Uncrewed Swarm and Cross-Scenario Co-Simulation
01
Project Background
Swarm strategies and cross-system interfaces must be verified together.
In civilian inspection, area search, and collaborative research tasks, correct operation of one uncrewed platform does not establish reliable collaboration among multiple entities. Task allocation, entity identity, communication delays, and environmental changes can all affect the overall process. Running different simulation systems together also introduces potential time and coordinate inconsistencies.
We combine swarm-strategy comparisons and cross-scenario data exchange in one reproducible project, while defining their acceptance objectives separately. Scenario scale, platform behavior, and data sources are specified first. Abstract illustrative parameters are not presented as actual equipment performance, and simultaneous movement of multiple entities does not substitute for analysis of collaboration effectiveness.
02
Technical Approach
Decouple scenarios, strategies, and synchronization interfaces.
The scenario layer defines the environment, tasks, entities, and events. The strategy layer receives agreed state data and returns task allocations or behavior choices. The synchronization layer maps time, coordinates, and messages across systems. Authority to update each state is explicitly assigned to avoid discontinuities caused by two simulators modifying the same entity.
Assign stable cross-system identifiers to entities, and record timestamps, sequence numbers, and validity periods in messages. Strategies rely only on observations defined in the contract, without accessing additional information from the evaluation environment. For systems that do not run in real time, agree on synchronized advancement or offline exchange in advance; display playback speed must not stand in for a common simulation clock.
03
Implementation Process
Baseline comparisons, incremental expansion, and fault injection.
Establish a rule-based baseline with a small number of entities and simple tasks, checking task-start, state-change, and completion events. Compare strategies using identical inputs and evaluation definitions, retaining random seeds and failed runs. After confirming single-system behavior, connect a second scenario or external algorithm and check entities and messages individually.
During expansion, introduce changes in entity count, task adjustments, delays, packet loss, and disconnected nodes in turn, recording failure causes and recovery. Change one major factor at a time so result differences remain explainable. After multiple runs, report sample counts, completion rates, and anomaly types rather than editing together only a successful demonstration.
04
Deliverables
Scenario packages and batch-run evidence.
Recommended deliverables include scenario configurations, entity parameter descriptions, strategy interfaces, a rule-based baseline, integration adapters, batch-run configurations, and an analysis report. Each aggregate result links to raw logs, the project version, and statistical rules, enabling the recipient to select a record for rerunning or replay.
Interface documentation explains time advancement, coordinate transformations, state ownership, and handling of abnormal messages. Licenses for third-party simulation software, algorithms, and data are listed separately. Software co-simulation alone must not be interpreted as established capability to control physical swarms or operate in the field.
05
Acceptance Method
Check consistency before evaluating strategy effectiveness.
First check for mismatched cross-system entities, backward time jumps, stale states, and incorrect execution of duplicate messages. Then rerun representative tasks and compare strategies against predefined metrics. When samples are insufficient or results vary substantially, retain uncertainty rather than forcing a ranking.
Verification results are limited by the environment, models, and observation conditions. Passing simulation does not replace safety and compliance verification of physical equipment; a working integration interface does not establish the physical accuracy of every submodel. At handover, list the verified scope of interfaces, strategies, and models separately.
06
Implementation Outcomes
Reduce repeated scenario configuration and review effort.
Through unified scenario orchestration and data exchange, we organize separate runs into repeatable joint simulations.
Efficiency is assessed through scenario preparation time, interface configuration errors, reproducibility of results within a batch, and time to locate events.