Connected Operations
Professional service robots are embedded in connected operational environments.
Connectivity enables supervision, coordination,
updates and lifecycle management across fleets and locations.
This layer includes telemetry, remote monitoring,
fleet orchestration and operational analytics.
In many deployments, it relies on IoT-based infrastructures
that allow robots, sensors and backend systems
to exchange state information continuously.
Connectivity is not an optional enhancement.
Without persistent visibility into system state,
service robots cannot be scaled beyond pilot deployments
or operated reliably in distributed environments.
Evaluation at this layer focuses on observability:
whether system behaviour can be monitored,
failures diagnosed and interventions executed
without direct physical access.
Operational Function
Enables control, visibility, and continuity across distributed robotic systems.
Trust Infrastructure
As service robots become connected systems,
trust can no longer rely on physical safety alone.
It must be established through verifiable behaviour,
controlled access and traceable operations.
This layer covers identity management,
authentication, access control
and system logging.
Its purpose is not surveillance,
but accountability: the ability to reconstruct
what a system did, when and under which conditions.
In some deployments, blockchain-based mechanisms
are used to create tamper-evident logs
or shared audit trails across organisations.
These mechanisms are context-specific
and complement — not replace — existing standards.
Trust infrastructure determines whether
robotic operations can be verified,
audited and accepted in regulated
or public-facing environments.
Trust Function
Makes robotic operations verifiable, accountable, and reviewable.
Autonomy Governance
Autonomy in service robotics is not defined by independence,
but by controlled decision-making within defined boundaries.
This interface layer governs how, when
and under which conditions a system may act on its own.
It includes escalation rules,
fallback mechanisms,
override procedures
and constraints on automated decisions —
particularly in environments shared with humans.
Modern service robots often rely on
machine learning and neural networks
for perception, navigation and decision support.
These capabilities increase flexibility,
but also require governance to prevent
opaque decision chains or cascading failures.
Agentic control systems amplify this need.
Without clear autonomy governance,
self-directed systems risk exceeding
their intended operational scope.
Governance mechanisms ensure that learning-based systems
remain constrained to their certified operational scope,
even as models and environments evolve.
Control Function
Limits autonomy to predictable, reviewable, and safe operational behaviour.
Interoperability
Service robots are deployed within existing
technical and organisational infrastructures.
Their long-term value depends on integration,
not isolation.
This layer covers communication with
enterprise systems,
facility management platforms,
logistics software
and regulatory reporting processes.
Application-to-application (A2A) interfaces
and standardised APIs play a central role.
Interoperability reduces vendor lock-in,
simplifies procurement
and enables robots to remain operationally relevant
as surrounding systems evolve.
From an evaluation perspective,
interoperability determines whether a service robot
can become part of operational infrastructure
rather than remain a standalone solution.
Integration Function
Ensures system compatibility across organisational and technical boundaries.
