Forest and Environmental Research with Mobile Robots: How Field Ecologists Use UGVs

TL;DR: Forest robot research uses unmanned ground vehicles (UGVs) to collect repeatable, georeferenced data under tree canopies where GNSS is degraded and drones lose line of sight to the forest floor. Field ecologists deploy mobile robots like the Leo Rover with LiDAR, multispectral cameras, soil probes, and ROS 2 navigation stacks to map understory structure, monitor biodiversity, sample soil and microclimate, and run long-duration plot surveys. The result is denser datasets, fewer human-hours in difficult terrain, and measurement protocols you can actually reproduce season after season.

Why forest robot research needs ground-based UGVs

Forests break most assumptions that field robots are designed around. Canopy degrades GNSS signals, often leaving position error in the metre-level to tens-of-metres range under dense cover, depending on receiver, constellation, season, and stand structure. Aerial platforms see the top of the canopy but not the trunks, deadwood, or seedlings that drive ecological questions. Manual transects are slow, biased by observer fatigue, and rarely repeatable to the centimeter.

Mobile robots sit in the gap. A UGV moves through the understory at human pace, carries the same sensor payload to the same waypoints week after week, and logs every measurement with timestamps and local coordinates. For forest robot research, that reproducibility is the point. You are not replacing the ecologist; you are removing the variance the ecologist cannot control.

What ecologists actually measure with a forest research robot

Field ecology questions translate into specific sensor payloads. Below are the measurement categories where UGVs have a clear practical fit, based on published deployments and ongoing university projects using platforms in the Leo Rover class.

• Forest structure and biomass: 3D LiDAR (e.g. Velodyne VLP-16, Livox Mid-360, Ouster OS0) generates point clouds of trunks, branches, and understory. Diameter at breast height (DBH) can be extracted with reported errors of a few centimeters under good conditions.
• Microclimate: Air temperature, humidity, PAR (photosynthetically active radiation), and CO₂ sensors mounted at fixed heights. The robot revisits the same plot grid, reducing spatial bias from handheld surveys.
• Soil properties: Probe-mounted moisture, temperature, and electrical conductivity sensors. Some teams add a small actuator to insert TDR probes at preset depths.
• Biodiversity proxies: Acoustic recorders for bird and bat passive monitoring, camera traps triggered at the robot’s stop points, and multispectral cameras for vegetation indices on shrubs and seedlings.
• Phenology and disease: RGB and multispectral imaging of marked individuals across the season, with the robot positioning the camera at the same relative pose each visit.

How autonomy works under the canopy

Autonomous navigation in forests is not solved, but it is workable for plot-scale research. The core problem: you cannot trust GNSS, so the robot must localize against the environment itself.

The standard stack today combines LiDAR-inertial odometry (LIO-SAM, FAST-LIO2) or LiDAR odometry (KISS-ICP) with a ROS 2 navigation framework (Nav2). The robot builds a local map as it moves, fuses IMU data to handle slips and roots, and may use LiDAR loop closure when it revisits known features. For a Leo Rover-class platform, typical operational range per mission is plot-scale, limited more by battery and terrain than by mapping software.

Three practical constraints shape every forest robot research deployment:

1. Terrain traversability. Roots, logs, and soft soil set the upper bound on platform size and ground clearance. A 4WD rover with around 130 mm clearance and pneumatic tires handles many temperate forest floors; boreal deadfall and tropical buttress roots often do not.
2. Perception in clutter. Tall grass and ferns appear as obstacles to a naive costmap. Teams tune voxel filters and use ground segmentation (e.g. Patchwork++) to let the robot push through compliant vegetation while still avoiding solid trunks.
3. Communication. Wi-Fi dies fast in forests. Most deployments run fully onboard with ROS 2 DDS, log to local SSD, and use LoRa or a 4G modem mainly for heartbeat, status, and safety supervision.

What a Leo Rover deployment in forest research looks like

The Leo Rover is an open-source UGV that research groups use as a base platform when they want to skip the mechanical engineering and focus on payload and software. The relevant specs for forest work: 4WD with rocker suspension, IP54 sealing, ROS 2 support, a payload capacity of 5 kg, and a top speed of about 0.4 m/s, which is appropriate for sensor stability in uneven terrain.

A typical forest research configuration looks like this:

• Compute: NVIDIA Jetson Orin Nano or a small x86 SBC running ROS 2 Humble, or Jazzy where Ubuntu 24.04 support is available.
• Primary perception: A 3D LiDAR (Livox Mid-360 or Ouster OS0; a 2D RPLIDAR S3 for lower-budget navigation-only setups) plus a stereo or RGB-D camera (ZED 2i, RealSense D435i where lighting permits) for visual texture.
• Science payload: Multispectral camera (MicaSense or similar), environmental sensor cluster (BME280, SCD41 for CO₂), and a soil probe on a small linear actuator.
• Power: Stock battery for short missions; external LiFePO₄ pack for longer deployments.

Because the platform is open-source, teams modify the chassis to mount specific instruments, fork the ROS 2 packages to add their sensor drivers, and publish their configurations. That openness matters in academic forest robot research, where reviewers expect reproducible hardware and software descriptions.

Where this overlaps with agricultural and precision land-use research

Forest ecology and precision agriculture share more methodology than the field labels suggest. Both need repeatable georeferenced sampling, both can work under partial canopy, and both increasingly rely on multispectral indices (NDVI, NDRE) computed from ground level as well as from drones. A UGV configured for orchard monitoring is, in most respects, the same machine as one configured for understory biodiversity transects. Teams that build a platform for one use case often extend it to the other.

Real research directions and current limitations

Several published lines of work illustrate where forest robot research stands today. ETH Zürich and related groups working with legged platforms such as ANYmal have shown autonomous traversal in unstructured forests, with low localization drift over hundreds of meters in favorable stands. Wheeled UGVs are more limited in terrain but far cheaper and easier to maintain, which is why many ecological deployments still use them.

Open problems you should expect to handle yourself:

• Long-term autonomy. Battery life on small UGVs is typically 2-4 hours of active operation. Charging docks in remote plots are not standard.
• Seasonal map change. Leaf-on and leaf-off conditions change LiDAR maps substantially. You will need to rebuild or update maps each season.
• Wildlife disturbance. Robot noise and presence can bias the behavior you are measuring. Quiet drives, low speed, and scheduled standoff periods help.
• Data volume. A LiDAR-equipped robot can generate 50-200 GB per field day. Plan storage and processing pipelines before deployment, not after.

How to decide whether a UGV fits your study

A mobile robot is worth the integration effort when your protocol meets at least two of these conditions: the measurement is repeated at fixed locations over time, the sensor payload is heavier or more sensitive than a person can carry steadily, or the data needs centimeter- to decimeter-level relative pose information you cannot get from handheld GNSS under canopy. If your study is a one-time survey of a 50 m transect, a tape measure and a clipboard are still faster.

FAQ

Can a UGV navigate autonomously in a dense forest without GNSS?

Yes, within limits. LiDAR-inertial SLAM stacks like FAST-LIO2 and LIO-SAM provide local pose estimates with drift typically below a few percent of distance traveled in structured forest. For plot-scale work, this is often sufficient. For longer transects, you add fiducial markers, surveyed control points, or periodic GNSS fixes in clearings.

What sensors are most useful for forest ecology with a mobile robot?

3D LiDAR for structure, multispectral or hyperspectral cameras for vegetation indices, environmental sensors (temperature, humidity, PAR, CO₂) for microclimate, and soil probes for substrate properties. The exact mix depends on your research question.

How long can a forest research robot operate per mission?

Typical small UGVs run 2-4 hours on internal batteries under continuous sensing and driving. External battery packs can extend this toward a full field day. Slow speeds and intermittent sensing extend runtime further.

Does the Leo Rover handle wet, muddy, or root-covered terrain?

The Leo Rover has IP54 sealing and 4WD with rocker suspension, which covers many temperate forest floors including wet leaves, shallow mud, and small roots. Deep mud, large deadfall, and sustained steep slopes around 30° or more are outside its practical envelope.

Is ROS 2 required, or can older ROS 1 stacks be used?

ROS 2 is the practical choice for new deployments. Most current SLAM, Nav2, and sensor driver development targets ROS 2 Humble and Jazzy. ROS 1 has been end-of-life since May 2025.

How much does a research-ready forest UGV cost to assemble?

A Leo Rover-class platform with LiDAR, stereo camera, onboard compute, and a basic science payload typically lands in the range of 10,000-25,000 EUR depending on sensor choices. Hyperspectral or survey-grade LiDAR pushes this substantially higher.

Can the same robot be used for both forest and agricultural research?

Yes. The platform, navigation stack, and most sensors transfer directly. You change the payload (for example, swapping a soil probe for a canopy multispectral camera) and the mission parameters, not the robot.

Start your forest robotics project

If you are planning a field deployment and want to see how an open-source UGV fits into ecological and environmental research workflows, explore Fictionlab’s research applications page for current configurations, payload examples, and contact details for technical discussion.