Warehouse Robot Motion Planning Patents: Leaders & White Space 2026
- Filing has plateaued, not grown. the peak year (2022, 37 families) is also roughly the midpoint of the trend — activity since has not pushed past it.
- Control and manipulation dominate the claim space. G05D and B25J together cover more records than any other IPC pairing, while G06N-tagged AI-model filings sit at just 16.
- Ownership is fragmented outside a handful of pairings. only nine co-assignee pairs exist across 204 families, and the strongest link (Hyundai plus Kia, 15 records) is an automotive OEM tie, not a warehouse-robotics specialist.
What this dataset covers
This landscape covers 204 patent families published between 2015 and mid-2026 that combine warehouse, logistics or warehouse-automation robots with path planning, multi-robot coordination or traffic-aware routing. The search string is deliberately narrow: it isolates motion-planning claims from the much larger body of general warehouse-robotics filings, so counts here should be read as a slice of that field, not its whole.
Filing activity peaked in 2022 at 37 families and has not exceeded that level since, with the most recent year understated because publication typically lags filing by around eighteen months. Receiving-office data shows the United States and China as the two largest filing venues, with Europe, India, Singapore and the WIPO PCT route trailing behind.
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Filing trend and technology composition
Two views of the same 204-family dataset: how filing volume has moved year over year, and which IPC subclasses carry the claim density.
A flat trend line, not a growth curve
Annual filings rose from 8 in 2017 to a peak of 37 in 2022, then held roughly level through the most recent partial year at 8. Because 2022 sits near the midpoint of the coverage window, the shape reads as a plateau reached early rather than a technology still accelerating — though the final one to two years are always undercounted due to publication lag.
Control and manipulation lead; commerce logic is close behind
G05D (control of non-electric variables) and B25J (manipulators and robots) are the two largest subclasses, at 79 and 59 records respectively, confirming that most claims concern how a robot moves and manipulates rather than how a fleet is scheduled. G06Q (business/commerce data processing) at 46 and B65G (conveying and material handling) at 30 show scheduling and physical-handling logic are also well covered. G06N (AI-model computing) at 16 is comparatively thin given how central learned policies are to modern path planning — a signal worth checking against the white-space section below.
Shares are the percentage of the 204 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Warehouse Robot Motion Planning with Eureka
This page is one run against one query. Ask Eureka your own question about warehouse robot motion planning and every answer comes back with the patent numbers behind it.
Try EurekaA representative filing
Warehouse robot control method and apparatus, robot, and warehouse system
The filing (assigned to HAI ROBOTICS CO., LTD., published 2023-04-13) covers a method where a container scheduling instruction carries a container type, which determines which pose-recognition algorithm the robot applies before picking up the container. The claim structure ties container classification directly to the recognition-and-pickup pipeline rather than treating pose detection as a generic step.Full claim text and family members are available in Eureka.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20190364492A1 | Methods and devices for radio communications | 871 |
| 2 | WO2018125686A2 | Methods and devices for radio communications | 312 |
| 3 | US20200205062A1 | Methods and devices for radio communications | 144 |
| 4 | US20200302391A1 | Order processing method and device, server, and storage medium | 103 |
| 5 | US20200180647A1 | Neural network based modeling and simulation of non-stationary traffic objects for testing and development of… | 100 |
| 6 | US10390003B1 | Visual-inertial positional awareness for autonomous and non-autonomous device | 66 |
| 7 | US10192113B1 | Quadocular sensor design in autonomous platforms | 58 |
| 8 | US20220126445A1 | Machine learning model for task and motion planning | 56 |
| 9 | US11452032B2 | Methods and devices for radio communications | 54 |
| 10 | CN113031603A | 一种基于任务优先级的多物流机器人协同路径规划方法 | 51 |
Citation counts favour older records in any searched corpus; treat them as a signal of influence, not of current importance. Several of the top-cited entries here relate to radio communications methods rather than motion planning directly, reflecting how broadly the underlying assignees' portfolios span.
Patent titles are shown in the language they were filed in, not translated, so that each record stays verifiable against the original filing — a translated title will not match in Eureka or in any national register. Each row carries its publication number; clicking a row searches Eureka by that number.
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Three patterns stand out once the counts are read together: where claim density sits, how citation age skews the influence picture, and how concentrated ownership actually is.
Control-layer claims are the most contested
With 79 of 204 records in G05D and 59 in B25J, new filings aimed squarely at generic motion-control or manipulator claims are entering the densest part of the map. Differentiation is more likely to hold up where a claim ties control logic to a specific hardware or task constraint rather than describing planning in the abstract.
The most-cited records are not all motion-planning specific
The highest citation counts in this corpus belong to records with titles about radio communications methods, not warehouse path planning — a reminder that citation counts reward age and broad applicability within a searched corpus, not topical centrality. Reading the citation table as a ranking of importance to this specific field would be a mistake.
Collaboration is rare and mostly automotive
Only nine co-assignee pairings appear across the whole dataset, and the strongest is Hyundai paired with Kia at 15 records — an OEM relationship, not a warehouse-robotics partnership. Specialist robotics assignees in this corpus are filing largely alone, which lowers the odds of blocking thickets built by joint ownership.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to warehouse robot motion planning, with the prior art for and against each one.
Who is active, and where momentum is heading
Recent-year momentum figures should be read cautiously: the most recent year is partial, so year-over-year drops for several assignees below reflect a short filing window as much as a real pullback.
Automotive OEMs cooling off, not exiting
Hyundai and Kia each show one family in the latest year, down 50% year over year — consistent with a partial final year rather than a strategic retreat, especially given their strong co-filing history together.
Specialist robotics filers show a pause
HAI Robotics, Intel, Nvidia and Geek+ (Beijing) all register zero families in the latest year in this dataset, with Nvidia down 100% year over year. Given publication lag, this likely understates real filing activity more than it signals disengagement.
A long tail beyond the named leaders
Beyond the handful of assignees with multi-year momentum data, the dataset includes many single- or few-filing entities, from regional logistics firms to research institutes. That tail is where freedom-to-operate checks are most likely to surface an unexpected blocking claim.
| Assignee | Recent year | YoY |
|---|---|---|
| Hyundai Motor Company | 1 | -50% |
| Kia Motors Corporation | 1 | -50% |
| HAI Robotics Co., Ltd. | 0 | — |
| Intel Corporation | 0 | — |
| Nvidia Corporation | 0 | -100% |
| Beijing Geek+ Technology Co., Ltd. | 0 | — |
| Beijing Xiaomi Robot Technology Co., Ltd. | 0 | — |
| Siemens AG | 0 | — |
Where to take this next
The counts and rankings here are a starting point for two kinds of follow-up work: checking a specific claim against the corpus, and watching where filing shifts once the most recent year fills in.
Run a freedom-to-operate check
If a design uses container-type-conditioned pose recognition or traffic-aware re-routing logic, checking it against the specific claim language behind the representative record and its family is the next concrete step.
Explore this dataset in EurekaTrack the under-claimed branches
Learned-policy fleet routing and cross-fleet handoff protocols show thinner density than the core control subclasses — worth revisiting once the current partial year of filings settles.
Set up monitoring in EurekaCommon questions on this landscape
Ownership in this dataset is fragmented rather than concentrated behind one or two firms. The strongest documented relationship is between Hyundai and Kia, who co-file together at a higher rate than any other pair in the corpus, but most of the other named assignees, including specialist warehouse-robotics firms, file largely on their own. Because only nine co-assignee pairs exist across 204 families, joint-ownership blocking positions are the exception, not the rule, in this field.
The trend is flat rather than growing. Filings rose from 8 in 2017 to a peak of 37 in 2022, and volume in the most recent year sits back at 8 — with 2022 sitting near the midpoint of the coverage window rather than at its end. That shape suggests the field reached its current filing intensity early and has held there since, though the final one to two years are always undercounted because publication lags filing by around eighteen months.
G05D (control of non-electric variables) and B25J (manipulators and robots) carry the most records in this dataset, at 79 and 59 respectively, meaning most claims address how a robot moves and manipulates objects rather than how a fleet is scheduled. G06Q (business and commerce data processing) and B65G (conveying and material handling) are also well represented. AI-model computing under G06N is comparatively thin at 16 records, which is worth noting given how central learned planning policies are becoming in practice.
US20230114588A1, assigned to HAI Robotics and published 2023-04-13, covers a warehouse robot control method where a container scheduling instruction specifies a container type, and that type determines which pose-recognition algorithm the robot applies before picking the container up. The claim structure links container classification directly to the recognition-and-pickup sequence, rather than describing pose detection as a generic, container-agnostic step. Anyone building a pickup pipeline that selects a recognition method based on load type should review this family's full claim scope before finalising a design.
Based on IPC composition, learned-policy approaches to fleet routing sit at the thinner end of the map, with only 16 records tagged to AI-model computing (G06N) against 79 in general motion control. Dynamic traffic-aware re-routing under live congestion, cross-fleet task handoff protocols, and mixed human-robot aisle coordination also show less claim density than the core control and manipulator subclasses. These are reasonable areas to probe for a first-mover claim, though a full clearance search should confirm density before committing to a filing strategy.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.