If your process depends on frequent changeovers, mixed traffic, AMRs, or shared human-robot work, physical fencing and fixed zones often force a tradeoff between safety and productivity. Our approach replaces rigid boundaries with configurable, validated virtual safety behavior aligned to your real operating states.
Why traditional methods break down in dynamic cells
Physical fencing, fixed scanners, and hardcoded zones can work in stable, repeatable cells. But in high-variation environments, they create cost and performance drag:
-
Overly conservative stop behavior that reduces cycle efficiency
-
Rework every time tooling, traffic flow, or task logic changes
-
Lost floor space from fixed perimeter guarding
-
Complex exceptions around maintenance, access, and material flow
-
Inconsistent operator behavior around bypasses and temporary workarounds
What this means
You do not have a guarding problem only.
You have a system adaptability problem that becomes a safety and uptime problem.
Fenceless does not mean less safe
Fenceless safety means using engineered protective functions that are tied to actual hazards and operating states, rather than relying only on fixed physical barriers. The goal is controlled, deterministic safety behavior matched to real operations, with proper risk assessment and validation.
-
It is not “open access at all times”
-
It is not navigation-only obstacle avoidance
-
It is not a shortcut around standards or risk assessment
Important context: U.S. machine guarding obligations still apply under OSHA, including point-of-operation and exposure-based protection requirements. Your safeguarding method can vary, but your duty to protect workers does not.
How Sensory Robotics enables fenceless operation
Physical fencing, fixed scanners, and hardcoded zones can work in stable, repeatable cells. But in high-variation environments, they create cost and performance drag:
-
Overly conservative stop behavior that reduces cycle efficiency
-
Rework every time tooling, traffic flow, or task logic changes
-
Lost floor space from fixed perimeter guarding
-
Complex exceptions around maintenance, access, and material flow
-
Inconsistent operator behavior around bypasses and temporary workarounds
What this means
You do not have a guarding problem only.
You have a system adaptability problem that becomes a safety and uptime problem.
Why traditional methods break down in dynamic cells
We use 3D sensing and configurable safety logic to map protection to the real cell and its current mode of operation.
Core capabilities
-
Live 3D environment awareness for dynamic hazard detection
-
Configurable virtual zones aligned to task states and access patterns
-
Deterministic safety outputs for protective action
-
Structured change management for reconfiguration events
-
Validation-oriented deployment workflow for audit readiness
What this gives your team
-
Better access for operators, material handling, and maintenance
-
Fewer unnecessary stops than broad static “always-on” restriction zones
-
Faster changeovers with less guarding rework
-
Scalable safety architecture across multiple cell types
High-fit environments for fenceless safety
-
Multi-robot cells with frequent model changeovers
-
AMR + fixed robot overlap zones
-
Machine tending with variable human intervention
-
Aerospace and automotive applications with mixed traffic
-
Brownfield facilities where fencing expansion is impractical
-
Cells requiring high uptime and regular process adjustments
Cases that require a different path first
Fenceless deployment should usually be sequenced, not forced.
Start with risk reduction fundamentals when:
-
Hazards are not yet clearly identified by mode/state
-
Safety requirements are not documented
-
Commissioning discipline is weak
-
Change management is informal
-
Teams expect “plug-and-play” without validation planning
-
If these conditions exist, build the foundation first, then phase in virtual safeguarding.
Standards-aligned deployment model
-
Our deployment model is built around risk assessment, protective measure design, verification, and documented validation workflows expected in modern robot safety practice.
-
ISO 10218-1:2025 and ISO 10218-2:2025 define current international safety requirements for industrial robots, robot applications, and robot cells.
-
U.S. organizations commonly align robot safeguarding programs to ANSI/RIA R15.06 frameworks and related guidance for integration and safeguarding methods.
-
OSHA machine guarding requirements remain a legal baseline for worker protection in applicable environments.
6-step deployment framework for fenceless safety
-
Risk Definition
Identify hazards by operating mode, task state, and human interaction pattern. -
Safety Requirements
Define required protective actions, response expectations, and allowed state transitions. -
Zone Architecture
Map virtual protection zones to real geometry, movement envelopes, and access routes. -
Controls Integration
Integrate safety outputs into stop/reaction architecture and supervisory logic. -
Verification & Validation
Execute planned tests for normal, edge, and fault scenarios; record results. -
Operational Governance
Apply change-control rules, retraining triggers, and periodic revalidation cadence.
High-fit environments for fenceless safety
-
Multi-robot cells with frequent model changeovers
-
AMR + fixed robot overlap zones
-
Machine tending with variable human intervention
-
Aerospace and automotive applications with mixed traffic
-
Brownfield facilities where fencing expansion is impractical
-
Cells requiring high uptime and regular process adjustments
Why operations leaders pursue fenceless safety
-
Recover usable floor space
-
Reduce downtime from guarding changes
-
Increase line adaptability for new SKUs/processes
-
Improve operator flow and serviceability
-
Build reusable safety templates across sites
*Use our ROI calculator to model these outcomes for your plant assumptions.
Trusted by teams building next-generation automation safety
-
Active programs in automotive, aerospace, CPG, and defense-linked initiatives
-
Integrations with major robot and automation ecosystems such as FANUC, Denso, ABB, Yaskawa, KUKA, Universal Robotics and Kawasaki.
-
Demonstrated operation in mixed human-robot-AMR environments
Fenceless Safety FAQ
1) What is fenceless safety in robotics?
It is a safeguarding strategy that uses engineered protective functions, often virtualized by sensing and safety logic, to protect people without relying only on fixed perimeter barriers.
2) Is fenceless safety compliant?
It can be, when designed and validated through the applicable risk assessment and standards framework for the specific application and jurisdiction.
3) Is this the same as collaborative robotics?
Not automatically. Collaborative operation is one subset with specific constraints. Fenceless architectures can exist outside classic cobot-only scenarios.
4) Can I eliminate all physical guarding?
Sometimes no. Many applications still require physical controls in selected zones. The right answer is hazard-and task-dependent.
5) Does obstacle avoidance satisfy safety requirements?
No. Navigation avoidance and functional safety are different. Safety requires deterministic protective behavior and verification against defined requirements.
6) Where do teams usually fail?
Most failures come from weak mode/state definitions, poor change control, and incomplete validation of edge cases.
7) What data do you need to evaluate fit?
Cell layout, robot motions, tool hazards, stop behavior targets, operating modes, access patterns, and process change frequency.
8) How long does deployment take?
Timing depends on complexity, controls readiness, and validation scope. Pilot cells move faster when requirements are documented up front. Typically we can deploy within four weeks.
9) Can this scale across multiple lines?
Yes, if you standardize zone logic, validation templates, and governance for configuration changes.
10) What is the best first step?
Run a scoped assessment with your highest-friction cell, define measurable success criteria, and validate before broad rollout.