The joint solution targets positioning reliability in urban canyons, tree-lined roads and other multipath-heavy environments where GNSS receivers commonly lose accuracy, and is delivered to Teseo devices as a firmware upgrade rather than requiring new hardware. FocalPoint said the software is built on its Supercorrelation technology and has shown accuracy improvements over standard commercial-grade solutions in recent global trials, with the partnership now advancing toward deployment on current and next-generation OEM platforms.

“Our joint solution addresses the GNSS reliability pain points experienced by many OEMs when architecting their ADAS solutions,” said Scott Pomerantz, CEO at FocalPoint.

STMicroelectronics said the agreement, paired with the open architecture of its Teseo platform, expands its product portfolio to help customers push beyond the limits of traditional GNSS receivers, with the two companies also working toward sub-meter accuracy through FocalPoint’s Precise+ technology. Evaluation kits integrating S-GNSS Auto on Teseo V and Teseo VI are now available to OEMs, Tier 1 suppliers and ecosystem partners.

“We’re delighted to enter this commercial agreement with ST in addition to becoming an ST Authorized Partner, taking our collaboration to the next level. Our joint solution addresses the GNSS reliability pain points experienced by many OEMs when architecting their ADAS solutions,” said Pomerantz. “Together, we deliver significant improvements that make autonomous and connected vehicles safer and more reliable.”

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The core differentiator is TAP, which delivers correction via L-band satellite signal rather than a local base station or cellular network. If RTK correction drops out, the TAPFill function automatically switches to TAP while keeping the positioning result aligned to the RTK coordinate reference frame, preserving guidance continuity. If GNSS signal is lost entirely or quality falls below threshold, the system issues a warning and requires the operator to disengage and manually confirm conditions before resuming.

Core hardware includes the GC30 Guidance Controller, TC120 tablet terminal (10.1″, IP67), TES30 electric steering motor, camera, and vehicle-specific brackets. Tersus says the kit is compatible with over 90% of ag vehicles across brands, with installation recommended through trained personnel or authorized dealers. The system supports all major constellations — GPS, GLONASS, BeiDou (BDS-3), Galileo, QZSS, SBAS, IRNSS — plus the L-band TAP signal.

Notably, Tersus is also pursuing an open-source-oriented integration path with the AgOpenGPS community, separate from its standard commercial licensing — framed by the company as a way to pair its GNSS/INS fusion and motor-control hardware with community-developed flexibility. ISOBUS VT&TC compatibility and section control for sprayers/seed drills are in development, alongside a 12-inch tablet option. Indicative retail pricing runs roughly $4,000–$8,000, dealer/region/config dependent.

Mark Chen, Tersus’s Director of Digital Media, positioned the AG993 as a stepping stone toward broader autonomy — pointing to ISOBUS Tractor Implement Management, multi-sensor fusion (cameras, LiDAR, radar, IMU) for obstacle detection, and data connectivity with platforms like John Deere Operations Center and agrirouter as the next layers, alongside growing European regulatory/cybersecurity requirements around geofencing and human-machine responsibility boundaries.

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Announced June 17, Kestrel integrates Honeywell’s HG3900 MEMS Inertial Measurement Unit with an M-code receiver and a multi-GNSS receiver in a package the company says is 40 percent smaller and lighter than comparable EGI products on the market. The M-code capability provides access to the military GPS signal’s enhanced anti-spoofing and anti-jam protections, while the multi-GNSS receiver broadens the available constellation coverage under nominal conditions. When external signals are unavailable, the INS layer maintains self-contained navigation continuity.

The system is intended primarily for Group 2 and 3 collaborative combat aircraft and loitering munitions, where the combination of SWaP-C constraints and GNSS-denial risk is most acute, though Honeywell notes applicability to crewed platforms with similar constraints. The company claims up to 80 percent improvement in navigation accuracy over legacy systems and cost reductions of up to 50 percent — both figures are company-sourced. Kestrel will be available in non-ITAR configurations for international defense and commercial operators.

“This system helps operators maintain mission objectives in environments where legacy GPS systems are lagging behind,” said Matt Picchetti, vice president and general manager of Navigation & Sensors at Honeywell Aerospace. Honeywell has produced more than 60,000 EGI units since pioneering the technology in the mid-1990s.

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The enclosure combines the Trimble PX-1 RTX with a Calian AC4990ECF full-band Accutenna 4 (AC4) antenna element in a single drop-in package. The Calian antenna incorporates the company’s eXtended Filtering (XF) technology, which applies tight filtering around GNSS bands — to -80 dB — to reject out-of-band interference from LTE, WiFi, Bluetooth, and Ligado signals. The XF+ variant isolates upper and lower band gain paths to prevent jamming saturation across both.

The PX-1 RTX itself combines Trimble’s CenterPoint RTX corrections with compact GNSS-inertial hardware, delivering real-time centimeter-level positioning and precise true heading. The system is designed to support tighter control during takeoff and landing in obstructed environments and to reduce reliance on magnetic sensors.

“Bypassing lengthy development cycles provides an edge in today’s highly dynamic UAV landscape,” said Joe Hutton, director of Applanix airborne products at Trimble. “Removing board-level integration challenges like RF interference and mechanical noise, and incorporating a high-performance, state-of-the-art GNSS antenna element from Calian, this drop-in positioning solution enables our customers to integrate real-time centimeter positioning into their products in weeks instead of months.”

The smart antenna enclosure option is expected to be available through Trimble Applanix sales channels in Q3 2026.

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The patent covers a visual-inertial odometry approach that uses onboard cameras and inertial sensors to estimate position without GPS. When multiple drones operate together, the system shares positional data between vehicles in real time to improve individual accuracy. The architecture uses an Extended Kalman Filter for state estimation and is designed to keep flight-critical processing onboard each vehicle while minimizing inter-vehicle data exchange.

The capability addresses a persistent vulnerability in drone operations: GPS jamming and spoofing have degraded or disabled unmanned systems across multiple recent conflict zones, accelerating DoD and NATO investment in navigation solutions that do not depend on satellite signals.

The option agreement provides an evaluation period during which NorthStrive will assess the technology and engage potential partners before negotiating a definitive license.

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The company positions the QLX3Gx as a market-ready receiver built around its Dragonfly Digital RF architecture, which moves many traditionally analog RF functions into the digital domain to reduce power, size and cost while retaining multi-constellation GNSS performance. The new evaluation kit is intended to let OEMs characterize power consumption and positioning behavior in their own devices before committing to volume designs. 

According to Qualinx, the QLX3Gx can operate in a 1 mW GNSS mode, with the same silicon supporting a range of power-versus-performance configurations through software. The chip is designed to track multiple constellations and bands concurrently, and to keep tracking and navigation computation on the chip rather than offloading to cloud services or host processors. It also supports authenticated Galileo signals via OSNMA to improve resilience against spoofing, with the company highlighting use cases in asset tracking, wearables and other edge devices that need long battery life as well as resistance to malicious interference. 

Qualinx is also emphasizing supply-chain and manufacturing aspects, noting that the GNSS chip is fabricated at GlobalFoundries’ facility in Dresden, Germany, as part of a broader European semiconductor footprint. A recent €20 million funding round is intended to help move the QLX3Gx family into volume production and expand its availability in international markets. 

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Built on the company’s X20 high-precision platform, the module delivers RTK-grade performance while maintaining precise GNSS-based heading even at low speeds or standstill, a key requirement for auto-steering and autonomous operation. Target sectors include precision agriculture, unmanned aerial vehicles, autonomous machinery, marine and robotics navigation. 

All-band on both antennas, with scalable corrections

The ZED-X20D tracks all major GNSS constellations on L1, L2, L5 and L6, and adds L-band reception for PPP correction services, an “all band on both antennas” approach that is intended to maximize heading availability and stability in challenging environments. To meet different accuracy and deployment needs, it works with RTK, PPP-RTK and PPP correction services, including u-blox’s PointPerfect offerings for regional and global coverage. Built-in support for Galileo E6 enables use of the free Galileo High Accuracy Service (HAS), giving equipment makers multiple options to source corrections. 

u-blox is positioning the ZED-X20D as a drop-in upgrade for existing designs by retaining the established ZED form factor and pairing the module with its ANN-MB2 all-band antenna and PointPerfect services as a turnkey high-precision bundle. The company says this combination is aimed at simplifying design, reducing system cost and accelerating mass adoption of automated and autonomous equipment across agriculture, UAVs, construction and other industrial domains. 

Security and interference resilience for trusted heading

The module includes u-blox’s end-to-end hardened security, with secure boot, signed firmware and a hardware root of trust for cryptographic material, as well as support for Galileo OSNMA and encrypted correction data. All-band frequency diversity and interference monitoring are designed to improve resilience against jamming and other RF threats, while access to high-quality GNSS measurements supports reliable post-processing and integrity monitoring—features likely to appeal to developers building safety-critical or highly automated systems on top of the new heading platform.

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The new solution, SimXTRACT, allows engineers to decompose real-world RF recordings into discrete signal components and replay them as fully controllable scenarios on Spirent simulators.

Positioning, navigation and timing (PNT) developers have traditionally been forced to choose between RF record-and-playback on one side and pure lab simulation on the other. Record-and-playback captures all the richness of the real world, but offers limited control and repeatability. Simulation provides precise control over parameters and repeatability for regression and corner-case testing, but can lack the full complexity of live-sky environments. Spirent positions SimXTRACT as a way to fuse these two approaches.

According to the company, SimXTRACT takes signals captured in the field using Spirent record-and-playback devices and performs complex signal decomposition, breaking each received signal into separate line-of-sight and multipath ray paths. Metadata such as Doppler offset, code error, power level, and angle of arrival (AoA) is retained. That decomposed representation is then converted into simulator drive files that can be loaded into Spirent GNSS simulators as fully controllable, repeatable scenarios.

“SimXTRACT revolutionizes how you can test positioning solutions. By combining real-world insights with lab-based control and repeatability, our customers will no longer have to compromise on how they test in this fast-moving technology area,” said Peter Terry-Brown, Divisional CEO of Spirent’s Positioning business, in the announcement. “SimXTRACT ensures customers get the best of both worlds, with enhanced realism delivering more accurate results, quicker issue resolution, and faster time to market.”

By reducing the amount of time and number of trips required for field data collection, Spirent says users can cut the cost and complexity of scenario capture and generation while still working with high-fidelity, real-world conditions. Developers can also analyze signal recordings, search for specific types of events or environments, and then recreate those conditions in the lab to focus troubleshooting or performance characterization.

Spirent expects SimXTRACT to be used across a broad set of sectors where high-precision PNT is critical, including automotive, chipsets, consumer devices, defense and critical infrastructure. Terry-Brown frames the tool as a way to “bring the real-world environment into every stage of your product realization process,” with the goal of improving product quality while saving time and money.

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Agriculture has entered the era of continuous PNT.

Precision agriculture is moving toward full automation. Guidance systems once treated GNSS as the entire solution; today, the industry recognizes that satellite signals are necessary but insufficient. Farms have become complex RF environments. Tree canopy, terrain, outbuildings, seasonal geometry shifts, multipath near grain elevators, interference from adjacent equipment, and the simple reality that tractors roam in and out of open-sky visibility all challenge the idea that GNSS alone can sustain continuity.

OEMs are building guidance systems that must keep machines on path even when GNSS falters. Autonomy depends on uninterrupted perception of position, velocity and attitude. That means pairing GNSS with inertial systems engineered for agricultural machines, not adapted from other domains.

In a recent conversation with Inside GNSS, Tzeno Galchev, Director, Product Marketing and Applications Engineering for Analog Devices, Inc. (ADI), described how their inertial measurement units (IMUs) are being integrated into next-generation tractors, implements, drones and robotics platforms. ADI’s engineers are focused on what really matters in the field: disciplined inertial performance, controlled lifetime drift, rugged packaging and reliable sensor fusion with GNSS. The message was unambiguous: Autonomy in agriculture can scale rapidly when inertial becomes a baseline requirement. 

The Market Reality: Why Inertial Matters Now 

Precision agriculture has matured beyond the first decade of “straight-line” GNSS guidance. Machines now operate in a wider set of field geometries, crop types and environmental constraints. Several forces are converging:

Tractors are evolving from operator-assisted systems to autonomy-ready platforms. Implements are following, including precision planters, high-clearance sprayers, and robotic harvesters. Each requires continuous PNT. A single GNSS dropout during an autonomous end-of-row turn can result in overlap, missed coverage or unsafe behavior.

Agriculture spans open sky areas and GNSS-hostile corridors. Machines pass under tree rows, within orchard canopies, beside barns or silos, or along field edges lined with windbreaks. Modern high-value crops, such as vineyards, orchards and berries, introduce dense canopy that disrupts L-band signals. Even row crops can create directional multi-path in late summer.

OEM Pressure to Deliver “Always-on” Paths

Agricultural OEMs face customer expectations shaped by the automotive sector. The question is no longer whether GNSS can deliver accuracy; it is whether the total system delivers continuity. That continuity is now a competitive differentiator. Dead-reckoning performance, not positional Root Mean Square (RMS) in open sky, shapes the user experience.

Cost Realism and the Mid-Market Explosion

Farm sizes vary globally. Not every user can justify aerospace-tier inertial systems. ADI’s view is that precision agriculture needs inertial performance that respects cost boundaries while still meeting the dynamics of field machinery: vibration, temperature cycling and shock.

“The demand is there because there’s a shortage of workforce, especially in the developed countries, and these machines make a considerable difference in the cost and efficiency of farming operations,” Galchev said. “They are replacing and reducing the number of workers needed as well as putting workers out of harm’s way.” 

The Shift to Autonomy-Grade Attitude Estimation

GNSS provides position and velocity; but many operations require continuous knowledge of roll, pitch and yaw. Sprayers use boom leveling. Planters need implement attitude to maintain depth accuracy. Drones require stable orientation in low-signal environments. INS establishes those states even when GNSS is degraded.

THE RESULT: GNSS remains the reference, but inertial is now the mechanism that closes the reliability gap.

Inertial Basics for Agricultural Platforms 

Agricultural operators rarely see inertial systems directly. They see better lines, fewer skips, improved boom stability, and smoother turns. Under the hood:

• IMUs measure angular rate and acceleration along orthogonal axes.

• Sensor fusion in an inertial navigation system (INS) uses those measurements to propagate position, velocity and attitude during GNSS gaps.

• Drift is inherent, but it can be minimized, modeled and constrained with well-tuned sensor fusion.

• GNSS resets the INS, bounding cumulative error.

• Agricultural use-cases emphasize short-to-medium duration bridging, not long-haul independent navigation.

Modern MEMS technology has reduced noise, bias instability, and temperature sensitivity to levels appropriate for automotive-grade and robotic applications. ADI’s work has focused on improving consistency across production units, strengthening environmental robustness, and integrating compensation routines at the firmware level.

Agricultural machinery introduces several complicating factors that inertial systems must handle cleanly:

• High vibration environments from diesel engines, tillage tools, and PTO-driven implements.

• Complex motion during headland turns, uneven terrain and differential traction events.

• Thermal swings, from dawn cold starts to midday heat.

• Mechanical shock, especially on implements.

• Long duty cycles, including 14 to 18 hour days in planting or harvest season.

This environment is less deterministic than automotive and more dynamic than many robotics platforms. The IMU/INS must treat vibration as a feature of the mission, not a source of error.

ADI’s Technical Approach

ADI designs inertial solutions with a focus on predictable error behavior, rugged packaging and stable sensor fusion. The company emphasizes several technical principles:

VIBRATION TOLERANCE. Farm machinery produces persistent broadband vibration. ADI considers how vibration intrinsically disturbs the sensors and ADI engineers design mechanical structures that better suppress, cancel and otherwise reduce the effect of vibration directly into the MEMS structures themselves because once vibration is allowed to pollute the sensor signal, it is too late for the INS system to do anything about it. This ensures the INS maintains the correct angular-rate and acceleration signatures even when implements shake violently.

BIAS REPEATABILITY. This is the lifetime bias drift expectation that intends to capture all unmodeled error sources and is not commonly specified in MEMS IMU datasheets. It provides a single error window that will determine the convergence times for critical estimation/filter loops. For systems that need to turn and deploy quickly, failure to anticipate and quantify these errors can limit deployment time and degrade initial heading accuracy. In their latest products, ADI has expanded their Bias Repeatability definition to include turn-on drift/settling, drift from package stress relief, electronic drift and thermal hysteresis. In parallel with expanding the coverage of this specification, ADI has reduced this metric by an order of magnitude in recently-released devices, such as the ADIS16545 and ADIS16576. 

AXIS-TO-AXIS ALIGNMENT. With tight axis-to-axis alignment out of the box and calibrated through an extensive inertial routine over multiple temperature set-points, tight alignment can be achieved only using mechanical alignment features. For tighter alignment than 0.25° one could leverage the tight axis-to-axis alignment (along with excellent bias repeatability in the accelerometer) to greatly simplify the frame alignment process. 

LINEAR, TEMPERATURE-CONTROLLED BEHAVIOR. Temperature gradients on tractors and implements are large. ADI incorporates temperature compensation models enforced at both the sensor and system level. The goal is not perfect thermal invariance, which is unrealistic in cost-sensitive segments, but predictable behavior that fusion algorithms can model accurately.

FUSION-FIRST PHILOSOPHY. ADI treats the IMU as one component of a larger PNT solution. Their systems are designed for tight integration with GNSS receivers, wheel speed sensors, magnetometers, and vehicle CAN data. Robust synchronization and time-based alignment of the inertial output simplifies system coupling. This architecture enables robust attitude estimation and velocity smoothing, especially during headlands or canopy exposure.

PREDICTABLE LIFECYCLE PERFORMANCE. Agricultural platforms must last. ADI designs for multi-season reliability and bounded long-term drift. The objective is to ensure a machine equipped with an ADI IMU behaves the same in year four as it did in year one.

“You can’t calibrate a sensor’s inherent noise performance, its stability, or its response to vibration,” Galchev said. “These unmodeled error sources directly produce error at the output, and that’s where ADI focuses on innovating at the chip level.”

This technical discipline supports the system-level view: Inertial is not a premium feature; it is a foundation for reliable GNSS-enabled autonomy.

Integration in the Field: What Engineers Face

Engineers integrating inertial systems into agricultural machines confront real-world constraints that differ from lab conditions. ADI’s field experience highlights specific patterns.

Booms flex. Toolbars vibrate. Tractor frames twist. Sensor placement often becomes a compromise. An INS may be exposed to off-axis motion uncorrelated with actual vehicle trajectory. ADI mitigates this through calibration routines, filtering strategies, and noise modeling that treat flex and vibration as signal partitions.

Implements as Independent Dynamic Systems

The implement behind a tractor behaves differently from the tractor itself. For operations like variable-rate spraying or multi-row harvesting, implement attitude, even when decoupled from tractor motion, must be sensed accurately. IMUs can be mounted on booms or frames to track these dynamics.

Agricultural systems rely on multiple data streams: GNSS, wheel speed, steering angle, hydraulic cylinder positions, and sometimes LiDAR or camera inputs. INS integration requires precise timing alignment. ADI designs its systems for deterministic latency and reliable time stamping, which improves fusion accuracy.

The transition from row guidance to headland turns stresses both GNSS and INS. Machines accelerate, decelerate, rotate sharply, and pass through GNSS-obstructed corners. ADI’s inertial fusion helps maintain attitude and velocity states during these high-dynamic transitions.

Agricultural drones operate close to trees and terrain. Ground robots operate beneath canopy. INS solutions provide roll/pitch stability, altitude smoothing, and fallback motion propagation when GNSS is degraded.

Economics: Performance Within Reach

Precision agriculture is expanding beyond large, capital-intensive farms. The next wave of adoption will come from mid-market operations and mixed-crop geographies.

• Cost matters. Expensive IMUs are non-starters. ADI designs MEMS-based solutions that offer robust performance within an accessible cost envelope.

• Scalability drives OEM decisions. Manufacturers want sensors available in volume, with predictable lead times and long lifecycle commitments.

• Global adoption requires price/performance balancing. Emerging markets need PNT reliability but cannot bear aerospace-grade costs. Scalable, rugged MEMS solutions fill this gap.

• Autonomy ROI depends on continuity. If a machine can maintain guidance through GNSS disruptions, it can operate longer hours and at higher speeds, improving economics for both OEMs and end-users.

“Just because you go from a big tractor to a smaller tractor, the conditions don’t change that much,” Galchev said. “If you want to achieve the same mission profile, you still need the same performance level.”

As ADI brings cost-efficient inertial capability into mainstream ag equipment, the performance gap between high-end and mid-tier platforms narrows.

The Road Ahead: Multi-Sensor Fusion and Autonomy

Agriculture is evolving toward heterogeneous fleets: autonomous tractors, robotic harvesters, terrain-following sprayers, orchard drones, and edge-connected implements. All require resilient PNT.

End-of-row autonomy

Low-speed, high-precision maneuvers demand stable attitude estimation. INS ensures smooth transitions even in partial GNSS shadows.

Terrain-following and boom dynamics

Sprayers rely on roll/pitch estimates for boom control. IMU data supports rapid damping of boom oscillation, improving chemical placement, reducing drift, and lowering input costs.

Cooperative ground-air systems

Drones performing scouting missions must integrate with guidance systems on the ground. Consistent inertial performance across platforms enables better data fusion and farm-level coordination.

Resilience as a design requirement

Interference, accidental or intentional, is increasingly common. INS helps maintain continuity of operation when GNSS performance degrades. It stabilizes machine behavior during uncertainty and helps diagnostic systems detect anomalies.

Regulatory evolution

As autonomy expands, functional-safety requirements will increase. INS adds a measurable layer of redundancy and validation, supporting safety cases for next-generation machines.

“We have sensors that we released more than 20 years ago still being produced,” Galchev said, “because our customers’ systems have long lifespans and once something works, it can be very difficult and expensive to re-qualify and swap it out.”

As autonomy accelerates, the next decade of agriculture will be shaped by platforms that assume GNSS variability and engineer around it from day one. That shift elevates inertial from an add-on to a core requirement. ADI, with its long record of sensor innovation and system-level discipline, is positioned to anchor that transition. Their approach: predictable drift behavior, calibration at the silicon level, ruggedized packaging, and tight GNSS-INS fusion, gives OEMs a stable foundation to build autonomy across tractors, implements, drones, and emerging agricultural robots. The path forward is clear: Resilient PNT will define productivity, and ADI’s inertial technology will increasingly sit at the center of the autonomy stack, enabling machines that navigate, adapt and operate with confidence in the real conditions of the farm.

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Launched in 2021, Robotic Autonomy in Complex Environments with Resiliency (RACER) was conceived not as a single robotic platform but as an autonomy “stack”—a collection of algorithms, datasets and neural network models that can be ported to multiple vehicles equipped with appropriate sensors. According to DARPA, that stack now allows users to “apply the RACER stack to any vehicle … turning it into an autonomous machine capable of operating in challenging off-road environments, independent of GPS or pre-mapped routes, and at mission-relevant speeds.” 

From Grand Challenge to GPS-independent ground autonomy

DARPA casts RACER as an heir to its 2004–2005 Grand Challenge, which helped kick-start the modern era of autonomous vehicles. Two decades later, the focus has shifted from proving that a single vehicle can complete a desert course to fielding a reusable autonomy layer that can be adapted quickly to new platforms and operational environments. 

“RACER isn’t just about replicating existing military capabilities,” RACER program manager Stuart Young said in the agency’s announcement. “It’s about fundamentally reimagining how missions are executed.” 

For the PNT community, the notable point is that RACER explicitly assumes unreliable or unavailable GPS. DARPA’s description emphasizes that the stack is intended to operate “off the grid,” with reduced reliance on GPS and pre-programmed paths, allowing robotic systems to handle missions such as reconnaissance and breaching at standoff distance from friendly forces. 

Army use cases: breaching and long-range reconnaissance

RACER’s final phase centered on operationally realistic scenarios with Army units. In October 2025, the program partnered with the Army’s III Armored Corps 36th Engineer Brigade during a combat breaching demonstration at Fort Hood, Texas, under the Machine Assisted Rugged Soldier effort. Using the RACER Heavy Platform—a Carnegie Robotics system built on a Textron M5 chassis—the Army paired the robotic vehicle with an M58 mine-clearing line charge to autonomously open a lane through a minefield. 

DARPA highlighted that event as a proof point for using heavy uncrewed platforms in high-risk tasks where GPS may be degraded or denied and where keeping human crews farther from the breach is a priority.

In November 2025, soldiers from the 11th Armored Cavalry Regiment employed RACER-equipped Polaris RZR–based “RACER Fleet Vehicles” as part of an opposition force during a live force-on-force rotation at the National Training Center, Fort Irwin, California. With integrated ISR payloads, those platforms were tasked to conduct autonomous long-range reconnaissance—traditionally a mission for manned scout teams—again with reduced reliance on GPS and no detailed pre-mapping of the route. 

“By decreasing reliance on GPS and pre-programmed paths, RACER ensures warfighters can deploy autonomous assets in any environment, even when operating off the grid,” Young said. “Instead of human scouts going 12 or 15 km into enemy territory, that dangerous work can be handled by a robot while humans are safe, and the risk is minimized.” 

Perception and fast adaptation as “soft” alternative PNT

DARPA describes RACER’s perception architecture as the program’s most significant technical advance. Earlier autonomous ground systems often needed weeks of retraining when moved to a new environment. RACER, by contrast, is said to adapt a new model in roughly a day, which DARPA calls “invaluable for warfighters who need to deploy robotic assets rapidly to unfamiliar terrains.” 

The agency likens the behavior to a human driver with “a priori insight” about how roads normally behave: the autonomy stack predicts what lies ahead based on prior experience and sensor evidence, then adjusts its speed and path when cues—such as an oddly parked vehicle or construction cones—indicate elevated uncertainty. That predictive capability allows higher speeds and safer operation in unstructured terrain, without the crutch of detailed maps or continuous GPS. 

While RACER does not introduce a new radio-frequency PNT source, it effectively functions as a local, perception-driven navigation solution—a form of “soft” alternative navigation that depends on machine-learned terrain understanding rather than external timing or ranging. For PNT practitioners tracking how DoD plans to fight through GPS disruption, RACER sits alongside inertial, visual-odometry, and terrain-referenced navigation efforts as part of a broader shift toward GPS-independent autonomy.

DARPA’s final RACER experiment at Fort Irwin, California, was used to validate this perception architecture and its rapid retraining process, demonstrating that models could be adapted quickly to new terrain conditions while maintaining autonomous mobility. 

Transition paths and dual-use autonomy

With the experimentation campaign ending, DARPA is now emphasizing transition to both military programs and commercial sectors such as agriculture, construction, mining and off-road logistics, where vehicles face similar perception and navigation challenges. Multiple companies have spun out of RACER, including Field AI and Overland AI, which trace their origins to NASA Jet Propulsion Laboratory and University of Washington research respectively. 

“Now that the RACER program is ending, there is a lot of commercial opportunity for private equity,” Young said. “It’s time for both military users and private investors to recognize the transformative potential of RACER and embrace a future where autonomous systems are not just a possibility, but a reliable and integral part of our world.” 

RACER is another signal that DoD is planning for operations where GPS cannot be assumed. Even without a new RF PNT layer, programs like RACER are pushing the services toward autonomy stacks that treat GNSS as one input among many—and that are explicitly designed to keep vehicles moving when that input disappears.

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