Collision Avoidance System CAS

• Collision Avoidance Systems are integrated technologies that combine sensor data, detection algorithms, and maneuver planning to prevent collisions.
• They utilize diverse sensing methods such as ADS-B, radar, and vision to accurately perceive obstacles and assess collision risks.
• Advanced planning strategies like geometric and optimization-based approaches ensure effective, real-time maneuvering while balancing safety and mission goals.

A collision avoidance system (CAS) is an embedded, automated, or semi-automated system that enables a vehicle—such as an unmanned aerial vehicle (UAV), ground vehicle, or other mobile platform—to perceive threats in its operational environment and execute maneuvers that prevent collisions or minimize risk. CAS research spans diverse sensor modalities, detection algorithms, and decision-making frameworks, and is fundamentally structured around two complementary functionalities: perception (sensing and detection) and avoidance maneuvering. The technical literature categorizes and compares a range of CAS designs according to their sensing technology, detection logic, and maneuver strategies, with ongoing innovation in integrated architectures, computational efficiency, real-time performance, and adaptability to complex or uncertain environments (Pham et al., 2015).

1. Core Structure and Taxonomy of Collision Avoidance Systems

Every UAV collision avoidance system consists of two principal modules:

• Sensing and Detection: This segment processes environmental data (e.g., position, bearing, range, and velocity of obstacles or intruders) using hardware sensors and detection algorithms. Its output is the perception of collision threats.
• Collision Avoidance/Maneuver Planning: Once a threat is detected or predicted, this module computes and initiates maneuvers that avoid or mitigate the collision, balancing safety constraints with mission objectives and dynamic feasibility.

The CAS taxonomy is structured along these axes:

Sensing/Detection Category	Description	Noted Limitations
ADS-B (Automatic Dependent Surveillance–Broadcast)	Cooperative aircraft transmit location, altitude, speed, GPS data	Ineffective for non-aircraft obstacles
Visual Sensors	Camera-based (monocular/spherical), interpreted via computer vision	Poor depth/size inference, sensitive to lighting
Radar	Weather-resistant, direct ranging to objects	Bulky equipment, limited suitability for small UAVs
Cooperative Information Sharing	Data fusion from multiple UAVs via communication	Added communication overhead, dependent on sensor accuracy
Assumed Working Sensors	Theoretical/no technology specified; ideal data assumed	Lacks practical constraints

Collision detection is further categorized by the prediction and decision paradigm:

• Trajectory calculation & distance estimation: Computes minimum future distance (d_min) between objects (e.g., via vector algebra).
• Worst-case estimation: Considers all feasible intruder trajectories for safety guarantees; computationally demanding.
• Probabilistic estimation: Incorporates uncertainties as probability distributions, often evaluated with Monte Carlo simulation.
• Act as seen: Direct, reactive response to sensed threats, typically with no explicit future collision prediction.

Maneuver planning strategies are classified as:

Maneuvering Approach	Key Mechanism	Relative Pros/Cons
Geometric Approaches	Uses position/velocity/heading to compute spatial adjustment	Simple but sensor-noise sensitive, often requires cooperation
Optimized Trajectory Approaches	Optimization over discretized grids or cost functions (e.g., A*)	Near-optimal but computationally intensive
Bearing Angle–Based	Keeps obstacles at constant relative angles (vision-driven)	Innovative but vision-limited
Force Field Approaches	Obstacles/goals create artificial potential fields	Intuitive, not scalable for real time on constrained hardware
Other (Radar/Probabilistic)	Niche methods, often using statistical or signal-based analysis	Each has application-specific trade-offs

2. Sensing and Detection Technologies

The sensing layer is central to CAS effectiveness and is implemented with multiple technology paradigms (Pham et al., 2015):

• ADS-B: Facilitates collaborative sensing for aircraft-to-aircraft avoidance. Provides highly accurate positioning among compliant participants but cannot address obstacles lacking transponders (e.g., terrain, birds, non-cooperative UAVs).
• Vision-based Sensors: Employ cameras to infer obstacle bearing, often with computer vision preprocessing. These systems are lightweight, flexible, and enable bearing-based maneuvering but suffer from unreliable depth estimation and degraded performance in low light or adverse weather.
• Radar: Offers all-weather robustness and direct distance/ranging capabilities. However, weight, power, and volume constraints commonly preclude use in small UAVs.
• Cooperative Information Sharing: Extends situational awareness by fusing data across UAV teams via communication protocols, leveraging sensor diversity but introducing latency and dependence on network reliability.
• Assumed Working Sensor: Used primarily in simulation or algorithm development, this framework enables testing under idealized perception conditions but may overstate real-world applicability.

Detection algorithms process sensor inputs to identify and prioritize threats. Trajectory-based schemes (using vector algebra and dynamic prediction) provide interpretable risk metrics but demand high-fidelity state estimates and dynamic modeling. Probabilistic approaches, via Monte Carlo or analytical models, explicitly handle uncertainty at the expense of computational load. Worst-case analysis is invoked for stringent safety—computing all potential future conflicts but at the expense of runtime scalability. "Act as seen" methods, in contrast, are highly reactive and generally suitable for scenarios where detailed prediction is impractical or unnecessary.

3. Maneuver Planning and Avoidance Strategies

Once a collision is detected, the avoidance logic selects and executes the appropriate maneuver. The state-of-the-art literature distinguishes several archetypes (Pham et al., 2015):

• Geometric Approaches: Adjust vehicle trajectory by direct computation of spatial separation vectors based on position/velocity/heading. For example, in 2D, d_min is calculated as the norm of the velocity difference: $$d_\text{min} = \lVert \vec{v}_\text{UAV} - \vec{v}_\text{obstacle} \rVert$$. These methods are computationally efficient but sensitive to sensor error and are typically effective only under coordination (e.g., ADS-B environments).
• Optimized Trajectory Approaches: Formulate avoidance as path planning that minimizes deviation from a reference trajectory while ensuring constraint satisfaction. Methods commonly use grid-based search (A*) or cost functions that account for path length, threat proximity, and dynamic feasibility: $f(n) = g(n) + h(n)$. High computational requirements limit their operational real-time use, particularly when sensor limitations prohibit dense state-space discretization.
• Bearing Angle–Based Approaches: Rely on vision sensors to track objects at fixed relative bearings, resulting in equiangular or spiral avoidance trajectories. Effective in visually structured environments but subject to the same limitations as camera-based sensing.
• Force Field Approaches: Obstacles generate repulsive, and goals generate attractive, artificial potentials. Avoidance derives from following the gradient of the potential field, which requires sampling the local field at each timestep and can be computationally demanding:

$\vec{F}_\text{total} = -\nabla U(\vec{x}),$

where $U$ combines obstacle and goal contributions.

• Other Approaches: These include radar-based minimization (detecting zones of minimum radar return), and probabilistic schemes that select predefined maneuvers based on likelihood estimates derived from sensor uncertainty models.

Systematic comparisons reveal geometric and optimized methods favor predictability and performance at the expense of sensor precision and processor burden, while bearing and force field methods offer more flexible/reactive responses but are fundamentally constrained by environmental and computational factors. Hybridization—combining multiple strategies—emerges as a trend to balance trade-offs.

4. Comparative Assessment and Trends

CAS research is experiencing a dialog between increasing algorithmic sophistication (e.g., optimal, probabilistic planning) and hardware constraints (sensor fidelity, computation). Key trends and findings (Pham et al., 2015) include:

• ADS-B dependent systems excel when all potential obstacles are cooperative aircraft but are ineffective for avoidance of non-cooperative moving objects or terrain.
• Vision-based approaches—including bearing-angle tracking—offer a path to autonomy in unstructured or dynamic environments but face headwinds concerning data reliability and computational cost under variable lighting or weather.
• Optimization and force-field control deliver near-optimal and robust plans, yet often exceed embedded system resource budgets, precluding wide fielded use in small- to mid-size UAV classes.
• Probabilistic and simulation-based detection, notably via Monte Carlo methods, are increasingly used for risk assessment and to characterize and plan for rare, complex encounters—again trading computational resources for improved robustness to uncertainty.
• Emerging hybrid CAS frameworks explore fusing sensor data streams (ADS-B, radar, vision) and algorithmic modules to improve resilience and achieve real-time performance.

A pronounced trajectory toward 3-D environmental modeling, advanced computer vision (e.g., SURF features), and multi-agent sensor fusion is evident. The confluence of these trends points towards more robust, adaptive CAS designs able to negotiate the increased complexity of modern UAV operational theaters.

5. Representative Algorithms and Mathematical Foundations

Multiple algorithmic structures are prevalent (Pham et al., 2015):

• Minimum distance vector subtraction: Direct calculation of closest approach, fundamental to geometric CAS, efficiently implemented with vector algebra on $\mathbb{R}^2$ or $\mathbb{R}^3$ state data.
• Cost function optimization: Abstractly, minimizing $C(\vec{x})$, where $\vec{x}$ encodes current position, goal, and proximity to threats, subject to dynamic and operational constraints.
• A* pathfinding: Used in optimized approaches with discretized grid representations, $f(n) = g(n) + h(n)$.
• Monte Carlo simulation: Samples over uncertain state and prediction models to estimate collision probabilities; statistical outputs feed avoidance triggering logic.
• Force field gradient descent: Iterative adjustment of vehicle heading in response to potential field gradients computed by summing goal attraction and obstacle repulsion contributions.

The balance between closed-form analytic guarantees and stochastic, simulation-based evaluation—particularly for complex or high-uncertainty environments—remains an active field of paper.

6. Future Directions

State-of-the-art surveys and comparative analyses highlight several concrete research priorities (Pham et al., 2015):

• Sensor and algorithm integration: Enhanced multi-modal sensor fusion (e.g., vision, ADS-B, radar) is expected to provide more robust situational awareness, enabling CAS to function reliably despite environmental or sensor-specific failure modes.
• Advanced computer vision: Addressing existing vision sensor limitations, especially under non-ideal lighting, through more robust algorithms and hardware improvements to facilitate bearing or depth estimation.
• Computation reduction: Redesign or approximation of optimized and force-field algorithms to fit within real-time constraints imposed by UAV hardware, possibly leveraging approximation, parallelism, or hardware acceleration.
• Probabilistic/hybrid frameworks: Greater use of probabilistic risk assessment, combined with deterministic and reactive triggers, to improve responsiveness and robustness to uncertainty.
• Improved simulation: High-fidelity simulation tools, potentially enhanced with machine learning, for algorithm validation and risk assessment.
• Standardization and benchmarking: Updating classification frameworks and integrating more recent developments to reflect advances in algorithms, hardware, and hybrid system architectures.

Progress in these areas is anticipated to yield CAS architectures with enhanced real-world robustness, computational efficiency, and flexibility—paramount for next-generation autonomous platforms operating in civil, commercial, and military airspace.