---
title: Behavior-Based COLREGs Collision Avoidance
url: https://www.emergentmind.com/topics/collision-regulations-colregs-behavior-based-approach
type: topic
---

# Behavior-Based COLREGs Collision Avoidance

A behavior-based approach to COLREGs (International Regulations for Preventing Collisions at Sea) compliance relies on explicit modeling, classification, and execution of collision-avoidance maneuvers that mimic human seafaring rules in autonomous surface vehicles (ASVs) and other maritime robotics. This paradigm encompasses algorithmic frameworks that represent COLREGs as a hierarchy of behavioral protocols, typically realized through rule-based arbitration, cost function shaping in optimization routines, local replanning mechanisms, or direct embedding in control primitives. The approach differs from pure kinematic or physical modeling by incorporating discrete rule logic—such as give-way, stand-on, overtaking, or head-on situations—into the real-time navigation pipeline, ensuring not just geometric collision avoidance but explicit rule compliance for multi-agent and mixed human-autonomous environments.

## 1. Core Principles and Motivations

Behavior-based COLREGs strategies embed legal and social conventions of maritime navigation into the core architecture of collision avoidance algorithms. Fundamental principles include:

- Explicit mapping of encounter types (overtaking, head-on, crossing, stand-on/give-way) to formal rule representations (e.g., COLREGs Rules 13–17).
- Hierarchical or distributed arbitration, where ship-to-ship geometry determines which behavioral protocol is activated.
- Local, interpretable maneuver generation aligned with human-expectant actions, yielding social compliance and predictability.
- Modular composition, enabling integration with lower-level trajectory generation, optimization, or safety filters.

This approach is motivated by the recognition that geometric collision avoidance is insufficient in dense, human-interactive waterways, and that scalable, interpretable, and verifiable algorithms must directly reflect seafaring conventions [2312.08549], [2202.12069], [2109.00862], [2504.19247], [2601.11335].

## 2. Algorithmic Realizations: Architectures and Data Structures

### Two-Phase Conflict-Resolution (COMCORE)

The COLREGs-Compliant Conflict-Resolving (COMCORE) strategy exemplifies a layered behavior-based approach [2312.08549]. It features:

- **Phase 1:** Independent shortest-path plans per agent using A* search on a discrete workspace grid.
- **Phase 2:** Identification and classification of conflicts with behavioral replanning:
  - Conflicts are characterized as head-on or crossing by analyzing joint paths on a 3×3 grid.
  - Labeling as give-way or stand-on is based on relative vessel positions per COLREGs Rule 15.
  - Local waypoint insertions realize protocol-compliant maneuvers (e.g., anti-clockwise detours for both head-on vessels, asymmetric detours for give-way vessels in crossing).

A solution tree maintains candidate joint plans, updating paths locally and validating for new conflicts in a recursive loop.

### Optimization-based and Continuous Behavior Encoding

Alternative strategies encode COLREGs compliance as shaped cost functions within model predictive control (MPC) frameworks [2202.12069], where Gaussian-shaped “behavioral costs” penalize violation of rule-specific zones, e.g., port or starboard sectors, priority vessels, or overtaking corridors. In this paradigm:

- The relative bearing determines which regulation cost activates (head-on, crossing, or overtaking).
- The continuous cost landscape is parameterized so that rule-favored behaviors become cost-minimizing, ensuring social navigation even in dense or canal environments [2202.12069].

### Hybrid and Sampling-based Planning

The hybrid VORRT-COLREGs algorithm combines rapidly-exploring random trees (RRT) with Velocity Obstacles (VO) [2109.00862]:

- RRT proposes candidate waypoints, while VO-based filtering prunes any extensions that violate collision or rule-compliance constraints (e.g., minimum Closest Point of Approach (CPA), required starboard detours).
- Each extension is forward-simulated and checked for admissibility under rule-specific angular bounds or motion constraints expressed by the COLREGs.

### Control Barrier Function Approaches

Recent advances formulate behavior-based COLREGs compliance as switching sets of control barrier functions (CBFs) mapped to encounter types [2504.19247], [2601.11335]:

- For each dynamic scenario (e.g., head-on, crossing from starboard), a CBF encodes the correct avoidance direction (left/right turn), integrated as a linear constraint into a quadratic program enforcing collision-free, rule-compliant local control.
- In distributed multi-ASV settings, behavioral layers (e.g., MOOS-IvP colregs_bhv) synthesize nominal commands, which are then filtered by CBF constraints to guarantee safety even when other vessels are non-cooperative [2601.11335].

## 3. Formal Rule Encoding, Encounter Classification, and State Machines

Behavior-based methods require precise mathematical and computational representations of COLREGs situations. Typical components are:

- **Encounter Type Classification:** Using relative bearing, heading, and CPA/TCPA thresholds to partition space into head-on, overtaking, crossing, stand-on, give-way, and emergency/ambiguous modes [2312.08549], [1907.00039], [1907.00198].
- **Behavior Arbitration Automata:** Band-structure or finite-state machines governing transition among behavior modules (e.g., stand-on, give-way, evasive action), with explicit handling of rule inversion in restricted contexts such as Rule 9 (narrow channels) [2207.08227]. The arbitration resolves conflicts between multiple simultaneous rules and handles changing situational geometry.
- **Rule-to-Behavior Libraries:** For each rule, a corresponding maneuver primitive is specified—e.g., starboard turns for head-on, port detours for overtaking, unaltered course for stand-on [2312.08549], [2109.00862]. Some methods enforce compliance by soft constraints through cost shaping, others as hard constraints via trajectory library selection or QP safety filters.

## 4. Integration with Optimization, Machine Learning, and Probabilistic Inference

Behavior-based COLREGs compliance can be embedded in several algorithmic substrates:

### Model Predictive Control (MPC)

- **Penalty Shaping:** Rule-specific penalty regions (often elliptical or sector-based around moving obstacles) are constructed to reflect correct passing maneuvers [2202.12069], [1907.00039], [1907.00198].
- **Constraint Enforcement:** Discrete COLREGs state variables can be held fixed or switched as discrete modes in multiphase or receding-horizon optimizations [2012.12145], [1907.00198]. Transitions are governed by encounter classifiers and finite-state diagrams.

### Learning-Based and Probabilistic Approaches

- **Deep Reinforcement Learning (DRL):** COLREGs are encoded in reward shaping (e.g., negative penalty for entering wrong sectors or failing to maneuver early) or as hard constraints via action masking with formal temporal logic [2112.00115], [2402.08502], [2006.09540].
- **Dynamic Bayesian Networks (DBN):** Probabilistic models infer the intention of other vessels, conditioning trajectory predictions and maneuver compatibility on inferred rule-compliance and route/grounding hazards [2504.00731].
- **LSTM-based Intention Models:** Learned classifiers predict whether obstacles will pass port or starboard, allowing the ASV to plan active information-seeking and rule-consistent maneuvers under high uncertainty [2411.01011].
- **Large Language Models (LLMs):** LLMs (e.g., GPT-4) interpret input state (risk indices, relative heading, CPA/TCPA) and generate interpretable, text-based rule-compliant maneuver commands, integrated into lower-level trajectory and control modules [2411.16587].

## 5. Representative Workflows and Pseudocode Patterns

Behavior-based COLREGs compliance algorithms typically follow the workflow:

1. **State Estimation:** Fusion of sensor data to build an up-to-date state representation (ownship, obstacles, static map) [2204.12906].
2. **Encounter Classification:** For each obstacle, compute geometric/motion features, classify the encounter, and label the rule in effect.
3. **Behavior Arbitration:** Evaluate predicates for rule activation and select the applicable behavior module (e.g., keep course, alter course starboard, evasive maneuver) based on a priority scheme.
4. **Maneuver or Trajectory Generation:** Use rule-labeled collision avoidance/replanning to produce waypoints, control signals, or QP optimization with behavior-specific constraints.
5. **Execution and Validation:** Apply the chosen control action, validate for new conflicts, and repeat.

This workflow can be realized in a combination of event-driven (discrete) automata, continuous control updates, or sampling-based planning cycles [2312.08549], [2109.00862], [2204.12906].

## 6. Performance, Validation, and Limitations

Empirical validation across a range of environments and agent counts demonstrates that behavior-based COLREGs compliance strategies:

- Achieve zero collision incidents and high command compliance rates in structured and unstructured simulations, and in real-world multi-vessel and adversarial scenarios [2312.08549], [2601.11335], [2109.00862].
- Scale efficiently to up to 10–15 vessels in general grid- or sampling-based planners, with sub-second planning times and negligible increase in cost or detour length relative to unregulated path planners [2312.08549], [2109.00862], [2504.19247].
- Preserve socially compliant trajectories and “human-like” decisiveness; however, limitations include the need for discrete scenarios (ignoring physical dynamics in some implementations), limited coverage of some COLREGs rules (e.g., overtaking in COMCORE), and the requirement of accurate state information and synchronized timing [2312.08549], [2504.19247], [2601.11335].
- Extensions in recent work include intent-aware behavior under uncertainty [2411.01011], integrated probabilistic inference and learning [2504.00731], and explainable decision-making via LLMs [2411.16587].

## 7. Prospects and Open Research Directions

Emergent themes in state-of-the-art behavior-based COLREGs compliance research include:

- Extension to more complex rule sets (overtaking, restricted visibility), multi-ship roundabouts, and high-density traffic [2312.08549], [2109.00862].
- Seamless integration with learning-based methods for intent inference, uncertainty quantification, and data-driven prediction of non-cooperative agents [2411.01011], [2504.00731].
- Real-time formal guarantees of safety, correctness, and completeness via hybrid CBF and temporal logic [2402.08502], [2504.19247], [2601.11335].
- Robustness in the presence of imperfect sensing, reactive human operators, and heterogeneous platform dynamics [2601.11335].
- Enhanced explainability and transparency using LLM-driven or interpretable symbolic encodings [2411.16587].
- Generalization to restricted domains (narrow channels, grounding hazards), dynamic route planning, and context-dependent rule inversion [2207.08227], [2504.00731].

The behavior-based approach thus provides a modular, interpretable, and extensible recipe for embedding navigational rules in ASV systems, directly supporting scalability to realistic, congested, and socially critical marine domains.

Source: https://www.emergentmind.com/topics/collision-regulations-colregs-behavior-based-approach