MP-MSG-229-11.pdf — page 9

Orchestrating Manned-Unmanned Missions 
Using AI-Based Tasking System Across Multi-Domain Operations in M&S 
STO-MP-MSG-229 
11 - 9 
 
 
Table 2: Manned and Unmanned Assets Capabilities and Specifications. 
Agent 
Capabilities 
Known Specification 
UAV 
Navigation, target detection 
and identification, signal 
jamming detection, 
communications interception, 
intelligence collection 
Model: Camcopter S-100, Speed: 102 km/h, Autonomy: 6 hours, 
Energy: 240 kWh, Range: 612 km, Max Speed: 185 km/h, Motor 
Power: 40 kW, Energy Consumption: 240 kWh at typical speed. 
UUV 
Subsea patrol, tampering 
detection, enemy UUV 
interception 
Model: Iver 3 AUV, Speed: 4.63 km/h, Autonomy: 8 hours, 
Energy: 0.8 kWh, Range: 37.04 km, Max Speed: 7.408 km/h, 
Motor Power: 0.1 kW, Energy Consumption: 0.8 kWh at typical 
speed. 
Submarine Sonar detection, counterattack 
and interception, diver 
deployment 
Model: Boston Whaler 350, Speed: 48.28 km/h, Max Speed: 
91.89 km/h, Motor Power: 895 kW, Energy Consumption: Range 
depends on speed. 
Frigate 
Surface surveillance, anti-
submarine warfare (ASW), 
anti-surface warfare (ASuW), 
anti-air warfare (AAW), UAV 
and RHIB deployment. 
Model: HMCS Halifax-Class, Speed: 27.78 km/h, Autonomy: 
1583.33 hours, Energy: 30,000 km, Range: 18,322.48 km, Max 
Speed: 55.56 km/h, Motor Power: 240 kW, Energy Consumption: 
4715 kW (estimated). / 2nd Submarine used Model: Akula-Class, 
Speed: 46.3 km/h, Max Speed: 61.116 km/h 
RIHB 
Fast deployment, personnel 
extraction, rapid interdiction, 
anti-sabotage operations, 
manned 
Model: Boston Whaler 350, Speed: 48.28 km/h, Autonomy: N/A, 
Energy: N/A, Range: N/A, Max Speed: 91.89 km/h, Motor Power: 
895 kW, Energy Consumption: Range depends on speed. 
3.3 
Dynamic Tasking of MUM-T in MDO  
The algorithm presented here addresses a dynamic, energy-constrained decision-making problem for 
autonomous agents operating in complex environments. The agent, modeled with capabilities for target 
detection, observation, and engagement, is tasked with navigating towards a final destination while 
optimizing energy consumption. The system operates by reading a JSON configuration file, which details the 
agent’s energy levels, capabilities, and environmental factors such as observation and engagement range. 
The agent’s reasoning process is driven by continuous updates of its energy state, simulating time 
progression and movement through a series of predefined positions determined during mission planning. As 
the agent moves, it computes the energy required to reach subsequent positions or the final destination, 
adjusting its route if it detects insufficient energy to proceed. At each decision point, the algorithm checks 
whether the agent’s current energy level is adequate for reaching the target positions, considering both 
observation and engagement capabilities. If the agent’s energy falls below the required threshold to reach the 
final destination, it reassesses its route, discarding positions that are unreachable. If the agent has enough 
energy to proceed, it makes decisions based on the optimal route and energy consumption. This decision-
making is extended to multi-agent scenarios, where the algorithm allows for the sharing of information 
regarding target observations and the assignment of tasks based on the least energy-expensive agent. In cases 
where observations are unreachable by any agent, they are flagged and excluded from the task list. The 
algorithm’s ability to dynamically adjust to changing energy levels and environmental conditions, while 
considering agent capabilities and task prioritization, showcases its potential for real-time, energy-efficient 
decision-making in autonomous systems and MUM-T coordination.  
In order to break down the nodes present in the decision-tree for the tasking model, here are the variable 
names and descriptions: i) kWh: Energy levels (initial and maximum) in kilowatt-hours (kWh),