Two-Phase Bilevel Search for the Moving-Target Traveling Salesman Problem with Moving Obstacles
arXiv:2606.18730v1 Announce Type: cross Abstract: The Moving-Target Traveling Salesman Problem (MT-TSP) seeks a minimum cost trajectory for an agent that departs from a static depot, visits a set of moving targets, each within one of their assigned time windows, and returns to the depot. In this article, we study the Moving-Target Traveling Salesman Problem with Moving Obstacles (MT-TSP-MO), a generalization of the MT-TSP where the agent trajectory must avoid moving obstacles. We present a Mixed-Integer Conic Programming (MICP) formulation that can be solved using off-the-shelf solvers, as wel
The continuous advancements in AI and robotics necessitate more sophisticated algorithmic solutions for complex real-world navigation and optimization problems, particularly those involving dynamic environments.
This research provides foundational capabilities critical for autonomous systems operating in unstructured and dynamic environments, impacting logistics, defense, and exploration.
The ability to integrate moving obstacles into moving-target traveling salesman problems significantly enhances the practical deployability and safety of autonomous agents in complex scenarios.
- · Autonomous Logistics Companies
- · Defense Industry (UAVs/UGVs)
- · AI/Robotics Developers
- · Smart City Planners
- · Manual Logistics Operations
- · Systems with Static Planning Paradigms
Enhanced efficiency and safety for single-agent autonomous missions in dynamic environments.
Acceleration of multi-agent and fleet-based autonomous systems in urban or contested spaces.
Reduced operational costs and increased speed for delivery, reconnaissance, and emergency response applications across various sectors.
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Read at arXiv cs.AI