Route orchestration in multi-asset autonomous systems requires careful management of the transition from planned to active states. As fleet sizes grow and mission complexity increases,
the latency between route planning and execution becomes a
critical performance bottleneck. This work investigates the realtime constraints imposed by the plannedactive transition and
its cascading effects on overall system throughput.
Modern autonomous asset management systems must balance
between pre-planning efficiency and real-time responsiveness.
The create route() function establishes a planned route in the
system’s state space, but the actual execution begins only
upon activate route() invocation. This two-phase design enables
sophisticated scheduling and resource allocation but introduces
measurable activation latency that can compound across large
fleets.
Our contribution is a comprehensive empirical analysis of
activation timing, command volume scaling, and completion
reliability across heterogeneous asset types. We demonstrate
that activation tail latencies dominate control-plane service
level objectives (SLOs) during route fan-out scenarios, and
propose mitigation strategies for production deployments.
II. METHODS
We implement a controlled benchmark environment using the
provided asset management framework. Our experimental setup
registers heterogeneous assets of type drone and ground,
each with distinct performance characteristics and failure
modes.
A. Route Lifecycle Protocol
For each registered asset, we execute the following protocol:
1) Generate a route with 2–6 randomly distributed waypoints
using create route(asset id,waypoints)
2) Apply a synthetic scheduler delay modeling real-world
resource contention
3) Invoke activate route(route id) to transition from planned
to active state
4) Automatically emit a follow route command upon successful activation
5) Issue per-leg move commands for each waypoint transition
6) Monitor completion status across all command types
Route completion requires both the high-level follow route
command and all constituent leg movements to succeed. This
captures the reality that route execution can fail at multiple
abstraction levels.