July 2021 – May 2022
This phase of development introduces staggered crop rotation of food cultivars, a foundation for tracking food nutrition, advanced life cycle plant growth functions, the addition of random variation and total system entropy, and improved server-side management of agent definitions and data to provide a more dynamic, rich experience with even greater potential for non-linear outcomes and simulation of the real world.
With construction of the SAM Mars habitat analog at the iconic Biosphere 2, SIMOC now includes SAM Presets, a configuration of the SIMOC simulation that closely approximates the real-world SAM habitat. With the newly introduced 3D objects during Configuration and on the Dashboard, the user can now visualize the habitat before and during the simulation, including SAM.
An Arizona State University, undergraduate Computer Science Capstone team has developed a means to capture live sensor data for presentation in the SIMOC interface and capture in the SIMOC back-end. Pressure, temperature, humidity, light levels, CO2 and O2 data at SAM will be made available to local, in-hab crew and to the world through the National Geographic hosted web interface. This ground-truth data will build a positive feedback loop ultimately moving to the application of reinforcement learning in an AI version of SIMOC for a future optimized control system of the SAM life support systems. These couplings of the model to the real-world experiment are imperative to any successful scientific endeavor.
Phase V SIMOC development includes:
- New agents and a preset configuration were added to simulate the SAM research facility being constructed at Biosphere 2.
- A 3D view of the habitat was added to the configuration and dashboard screens.
- Agent definitions, stored in JSON files and previously loaded into the SIMOC database on deployment are now loaded into the model directly at runtime, improving speed and paving the way for real-time custom agent definitions.
- Connections between agents are now specified in a separate JSON file, and include a ‘priority’ parameter such that, for example, human agents will eat fresh food when available, or else eat rations.
- Random variation of currency exchanges and randomly-occuring events were enabled, and are defined in new JSON files, ‘agent_variation’ and ‘agent_events’.
- ‘StorageAgent’ and ‘GeneralAgent’ are combined into a single agent, ‘GeneralAgent’, which can store currencies (as specified by per-currency capacity) and/or exchange currencies. This enables internal storage for active agents, such as plants accumulating internal biomass. It also entailed splitting the ‘air storage’ functionality between the habitat and greenhouse, and adding a new agent (‘atmosphere_equalizer’) to maintain a consistent pressure and composition between the two.
- A new class, ‘PlantAgent’ was defined to encapsulate lifecycle functions specific to plants, e.g. accumulated growth and harvest parameters. A growth multiplier based on ambient CO2 concentrationw also added to this class.
- The CO2 mitigation system was updated to include CO2 storage (for CO2 removed from the internal atmosphere by physico-chemical means) and a CO2 makeup valve (to release stored CO2 back into the internal atmosphere as necessitated by plant demand).
- Testing and documentation were added for the core ABM and the SIMOC web server.