AGV Motion Planning Patents: Who Leads, Where the Gaps Are 2026
- Filing has cooled since its 2020 peak. 105 families filed that year against 19 in the most recent (partial) year — this is a maturing claim space, not a growing one.
- China dominates the receiving-office split. 647 of 836 families were filed there, versus 83 in the United States and 30 at the EPO — this is where the prior art is thickest.
- Control and navigation classes carry the weight. G05D and G01C together cover the bulk of records, while AI-based computing (G06N) sits at only 45 — a comparatively thin overlay on a mechanically-defined field.
What this landscape covers
This dataset tracks 836 patent families published between 2015 and mid-2026 that combine automated guided vehicle (AGV) terminology with motion-planning concepts — path planning, trajectory optimization, and obstacle avoidance — and sit inside the core navigation and control IPC classes. It is a claim space defined more by control engineering than by machine learning: the classification split shows G05D and G01C carrying most of the volume, with AI-flagged filings (G06N) a small minority.
Filing activity rose through the late 2010s, peaked in 2020, and has since declined — a pattern consistent with a field where the core mechanisms of path planning and obstacle avoidance have already been claimed by early movers, and later filers are working around a denser prior-art base rather than opening new ground. Publication lags filing by roughly 18 months, so the most recent year understates real activity, but the multi-year downward slope from the 2020 peak is not an artefact of that lag alone.
Filing trends and technology composition
Two views of the same 836 families: how filing volume has moved year over year, and how the subject matter splits across IPC subclasses.
A peak-then-decline filing curve
Filings climbed from 43 in 2017 to a peak of 105 in 2020, held near that level through 2022 (90), and have fallen since — a shape typical of a technology whose foundational claims are largely staked out, leaving later entrants to file narrower, defensive, or design-around applications.
Control and positioning classes dominate
G05D (control of non-electric variables) and G01C (navigation and gyroscopes) together account for the majority of records, confirming this is fundamentally a vehicle-control and localization field. Business-process overlays (G06Q), positioning/radar (G01S), and AI-based computing (G06N) appear as secondary layers rather than the core of most claims.
Shares are the percentage of the 836 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Automated Guided Vehicle Motion Planning with Eureka
This page is one run against one query. Ask Eureka your own question about automated guided vehicle motion planning and every answer comes back with the patent numbers behind it.
Try EurekaThe most-cited prior art in this space
US20240045444A1 — Automated guided vehicle management system and method
An AGV management system pairing a battery recharge management module, a task management module, and an AGV path planning module: the recharge module governs wireless charging in a parking area and ensures departing vehicles exceed a charge threshold, the task module assigns pick-up, drop-off and due-time information, and the path-planning module routes vehicles according to those assigned tasks.Filed by Delta Electronics Int'l (Singapore) Pte Ltd, published 2024-02-08.
| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US6643576B1 | Rapid adjustment of trajectories for land vehicles | 173 |
| 2 | US20180072212A1 | Free ranging automated guided vehicle and operational system | 159 |
| 3 | CN105607635A | 自动导引车全景光学视觉导航控制系统及全向自动导引车 | 152 |
| 4 | US5280431A | Method for controlling the movements of a mobile robot in a multiple node factory | 152 |
| 5 | US20160266578A1 | Automated guided vehicle system | 148 |
| 6 | JP2004280213A | Distributed path planning device and method, and distributed path planning program | 143 |
| 7 | CN107036618A | 一种基于最短路径深度优化算法的AGV路径规划方法 | 120 |
| 8 | US4940925A | Closed-loop navigation system for mobile robots | 115 |
| 9 | CN106647734A | 自动导引车、路径规划方法与装置 | 108 |
| 10 | CN107990903A | 一种基于改进A*算法的室内AGV路径规划方法 | 102 |
Citation counts reflect influence within the searched corpus and skew toward older filings; they are not a measure of current commercial relevance.
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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Browse MCP servers →What the numbers mean for a filing decision
Three signals worth weighing before drafting claims in this space: where the volume sits, how concentrated the field is, and what jurisdiction actually matters.
The core mechanisms are already claimed
A peak in 2020 followed by a sustained decline means the foundational path-planning and obstacle-avoidance approaches have largely been staked out. New filings now need to differentiate against a dense base rather than claim open territory.
China is the de facto prior-art baseline
With 647 of 836 families filed through the Chinese receiving office against 83 in the US and 30 at the EPO, any freedom-to-operate check that skips Chinese filings is checking less than half the relevant art.
AI overlays are a minority, not the mainstream
Only 45 records carry an AI-computing classification against 595 in core control (G05D) and 307 in navigation (G01C). Claims built around a learned planner still face a mostly rule-based, classically-controlled prior art landscape.
Joint filing is rare and mostly institutional
The strongest co-filing links pair a commercial assignee with individual inventors or a university with its own affiliated research institute, rather than cross-company alliances — collaboration here is thin and mostly internal.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to automated guided vehicle motion planning, with the prior art for and against each one.
Who is active, and where momentum has stalled
Assignee activity spans commercial AGV makers, universities, and a handful of research institutes; recent-year momentum has flattened across nearly all of the tracked filers.
A long tail behind a modest lead group
The ranking mixes commercial AGV and robotics firms with Chinese universities and affiliated research institutes, several of which co-file with their own spin-off institutes rather than external partners.
Momentum has broadly flattened
Several previously active filers, including university-affiliated groups, show zero filings in the latest tracked year and year-over-year declines of -100% where prior-year activity existed — consistent with the field-wide filing slowdown after 2020.
Collaboration clusters around individual inventors
The strongest co-assignee links pair a single corporate filer with named individual inventors rather than another company, suggesting most cross-listed filings are internal inventor credit rather than joint-venture IP.
| Assignee | Recent year | YoY |
|---|---|---|
| Vibrolo Co., Ltd. | 0 | — |
| Boomerang Systems, Inc. | 0 | — |
| Hefei University of Technology | 0 | -100% |
| Standard Robots (Anhui) Co., Ltd. | 0 | — |
| Texas Instruments Incorporated | 0 | — |
| Nanjing University of Science and Technology | 0 | -100% |
| Wuhu Hart Robot Industry Technology Research Institute Co., Ltd. | 0 | — |
| Hangzhou Dianzi University | 0 | — |
Where to take this analysis
The landscape points to a field with occupied core claims and a few thinner branches. Two directions are worth pursuing next.
Run a freedom-to-operate check against the Chinese filing base
With 647 of 836 families filed in China, any product heading to that market — or sourcing components from AGV makers filing there — needs claims checked against that corpus specifically, not just the US and EPO subset.
Explore FTO workflows in EurekaMap claim scope in the under-claimed branches
Fleet-level deconfliction, charging-aware replanning, and GPS-denied localization show thinner overlap with the dominant control-class filings. A claim drafted around one of these is more likely to clear a novelty search than one targeting core trajectory optimization.
Draft claims with EurekaCommon questions about this landscape
This dataset tracks 836 patent families published between 2015 and mid-2026 that combine automated guided vehicle terminology with motion-planning concepts such as path planning, trajectory optimization, and obstacle avoidance. Families are counted rather than raw document publications because families neutralise duplicate filings across jurisdictions and continuation applications, giving a fairer count of distinct inventions. The true figure for the most recent year is understated because publication typically lags filing by around 18 months.
No — filing peaked at 105 families in 2020 and has declined since, with the most recent tracked year showing only 19. The midpoint year, 2022, sat at 90, confirming the decline is a multi-year trend rather than a single-year dip. This pattern typically indicates that the foundational technical approaches have already been claimed, and later filers are narrowing their claims around a denser prior-art base.
China accounts for 647 of the 836 tracked families, far ahead of the United States (83), the European Patent Office (30), India (25), the WIPO PCT route (23), and Japan (6). Any competitive or freedom-to-operate analysis in this field that does not include Chinese-language filings is missing the majority of the relevant prior art. This concentration also means Chinese assignees, including universities and research institutes, make up a large share of the active filer base.
Not as the dominant classification — only 45 of the 836 records carry a G06N (AI-based computing) classification, compared with 595 in G05D (control of non-electric variables) and 307 in G01C (navigation and positioning). This suggests that most claimed AGV motion-planning approaches are still built on classical control and sensor-based navigation techniques rather than learned models. A claim built primarily around a machine-learning planner would face less classification overlap but should still be checked against the core control-class prior art.
US20240045444A1, filed by Delta Electronics Int'l (Singapore) Pte Ltd and published in February 2024, claims an AGV management system that couples battery recharge management, task assignment, and path planning into one control loop — vehicles are only released from a wireless-charging parking area above a charge threshold, and paths are planned according to assigned pick-up, drop-off, and due-time tasks. It matters because it links charging state directly into path-planning logic, a combination that sits in one of the thinner, less-claimed branches of this landscape. Anyone designing a fleet-management or charging-aware routing system should review its claim scope directly rather than assume it only covers basic path planning.
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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.