Alternating Target-Path Planning for Scalable Multi-Agent Coordination
📰 ArXiv cs.AI
Learn to scale multi-agent coordination using alternating target-path planning, a novel approach to concurrent target assignment and pathfinding
Action Steps
- Decouple target assignment and pathfinding using an iterative refinement framework
- Apply Alternating Target-Path Planning to allocate distinct targets and collision-free paths to agents
- Implement Conflict-Based Search (CBS) as a baseline for comparison
- Evaluate the scalability of the proposed approach using benchmarks
- Integrate the new approach into existing multi-agent systems for improved performance
Who Needs to Know This
This technique benefits teams working on multi-agent systems, such as robotics or autonomous vehicles, by providing a more efficient and scalable solution for coordination
Key Insight
💡 Decoupling target assignment and pathfinding enables more efficient and scalable multi-agent coordination
Share This
🤖 Scale multi-agent coordination with Alternating Target-Path Planning! 🚀
Key Takeaways
Learn to scale multi-agent coordination using alternating target-path planning, a novel approach to concurrent target assignment and pathfinding
Full Article
Title: Alternating Target-Path Planning for Scalable Multi-Agent Coordination
Abstract:
arXiv:2605.07744v1 Announce Type: new Abstract: The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets and collision-free paths to agents. Prior work on TAPF has relied exclusively on Conflict-Based Search (CBS), which tightly couples target assignment and pathfinding, resulting in compute-intensive, non-scalable solutions. In contrast, we propose an iterative refinement framework that decouples targe
Abstract:
arXiv:2605.07744v1 Announce Type: new Abstract: The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets and collision-free paths to agents. Prior work on TAPF has relied exclusively on Conflict-Based Search (CBS), which tightly couples target assignment and pathfinding, resulting in compute-intensive, non-scalable solutions. In contrast, we propose an iterative refinement framework that decouples targe
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