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BioLLM-Nav: Adaptive Multi-Agent Formation Control for Robotic Surgery using LLM-Guided Decision Making

Abstract

A novel framework combining large language models and formation control for surgical robot swarms, enabling real-time adaptation to dynamic surgical environments. The system uses LLM reasoning capabilities to interpret surgical context and guide precise multi-robot coordination while maintaining safety constraints.

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Research Gap Analysis

Current surgical robotics lacks intelligent coordination between multiple agents and doesn't leverage advanced language models for surgical context understanding and decision-making optimization

BioLLM-Nav: Adaptive Multi-Agent Formation Control for Robotic Surgery using LLM-Guided Decision Making

Motivation

Minimally invasive surgery using multiple cooperative robots requires precise coordination and real-time adaptation to dynamic environments. Current approaches rely on predetermined formations and limited decision-making capabilities, making it difficult to handle unexpected situations or optimize tool positioning for complex procedures. While recent advances in LLMs demonstrate strong reasoning capabilities and formation control algorithms show promise in coordinated movement, these technologies haven't been combined effectively in the surgical domain.

Proposed Approach

LLM-Based Surgical Context Understanding

  • Deploy specialized LLMs trained on surgical procedures and anatomical knowledge to interpret real-time sensor data and surgical context
  • Use self-certainty metrics to evaluate confidence in decision-making
  • Implement distribution-based quality assessment for visual feedback

Adaptive Formation Control

  • Design event-triggered formation control algorithms that respond to LLM-generated insights
  • Incorporate fault-tolerance mechanisms for safety-critical operations
  • Develop hierarchical control architecture combining high-level LLM planning with low-level formation execution

Real-time Optimization

  • Implement reinforcement learning for continuous improvement of formation patterns
  • Use HippoRAG-style memory systems to maintain contextual awareness across procedure phases
  • Deploy differential evolution algorithms to optimize controller parameters during operation

Expected Outcomes

  • Improved surgical precision through context-aware robot positioning
  • Reduced procedure times due to optimized tool coordination
  • Enhanced safety through predictive fault detection and recovery
  • Scalable framework applicable to different surgical procedures

Potential Applications

  • Minimally invasive surgical procedures
  • Microsurgery requiring multiple coordinated tools
  • Training simulations for surgical residents
  • Emergency response scenarios requiring coordinated robot teams
  • Adaptation to non-surgical medical procedures requiring precise tool coordination

Proposed Methodology

Combine LLM-based surgical context interpretation with event-triggered formation control, using reinforcement learning for continuous optimization and memory-augmented decision making

Potential Impact

Could revolutionize minimally invasive surgery by enabling more precise, adaptive, and safer multi-robot procedures while reducing cognitive load on surgeons and improving patient outcomes

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