arXiv:2605.28454v1 Announce Type: new Abstract: Greedy Best-First Search (GBFS) is the dominant approach for solving search problems where the goal can be estimated with a heuristic, such as planning, route finding, navigation, and pathfinding. This is especially true when the memory is tightly constrained, such as planning on edge devices. To alleviate that, we present GONDOR (Greedy Online Navigation with Dynamic Outpost-based Re-search), a memory-efficient extension of GBFS that allows search to continue under strict memory limits by periodically compressing the search tree while retaining
The continuous drive towards deploying advanced AI on resource-constrained edge devices necessitates new, memory-efficient search algorithms like GONDOR.
This development addresses a critical bottleneck in AI deployment, enabling more complex autonomous functions in real-world, embedded systems with limited processing power.
AI planning and execution can now be performed more effectively on edge devices due to significantly reduced memory requirements, expanding the scope of AI applications outside of major data centers.
- · Edge AI hardware developers
- · Robotics industry
- · Autonomous vehicle manufacturers
- · Developers of AI agents
- · Traditional high-memory AI systems for planning
Increased adoption of AI planning in memory-constrained environments, such as drones and consumer devices.
Acceleration in the development and deployment of truly autonomous agents and robots that operate independently without constant cloud connectivity.
New competitive landscape emerges for AI hardware, favoring designs optimized for efficient local processing over raw computational power.
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Read at arXiv cs.AI