Solid-state LiDAR

Solid-state LiDAR appears across 1 tracked robots, concentrated in Commercial. Start here when the job is understanding why this sensor matters, then sweep the live roster without scrolling through 1 oversized cards.

Sensor pages are really about decision quality. The key question is not whether the part exists, but what class of perception problem it meaningfully improves.

1 robots 1 ready now 1 manufacturers 0 public prices
Market snapshot Robot directory FAQ Reference library

Where it shows up

1 category

The heaviest concentration is in Commercial (1). On this route, category distribution is the fastest clue for whether Solid-state LiDAR is a baseline utility or a more selective differentiator.

What it tends to unlock

Shortlist impact

Perception, mapping, detection, and safer motion decisions, cleaner autonomy loops when the robot needs environmental context, and higher-quality data for navigation, manipulation, or monitoring.

What to verify

Do not stop at the label

Coverage, placement, and how the sensor performs in messy conditions, what decisions actually rely on the sensor versus backup systems, and whether the label signals depth, proximity, or full-scene understanding. Top manufacturers here include Coco Robotics (1).

Evidence sources

  • Aggregated from each robot's `specs.sensors` field in ui44 data.

Official references

Coco 2Coco Robotics · Commercial

Market snapshot

Use the structure first: which categories lean on Solid-state LiDAR, which manufacturers repeat it, and what usually ships beside it.

Top categories

# Name Usage
1 Commercial 1 robot

Top manufacturers

# Name Usage
1 Coco Robotics 1 robot

Commonly paired with Solid-state LiDAR

# Name Shared robots
1 Cameras 1 robot
2 Cellular 1 robot
3 Coco App 1 robot
4 End-to-end neural networks on NVIDIA Jetson Orin NX; trained via NVIDIA Omniverse, Isaac Sim, and Cosmos synthetic data pipelines 1 robot
5 GPS 1 robot
6 IMU 1 robot

At a glance

Kind Sensor
Tracked robots 1
Ready now 1
Public prices 0
Official sources 1
Variants normalized 1

Reading note

This page is strongest when you use the rankings to orient the market and the directory below to verify individual profiles. The goal is faster comparison, not another endless essay stack.

Robot directory · Solid-state LiDAR

The old card wall is replaced with a featured first-click strip and a dense inventory table so the route behaves like a serious directory.

This route now uses a shortlist-first browse model: open the clearest live profiles first, then sweep the full inventory in a dense table instead of burning through one oversized card after another.

Ready now

1

Public price

0

Official links

1

Featured now

1

How to scan this directory

Featured first, dense sweep second.

  • Featured cards: the cleanest first clicks when you need a fast sense of real-world implementation quality.
  • Inventory table: every tracked robot in a calmer scan path, sorted by readiness before price clarity.
  • Compare intent: use status, official links, and standout spec signals before trusting the label alone.

Best first clicks

Open these before sweeping the full inventory

These robots score highest on readiness, public detail quality, and image clarity, making them the fastest way to understand how Solid-state LiDAR shows up in practice.

Active Commercial
Coco Robotics Since 2026

Coco 2

Coco 2 is the next-generation fully autonomous delivery robot from Coco Robotics, a Venice Beach–based startup founded at UCLA in 2020. Unlike its predecessor which relied on remote human drivers, Coco 2 operates with full autonomy using end-to-end neural networks trained on millions of real-world city miles. The robot navigates sidewalks, bike lanes, and roads where permitted, reducing delivery times by up to 50% compared to the prior generation. Built around NVIDIA Jetson Orin NX edge computing and solid-state LiDAR, Coco 2 reaches speeds up to 21 km/h (13 mph) with a 32 km range per charge. It features a multi-compartment cargo area that fits up to four 18-inch pizza boxes or six separate customer orders, a 360-degree turn-in-place design, and a swappable battery. The robot is fully submersible for flood conditions and compatible with snow tires for winter operation. Coco powers deliveries through Uber Eats, DoorDash, and Wolt, serving over 3,000 merchants across US cities including Los Angeles, Chicago, Miami, and Jersey City, as well as Helsinki, Finland. The company plans to scale to thousands of robots globally through 2026 with expansion into Europe and Asia.

Public price

Price TBA

Not available for consumer purchase;…

Battery

32 km (20 mi) range per charge

Charge Not disclosed (swappable battery)

Shortlist read

Active in the catalog; verify the latest media and rollout details.

Profile

Full inventory · 1 robots

Compact mobile scan: status, price, standout context, and links stay visible without sideways scrolling.

Quick answers

FAQ

The short version of what this label means in the ui44 catalog, where it matters, and how to compare it without over-reading the marketing copy.

Frequently Asked Questions

How common is Solid-state LiDAR in the database?

Solid-state LiDAR currently appears on 1 tracked robots across 1 manufacturers. That makes this route useful for both deep research and fast shortlist scanning, not just one-off editorial reading.

Which robot categories lean on Solid-state LiDAR the most?

The strongest concentration is in Commercial (1). Category mix is the fastest clue for whether this component behaves like baseline plumbing or a more selective differentiator.

Does Solid-state LiDAR usually show up on ready-to-buy robots?

1 of the 1 tracked profiles are currently marked Available or Active. That means the label has live market relevance here, but you should still open the profiles with public pricing or official links first before treating it as a clean buyer signal.

What should I compare first on this page?

Start with readiness, official source quality, and the standout spec column in the inventory table. On component routes, those three signals usually remove weak profiles faster than reading every descriptive paragraph.

What usually ships alongside Solid-state LiDAR?

The strongest shared-stack signals here are Cameras (1), Cellular (1), and Coco App (1). Use those pairings to branch into adjacent component pages when one label is too narrow for the decision.

Are there enough public price points to benchmark this component?

0 matching robots currently expose public pricing. That is enough to create directional context, but not enough to treat one price bracket as the whole market. Use the directory to find the transparent profiles first, then widen the sweep.

Which manufacturers are worth opening first?

Start with Coco Robotics (1). Repetition across manufacturers is often the clearest signal that the component is part of a stable market pattern rather than a one-off marketing callout.

Reference library

The original long-form component research is still here, but collapsed so the main route can prioritize hierarchy and scan speed.

Fundamentals

The baseline explanation of what Solid-state LiDAR is, why it matters, and how to think about it before comparing implementations.

What Is Solid-state LiDAR?

Solid-state LiDAR is a sensor component found in 1 robot tracked in the ui44 Home Robot Database. As a sensor technology, Solid-state LiDAR plays a specific role in enabling robot perception, interaction, or operation depending on its implementation in each platform.

At a Glance

Component Type

Sensor

Used By

1 robot

Manufacturer

Coco Robotics

Category

Commercial

Available Now

1 robot

Sensors are the perceptual backbone of any robot. They convert physical phenomena — light, sound, distance, motion, temperature — into digital signals that the robot's AI can process and act upon.

Key Points

  • Convert physical phenomena into digital signals
  • Enable obstacle detection, navigation, and object recognition
  • Without sensors, a robot cannot interact safely with its environment

In the ui44 database, Solid-state LiDAR is categorized under Sensor components. For a comprehensive explanation of all component types, consult the components glossary.

Why Solid-state LiDAR Matters in Robotics

The sensor suite is one of the most important differentiators between robots. Robots with richer sensor arrays can navigate more complex environments, avoid obstacles more reliably, and perform more nuanced tasks.

Directly impacts what a robot can actually do in practice — not just on paper

Richer sensor arrays enable more complex navigation and interaction

Determines obstacle avoidance reliability and object/person recognition

Solid-state LiDAR Adoption

Used in 1 robot across 1 category — Commercial, indicating specialized use across the robotics industry.

How Solid-state LiDAR Works

Modern robot sensors work by emitting or detecting various forms of energy. The robot's processor fuses data from multiple sensors simultaneously (sensor fusion) to build a coherent understanding of its surroundings.

1

Active sensors

LiDAR and ultrasonic emit signals and measure reflections to determine distance and shape

2

Passive sensors

Cameras and microphones detect ambient light and sound without emitting anything

3

Sensor fusion

The processor combines data from all sensors simultaneously for a coherent environmental picture

Solid-state LiDAR Integration

Implementation varies by robot platform and manufacturer. Each robot integrates Solid-state LiDAR differently depending on system architecture, use case, and target tasks. Integration with other onboard sensors and the main processing unit determines real-world performance.

Technical notes and use cases

Deeper technical framing, matched technology profiles, and the longer use-case treatment for Solid-state LiDAR.

Solid-state LiDAR: Detailed Technology Analysis

In-depth technical analysis of 1 technology domain relevant to this component

Technology Overview

While the sections above cover general sensor principles, this analysis focuses on the particular technology domains relevant to Solid-state LiDAR based on its implementation characteristics.

LiDAR & Time-of-Flight Ranging

LiDAR (Light Detection and Ranging) and time-of-flight sensors measure distances by emitting light pulses and measuring the time they take to reflect back from surfaces. This principle enables precise, three-dimensional mapping of the robot's environment regardless of ambient lighting conditions — a significant advantage over camera-only systems that struggle in darkness or strong direct sunlight. In home robotics, LiDAR has become the gold standard for floor plan mapping and systematic navigation.

Read full technical analysis

Two main LiDAR architectures exist in consumer robotics. Mechanical spinning LiDAR uses a rotating mirror or emitter assembly to sweep a laser beam 360° around the robot, building a complete horizontal distance profile with each revolution. This technology is proven and reliable but involves moving parts that can wear over time. Solid-state LiDAR eliminates moving components by using arrays of emitters and detectors, or MEMS (micro-electromechanical) mirrors, to steer the beam electronically. Solid-state designs are more compact, potentially more durable, and increasingly cost-effective, though they may have slightly different field-of-view characteristics than spinning units.

Time-of-flight sensors used in robotics typically operate with infrared laser diodes at wavelengths around 850-940 nm, which are invisible to the human eye. Consumer robots universally use Class 1 eye-safe lasers, meaning the beam intensity is low enough to be safe even with direct eye exposure. The precision of these sensors — typically 1-3 cm at ranges up to 12 meters for consumer-grade units — enables robots to build room maps accurate enough for efficient navigation and furniture avoidance. More advanced implementations combine LiDAR distance data with camera imagery in a process called sensor fusion, creating rich 3D environmental models that combine the geometric precision of LiDAR with the semantic richness of visual data.

Implementation Context: Solid-state LiDAR in the Coco 2

In the ui44 database, Solid-state LiDAR is currently tracked exclusively in the Coco 2 by Coco Robotics. This commercial robot integrates Solid-state LiDAR as part of a total technology stack comprising 7 components: 4 sensors, 2 connectivity modules, and a End-to-end neural networks on NVIDIA Jetson Orin NX; trained via NVIDIA Omniverse, Isaac Sim, and Cosmos synthetic data pipelines AI platform.

Coco 2 is the next-generation fully autonomous delivery robot from Coco Robotics, a Venice Beach–based startup founded at UCLA in 2020. Unlike its predecessor which relied on remote human drivers, Coco 2 operates with full autonomy using end-to-end neural networks trained on millions of real-world city miles. The robot navigates sidewalks, bike lanes, and roads where permitted, reducing delivery t…

Visit the full Coco 2 specification page for complete technical details and availability information.

Solid-state LiDAR works alongside 3 other sensor components in the Coco 2: Cameras, GPS, IMU. This combination of sensor technologies creates the Coco 2's overall sensor capabilities, with each component contributing different aspects of environmental perception.

Solid-state LiDAR: Technical Deep Dive

Beyond the high-level overview, understanding the technical foundations of sensor technologies like Solid-state LiDAR helps buyers and researchers evaluate implementations more critically.

Engineering Principles

Every sensor converts a physical quantity into an electrical signal that can be digitized and processed. The raw analog output is conditioned through amplification, filtering, and A/D conversion before reaching the processor.

  • Optical sensors use photodiodes or CMOS arrays to detect photons
  • Acoustic sensors use piezoelectric elements to detect pressure waves
  • Inertial sensors use MEMS to detect acceleration and rotation
  • Range sensors use time-of-flight or structured light for distance measurement

Performance Characteristics

Sensor performance involves key metrics with inherent engineering trade-offs.

Accuracy How close the reading is to the true value
Precision Consistency across repeated measurements
Resolution Smallest detectable change in measurement
Sampling rate Reading frequency — critical for fast-moving robots
Field of view Spatial coverage area of the sensor

Technological Evolution

Sensor technology in robotics has evolved dramatically over the past decade.

Early home robots relied on simple bump sensors and infrared proximity detectors

Today's platforms incorporate multi-spectral cameras, solid-state LiDAR, and millimeter-wave radar

Miniaturization: sensors that filled circuit boards now fit into fingernail-sized packages

Next frontier: sensor fusion at the hardware level — multiple sensing modalities in single chip-scale packages

Known Limitations

No sensor is perfect in all conditions. Understanding limitations is critical for evaluating robots in specific environments.

  • Optical sensors struggle in direct sunlight or complete darkness
  • LiDAR can be confused by mirrors, glass, and highly reflective surfaces
  • Ultrasonic sensors may produce false readings in complex acoustic environments
  • Dust, fog, rain, and temperature extremes can degrade performance

Use Cases & Applications for Solid-state LiDAR

Key application domains for sensor technologies like Solid-state LiDAR.

Autonomous Navigation

Sensors enable robots to build maps of their environment, detect obstacles in real time, and plan collision-free paths. This is essential for both indoor robots (navigating furniture and doorways) and outdoor robots (handling terrain variations and weather conditions). The quality and coverage of the sensor array directly determines how reliably a robot can navigate without human intervention.

Object Recognition & Manipulation

Advanced sensors allow robots to identify objects by shape, color, and texture, enabling tasks like picking up items, sorting packages, or recognizing faces. Depth-sensing technologies are particularly important for calculating object distances and sizes, which is necessary for precise manipulation in both home and industrial settings.

Safety & Collision Avoidance

In environments shared with humans, sensors provide the critical safety layer that prevents robots from causing harm. Proximity sensors, bumper sensors, and vision systems work together to detect people and obstacles, triggering immediate stop or avoidance maneuvers. This is a fundamental requirement for any robot operating in homes, hospitals, or public spaces.

Environmental Monitoring

Sensors can measure temperature, humidity, air quality, and other environmental parameters. Robots equipped with these sensors can perform automated monitoring rounds in warehouses, data centers, or homes, alerting users to abnormal conditions like water leaks, temperature spikes, or poor air quality.

Human-Robot Interaction

Microphones, cameras, and touch sensors enable natural interaction between robots and humans. These sensors allow robots to recognize voice commands, detect gestures, respond to touch, and maintain appropriate social distances during conversations or collaborative tasks.

9 Capabilities Across 1 robot

Fully Autonomous Navigation (no remote human driver) Sidewalk, Bike Lane, and Road Navigation Multi-Compartment Delivery (up to 6 orders per trip) All-Weather Operation (submersible, snow tire compatible) 30% Grade (17°) Hill Climbing 360-Degree Turn-in-Place Swappable Battery Quick Swappable Tires Real-Time City Map Intelligence

Visit each robot's detail page to see which capabilities are available on specific models.

Market breakdown and adjacent routes

Manufacturer mix, specs context, price context, category overlap, and adjacent components worth branching into next.

Solid-state LiDAR Across Robot Categories

Solid-state LiDAR spans 1 robot category — from consumer to research platforms.

Commercial

1

robot using Solid-state LiDAR

Coco 2

Technologies most often paired with Solid-state LiDAR across 1 robot.

Cameras
100%
GPS
100%
IMU
100%
Cellular
100%
Coco App
100%
End-to-end neural networks on NVIDIA Jetson Orin NX; trained via NVIDIA Omniverse, Isaac Sim, and Cosmos synthetic data pipelines
100%

Browse the full components directory or see the components glossary for detailed explanations of each technology.

Alternatives to Solid-state LiDAR

399 other sensor technologies tracked in ui44, ranked by adoption.

IMU

28 robots · 1 also use Solid-state LiDAR

LiDAR

14 robots

Cliff Sensors

13 robots

Vision System

13 robots

Force/Torque Sensors

12 robots

Force Sensors

8 robots

Microphones

7 robots

RGB Camera

7 robots

Browse all Sensor components or use the robot comparison tool to evaluate how different sensor configurations perform across specific robot models.

Solid-state LiDAR in the Broader Robotics Industry

The robotics sensor market is one of the fastest-growing segments in the broader sensor industry. As robots move from controlled industrial environments into unstructured home and commercial spaces, the demands on sensor technology increase dramatically.

Key Industry Trends

Multi-modal sensing

Robots combine multiple sensor types (vision, depth, tactile, inertial) to build comprehensive environmental understanding

Miniaturization

Sensors that once occupied entire circuit boards now fit into fingernail-sized packages, making advanced sensing affordable for consumer robots

Edge AI integration

AI processing directly in sensor modules enables faster perception without cloud latency

Industry Adoption Snapshot

Solid-state LiDAR is adopted by 1 robot from 1 manufacturer in the ui44 database, providing a data-driven view of real-world deployment patterns.

Integration & Ecosystem Compatibility

Platform compatibility, voice integration, and AI capabilities across robots with Solid-state LiDAR.

Platform Compatibility

Uber EatsDoorDashWoltCoco App

AI Platforms

End-to-end neural networks on NVIDIA Jetson Orin NX; trained via NVIDIA Omniverse, Isaac Sim, and Cosmos synthetic data pipelines

Buyer and operations guidance

The long-form buyer, maintenance, and troubleshooting material kept available without forcing it into the main scan path.

Buyer Considerations for Solid-state LiDAR

If Solid-state LiDAR is an important factor in your robot selection, here are key considerations to guide your decision.

What to Look For in Sensor Components

Coverage area

Does the sensor array provide 360° awareness or only forward-facing detection?

Range

How far can the robot sense obstacles or objects?

Resolution

How detailed is the sensor data for recognition tasks?

Redundancy

Are there backup sensors if one fails?

Serviceability

Are sensors user-serviceable or require manufacturer maintenance?

Available Now: 1 of 1 Robots

Coco 2

How to Evaluate Solid-state LiDAR

Integration Quality

A component is only as good as its integration. Check how the manufacturer has incorporated Solid-state LiDAR into the overall robot design and software stack.

Complementary Components

Review what other sensor technologies are paired with Solid-state LiDAR in each robot — see the related components section.

Category Fit

Make sure the robot's category matches your use case. Solid-state LiDAR serves different roles in different robot types.

Manufacturer Track Record

Consider the manufacturer's reputation for software updates, support, and component reliability.

Compare Before You Buy

Use the ui44 comparison tool to evaluate robots with Solid-state LiDAR side by side.

Maintenance & Longevity: Solid-state LiDAR

Overview

Sensors are among the most maintenance-sensitive components in a robot. Their performance can degrade over time due to physical wear, environmental exposure, and calibration drift. Understanding the maintenance profile of a robot's sensor suite helps set realistic expectations for long-term ownership and operation.

Durability & Reliability

Sensor durability varies significantly by type. Solid-state sensors like IMUs and accelerometers have no moving parts and typically last the lifetime of the robot.

  • •Optical sensors like cameras and LiDAR can accumulate dust, scratches, or condensation on their lenses over time.
  • •Mechanical sensors such as bump sensors and encoders may experience wear on moving contacts.
  • •Environmental sensors for temperature and humidity are generally robust but can be affected by corrosive environments.
  • •Overall, sensor failure rates in modern consumer robots are low, but environmental factors like dust accumulation and UV exposure can gradually degrade performance rather than cause sudden failure.
Ongoing Maintenance

Regular sensor maintenance primarily involves keeping optical surfaces clean. Camera lenses, LiDAR windows, and infrared emitters should be wiped with a soft, lint-free cloth to remove dust and fingerprints.

  • •Many modern robots perform automatic sensor self-diagnostics and will alert users when calibration has drifted beyond acceptable limits.
  • •Some robots support user-initiated recalibration routines for specific sensors.
  • •For robots used in dusty or pet-heavy environments, more frequent cleaning of sensor surfaces may be necessary.
  • •Manufacturer documentation typically includes sensor care instructions specific to the robot's sensor configuration.
Future-Proofing Considerations

When evaluating sensor technology for long-term value, consider the manufacturer's track record for software updates that improve sensor utilization. A robot with good sensors and ongoing software development can actually improve its performance over time as algorithms are refined.

  • •However, sensor hardware itself cannot be upgraded post-purchase on most consumer robots, making the initial sensor specification an important long-term consideration.
  • •Robots with modular sensor designs that allow component replacement offer better long-term maintainability, though this is currently more common in commercial and research platforms than consumer products.

For the 1 robot in the ui44 database using Solid-state LiDAR, we recommend checking the individual robot pages for manufacturer-specific maintenance guidance and support documentation. Each manufacturer has different support policies, update frequencies, and warranty terms that affect the long-term ownership experience of their sensor technologies.

Troubleshooting & Common Issues: Solid-state LiDAR

Sensor-related issues are among the most common problems home robot owners encounter. Many sensor issues can be resolved with simple maintenance or environmental adjustments, while others may indicate hardware problems requiring manufacturer support. Understanding common failure modes helps you diagnose and resolve issues quickly, minimizing robot downtime.

Robot bumps into obstacles it should detect

Likely Causes

  • Dirty or obstructed sensor windows are the most frequent cause.
  • Dust, pet hair, fingerprints, or cleaning solution residue on LiDAR, camera, or infrared sensor surfaces significantly reduce detection accuracy.
  • Highly reflective surfaces like mirrors, glass doors, and glossy furniture can also confuse optical and laser-based sensors by creating phantom readings or absorbing signals entirely.

Resolution

  • Clean all sensor windows and lenses with a soft, dry microfiber cloth.
  • Avoid chemical cleaners unless the manufacturer specifically recommends them.
  • If cleaning does not resolve the issue, check for recent firmware updates that may address sensor calibration.
  • For persistent problems with specific surfaces, consider applying anti-reflective film to mirrors or glass surfaces in the robot's operating area.

Robot map becomes inaccurate or corrupted over time

Likely Causes

  • Sensor drift and calibration degradation can cause mapping errors.
  • Significant furniture rearrangement, new obstacles, or changed room layouts may confuse the mapping algorithm.
  • In some cases, electromagnetic interference from nearby electronics can affect sensor readings used for localization.

Resolution

  • Delete and rebuild the map from scratch using the manufacturer's app.
  • Ensure the robot's firmware is up to date, as mapping improvements are frequently included in updates.
  • If the problem recurs, run the robot during periods of minimal household activity to get the cleanest initial map.

Cliff or drop sensors trigger on flat surfaces

Likely Causes

  • Dark-colored flooring, transitions between floor materials, and thick carpet edges can trigger infrared cliff sensors.
  • Direct sunlight hitting the floor near the robot can also interfere with infrared detection by saturating the sensor with ambient infrared light.

Resolution

  • Clean the cliff sensors on the underside of the robot.
  • If the issue occurs at specific locations consistently, check whether the floor has very dark patches, strong color transitions, or high-gloss finishes that might confuse the sensors.
  • Some manufacturers allow cliff sensor sensitivity adjustment through the companion app.

When to Contact the Manufacturer

  • Contact the manufacturer if sensor issues persist after cleaning and firmware updates, if you notice physical damage to any sensor housing, or if the robot reports sensor errors in its diagnostic log.
  • Sensor calibration that cannot be corrected through standard procedures may indicate hardware degradation requiring professional service or component replacement.

For model-specific troubleshooting, visit the individual robot pages for the 1 robot using Solid-state LiDAR. Each manufacturer provides model-specific support resources and diagnostic tools for their sensor implementations.

What to do next

Stay in the catalog flow

This page should hand you off to the next useful comparison step, not strand you at the bottom of a long detail route.

1

Widen the layer

Browse all Sensor

Open the full sensor workbench when Solid-state LiDAR is only one part of the decision and you need the broader market map.
2

Side-by-side check

Compare the strongest live profiles

Move from label-level research into direct robot comparison once you know which profiles are documented well enough to trust.
3

Adjacent signal

Open Cameras

This is the most common neighboring component on robots that already use Solid-state LiDAR, so it is the fastest next branch if you need stack context.