CAMmary: A Review of Spacecraft Collision Avoidance Manoeuvre Design Methods

TL;DR

This survey consolidates state-of-the-art CAM methodologies, categorising them into analytic, semi-analytic, and numerical approaches and contrasting impulsive versus low-thrust implementations under deterministic or stochastic modelling. It explicates how uncertainty propagation and PoC/IPoC formulations underpin CAM design and compares multiple methods on a common short-term single-encounter dataset to quantify efficiency, accuracy, and optimality. The work highlights the trade-offs between rapid, closed-form solutions and flexible, constraint-rich optimisation, and underscores gaps in cislunar CAM and thrustless strategies that are critical for scalable autonomous operations. Collectively, the findings guide the development of autonomous collision avoidance pipelines that balance speed, robustness, and safety in increasingly congested and diverse orbital regimes.

Abstract

Ensuring safety for spacecraft operations has become a paramount concern due to the proliferation of space debris and the saturation of valuable orbital regimes. In this regard, the Collision Avoidance Manoeuvre (CAM) has emerged as a critical requirement for spacecraft operators, aiming to efficiently navigate through potentially hazardous encounters. Currently, when a conjunction is predicted, operators dedicate a considerable amount of time and resources to designing a CAM. Given the increased frequency of conjunctions, autonomous computation of fuel-efficient CAMs is crucial to reduce costs and improve the performance of future operations. To facilitate the transition to an autonomous CAM design, it is useful to provide an overview of its state-of-the-art. In this survey article, a collection of the most relevant research contributions in the field is presented. We review and categorize existing CAM techniques based on their underlying principles, such as (i) analytic, semi-analytic, or numerical solutions; (ii) impulsive or continuous thrust; (iii) deterministic or stochastic approaches, (iv) free or fixed manoeuvring time; (v) free or fixed thrust direction. Finally, to determine the validity of the algorithms potentially implementable for autonomous use, we perform a numerical comparison on a large set of conjunctions. With this analysis, the algorithms are evaluated in terms of computational efficiency, accuracy, and optimality of the computed policy. Through this comprehensive survey, we aim to provide insights into the state-of-the-art CAM methodologies, identify gaps in current research, and outline potential directions for future developments in ensuring the safety and sustainability of spacecraft operations in increasingly congested orbital environments.

Figures (7)

• Figure 1: Graphical representation of short- (left) and long-term (right) encounters.
• Figure 2: B-plane construction (adapted from Ref. Armellin2021). $v_{rel}$ and $r_{rel}$ are the relative velocity and the position of the primary in the B-plane, respectively.
• Figure 3: Distribution of simulation time.
• Figure 4: MD: Distribution of $\Delta v$s.
• Figure 5: PoC: Distribution of $\Delta v$s.