environments referenced across the dataset include NVIDIA Isaac Sim, Webots, Gazebo, and robo-gym. The dataset characterizes sim-to-real transfer infrastructure as maturing into a deployable engineering practice rather than a research exercise.
According to the dataset, pure DRL approaches without classical planner coupling appear commercially limited. Every industrial-deployment-oriented result in the dataset — including patents and literature from Ford, Google, Arena-Rosnav, and Shanghai Jiao Tong University — integrates DRL with classical planners such as A*, PRM, ORCA, or parametric Reeds-Shepp planners. The dataset recommends that IP strategists evaluate hybrid architectures as the primary freedom-to-operate landscape.
Data and insights on this page are based on a limited patent and literature dataset and are for reference only. Figures may not represent the complete technology landscape.