What Is Collaborative Safety for Robots? A Practical Guide

Introduction Collaborative safety is the set of protections that enable people and robots to work in the same space without fences. In international standards, collaborative applications are explicitly defined and validated so human contact—planned or accidental—remains within safe limits. This post breaks down how ISO 10218-1/-2 define collaborative applications for industrial robots, how those concepts extend to mobile robots via ISO 3691-4 and ANSI/A3 R15.08, and how Saphira helps teams accelerate compliance with these frameworks.

What the standards say about “collaborative”

• ISO 10218-1/-2 (industrial robots and robot systems): These foundational standards describe when a robot application can be called collaborative—namely, when people can directly interact within the robot’s workspace. In such applications, the robot must monitor and control energies so that any contact does not exceed specified impact force and pressure thresholds, and/or it must maintain protective distance from people.

• Collaborative techniques defined in practice include:

  • Power and Force Limiting (PFL): The robot is inherently safe on contact—through torque sensing, compliant actuation, and/or soft surfaces—to keep collision forces below limits.

  • Speed and Separation Monitoring (SSM): The robot uses sensors to maintain a minimum protective separation distance. As a person approaches, the robot slows; if the limit is breached, it stops.

  • Safety-Rated Monitored Stop (SRMS): The robot enters a safety-rated stop when a person enters the collaborative area, and restarts only when it’s clear.

  • Hand Guiding: The robot moves only under direct human guidance with safety-rated controls.

Extending to mobile robots: ISO 3691-4 and ANSI/A3 R15.08

• ISO 3691-4 (driverless industrial trucks, including AMRs): Extends similar safety principles to moving platforms. It requires safe stop functions, braking performance, and detection fields that adapt to speed and direction. The concept is the same as SSM: maintain a protective separation distance that accounts for robot speed, system reaction time, and sensor coverage.

• ANSI/A3 R15.08 (industrial mobile robots): Aligns with and expands requirements for mobile robot sensing, stopping behavior, operational speeds, functional safety, and validation of detection capabilities in real industrial environments. Together, these standards ensure the robot’s sensing and control match the realities of dynamic workplaces with people, pallets, and vehicles.

Why force limits and distance matter Collaborative applications assume people will share space with a robot. That means two complementary strategies:

1. Limit the harm if contact occurs (PFL). Collaborative arms often incorporate joint torque sensors, series elastic or compliant actuation, soft covers, and current-based collision detection to cap impact forces. Validation typically includes measuring impact force/pressure with instrumentation and confirming it remains below accepted limits for various body regions and scenarios. (Many teams use ISO/TS 15066 as guidance for test methods and thresholds.)

2. Avoid contact whenever possible (SSM). The robot uses safety-rated sensors and logic to maintain a protective separation distance that grows with speed and shrinks as the area is cleared. If the distance is violated, a safety-rated stop is triggered.

Virtual safeguarding: sensors and strategies Beyond physical guarding and interlocked fences, modern collaborative and mobile systems rely on “virtual” safeguards:

• Safety laser scanners (2D): Define warning and protective zones around stationary cells and mobile bases; zones dynamically adjust with speed and steering.

• Safety-rated 3D vision: Time-of-flight cameras or stereo systems (when safety-rated) provide volumetric detection over workcells, conveyors, or vehicle paths.

• Radar and ultrasonics: Useful in dusty, smoky, or reflective environments where optical sensors struggle; often complementary rather than primary.

• Light curtains and safety mats: Traditional presence-sensing devices that still play a role in creating collaborative work zones.

• Force/torque sensors and “skins”: For PFL, joint torque sensors, external FT sensors, distributed tactile skins, and compliant materials reduce impact and support reliable contact detection.

• Functional safety controllers: Safety PLCs and safety-rated control software implement stop categories, speed limits, muting, and interlocks with certified integrity (e.g., PL or SIL levels appropriate to risk).

Approaches to robot safety—in context

• Traditional safeguarding: Physical guards and interlocks isolate people from hazards. Effective, but inflexible.

• Safety-rated monitored stop: The robot halts when a person enters, then resumes once clear—common in applications with occasional human entry.

• Power and Force Limiting (PFL): Enables closer collaboration but demands careful force/pressure verification and ongoing monitoring of collision detection thresholds.

• Speed and Separation Monitoring (SSM): Enables co-presence with dynamic speed scaling. Success depends on coverage (no blind spots), response times, and validated protective separation distances.

Illustrative examples

• Cobot packaging cell (PFL + SRMS): A collaborative arm uses joint torque sensing and soft covers for PFL, allowing operators to hand-load parts. A safety scanner monitors the surrounding area; if someone steps into a high-risk zone during automatic mode, the robot transitions to a safety-rated stopped state. Validation includes impact force testing on common body contact points and verification of the stop behavior.

• AMR in a warehouse (SSM): An AMR uses front/rear safety scanners and side sensors to maintain protective distances while navigating aisles. Speeds are automatically limited near pick stations. Braking tests confirm stopping distances under worst-case loads and floors. The system logs events for traceability and audit.

Where teams struggle: ambiguity, traceability, and iteration

• Standards ambiguity: Which standards apply? What do they actually require for PFL validation or SSM field-of-view? Teams can spend months interpreting overlapping ISO and ANSI/A3 documents.

• Traceability gaps: Spreadsheets proliferate as requirements, tests, and evidence scatter across tools, making audits painful.

• Iteration churn: Small changes—new gripper padding, updated scanner placement—can ripple across requirements, tests, and documentation.

How Saphira accelerates compliance “Saphira is the fastest way for robotics companies to manage safety certification.” Engineers use it to automatically parse requirements and tests from relevant standards (UL, CE, ISO/TÜV, etc.) into structured, traceable information — eliminating months of manual standards review for each product iteration.

Key capabilities for collaborative and mobile robots:

• Automated standards structuring: Saphira converts directives and harmonized standards into actionable requirement/test lists using semantic product details. This is especially valuable where ISO 10218-1/-2, ISO 3691-4, and ANSI/A3 R15.08 overlap.

• Standards retrieval and citations: “SaphiraGPT can pull the latest standards text and cite the exact clauses behind each requirement.” Teams see the sections governing collaborative applications, PFL verification, SSM protective distances, and validation tests—without guesswork.

• Continuous traceability: “Test and rework records sync automatically to the platform, maintaining compliance evidence in real time.” Progress is tracked with automated dashboards instead of fragmented spreadsheets and tools.

• Impact analysis: “Any change triggers an automated report on affected requirements, components, and stakeholders.” If you update collision detection thresholds or swap a scanner, Saphira flags the requirements and tests you must re-run.

• Auditor-ready reporting: “Generates auditor-ready safety reports from existing engineering data (designs, test plans, outputs).” Submission packages for CE/UL/ISO come together with confidence.

Example workflow

1. Input product details and intended collaborative/mobile use case.

2. Saphira identifies applicable certifications, associated costs, and the exact technical requirements that must be met.

3. It auto-generates a structured requirement and test matrix covering PFL (force/pressure validation), SSM (protective separation distances, response times), SRMS, hand-guiding, and braking/stopping performance for mobile platforms.

4. SaphiraGPT attaches clause-level citations from the latest standards.

5. Engineering data, test plans, and results sync to maintain live compliance evidence through iterations.

6. Any design or software change triggers impact analysis and updated to-dos.

7. One click produces an auditor-ready report for CE/UL/ISO submissions.

Business impact

• Turn months of manual standards review into days by automating parsing and structuring of requirements and tests.

• Eliminate ambiguity with clause-level citations from the latest standards.

• Replace spreadsheet-based compliance with live dashboards and continuous evidence capture.

• De-risk audits with auditor-ready reports generated from existing engineering artifacts.

• Accelerate certifications (CE, UL, ISO) for collaborative and mobile robots, even across rapid product iterations.

Conclusion Collaborative safety is about making human–robot interaction not just possible, but measurably safe—through PFL, SSM, and rigorous validation as defined by ISO 10218-1/-2, ISO 3691-4, and ANSI/A3 R15.08. With Saphira, compliance becomes a structured, automated workflow rather than a manual burden — helping teams achieve certifications like CE, UL, and ISO more quickly and with greater confidence. See how Saphira structures ISO/CE/ANSI collaborative safety requirements for your robot, or get a demo of clause-level citations and auditor-ready reporting for your next certification cycle.