Neuromorphic Decision Dynamics for Deadlock-free Safe Navigation in Congested Environments

Background

Fleets of autonomous vehicles and robots are increasingly used in logistics, transportation, and defense. As these fleets grow, they must navigate crowded and sometimes hostile environments where unexpected hazards can abruptly disrupt operations. Avoiding collisions while maintaining steady progress toward destinations is essential. However, the central control systems and reliable communication networks needed to coordinate these fleets may be unavailable or compromised. These limitations have prompted approaches that allow each vehicle to make decisions using its own sensors.

Existing navigation methods still struggle to keep vehicles moving safely in crowded environments. Systems that rely on central control can experience processing delays as fleets grow, and a failure in the central system can disrupt the entire fleet. Approaches that let vehicles make decisions independently face a different challenge: existing collision-avoidance methods can leave vehicles stuck at crowded intersections. Without a way to resolve these stalemates, vehicles may become trapped in deadlocks, freezing in place, or livelocks, repeatedly moving back and forth without advancing. Keeping vehicles moving safely becomes even more difficult when sensor readings are inaccurate or nearby vehicles behave unpredictably or fail to cooperate.

Technology

Neuromorphic Decision Dynamics (NDD) offers software engineers, control systems engineers, data scientists, and machine learning researchers a framework for addressing collision avoidance and coordination challenges in autonomous vehicles and robots.

NDD is a decentralized control framework designed to help these systems navigate safely using local sensing. Inspired by how neurons move through a cycle of activation, sustained activity, and recovery, NDD represents each agent’s decision-making through nonlinear equations that evolve continuously over time. Each agent tracks its physical motion alongside an internal decision state that determines whether to accelerate or yield. An additional attention state adjusts how strongly the agent commits to that decision. Agents independently monitor the positions and velocities of nearby agents to assess potential conflicts and determine who should proceed first. As a conflict becomes more urgent, a bifurcation, a change in the decision system’s behavior, resolves competing decisions into an order of passage that avoids circular waiting.

NDD is designed to support collision avoidance and continued progress without communication between agents, synchronized clocks, assigned priorities, or central supervision. The framework provides mathematical guarantees of collision-free navigation and freedom from deadlock under specified operating assumptions. It addresses stalemates that can arise in existing collision-avoidance methods, including approaches based on Velocity Obstacles and Control Barrier Functions. Its attention mechanism introduces hysteresis, helping agents maintain a decision despite small changes or noise in sensor readings instead of repeatedly switching between proceeding and yielding. The architecture also adjusts its responses to uncooperative agents in real time. Each agent’s computational workload grows linearly with the number of nearby agents it monitors, supporting use in larger fleets.

Benefits

• Guarantees collision-free navigation and eliminates deadlocks and livelocks in congested multi-agent environments
• Operates fully autonomously without requiring inter-agent communication, centralized supervision, global clocks, or preassigned priorities
• Scales linearly in computational and memory complexity relative to local neighbor count rather than total population size
• Maintains robust decision stability against transient sensor noise and physical perturbations through built-in hysteresis
• Enables energy-efficient, sparse actuation via event-triggered neuromorphic control bursts
• Adapts dynamically in real time to safely yield to non-cooperative or rogue agents
• Outperforms traditional collision-avoidance methods in high-density and contested scenarios

Applications

• Autonomous vehicle intersection management
• Warehouse mobile robot fleet navigation
• Decentralized drone swarm collision avoidance
• Autonomous maritime vessel traffic control
• Communication-free multi-robot environmental monitoring

Additional info

Neuromorphic Decision Dynamics is a decentralized, communication-free multi-agent control architecture. It augments physical kinematics with continuous-time nonlinear decision and attention states governed by sigmoidal saturation and quadratic feedback. Driven by local conflict evidence, pitchfork bifurcations break symmetry from neutral equilibria into distinct accelerating or yielding commands. This nonlinear framework formally guarantees collision-free safety and finite-time deadlock clearance with computational complexity scaling linearly with local neighbors.