Multiagent Routing Problem with Dynamic Target Arrivals Solved via Approximate Dynamic Programming
Abstract
This research formulates and solves the multiagent routing problem with dynamic target arrivals (MRP-DTA), a stochastic system wherein a team of autonomous unmanned aerial vehicles (AUAVs) executes a strike coordination and reconnaissance (SCAR) mission against a notional adversary. Dynamic target arrivals that occur during the mission present the team of AUAVs with a sequential decision-making process which we model via a Markov Decision Process (MDP). To combat the curse of dimensionality, we construct and implement a hybrid approximate dynamic programming (ADP) algorithmic framework that employs a parametric cost function approximation (CFA) which augments a direct lookahead (DLA) model via a parameterization to the objective function. We show a statistically signifixC;cant improvement over the repeated greedy marginal heuristic benchmark policy for 19 out of 20 problem instances and a statistically signixC;ficant improvement over the repeated sequential orienteering problem benchmark policy for 8 out of 10 problem instances of the MRP-DTA. Results of excursion analysis show the value trade off of balancing solution quality and computational exB;ort when selecting the base optimization model for our CFA-DLA algorithm.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 24, 2022
- Accession Number
- AD1172394
Entities
People
- Andrew E. Mogan
Organizations
- Air Force Institute of Technology