# Chance-Constrained Belief-Space Maneuver Planning for Autonomous Collision Avoidance Under Uncertainty

arXiv: https://arxiv.org/abs/2609.13428 · HTML: https://mlsanity.com/paper/2609.13428/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 11 Sep 2026.

## Builds on (most cited first, 8 of 8)

- [Online algorithms for POMDPs with continuous state, action, and observation spaces](https://mlsanity.com/paper/1709.06196/) (2017)
- [ConstrainedZero: Chance-Constrained POMDP Planning using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints](https://mlsanity.com/paper/2405.00644/) (2024)
- [Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach](https://mlsanity.com/paper/2310.18966/) (2023)
- [Risk-Sensitive Orbital Debris Collision Avoidance using Distributionally Robust Chance Constraints](https://mlsanity.com/paper/2412.17358/) (2024)
- [Markov Decision Processes for Satellite Maneuver Planning and Collision Avoidance](https://mlsanity.com/paper/2501.02667/) (2025)
- [Chance-Constrained Control for Safe Spacecraft Autonomy: Convex Programming Approach](https://mlsanity.com/paper/2403.04062/) (2024)
- [A Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance](https://mlsanity.com/paper/2508.05876/) (2025)
- [Semi-Decentralized Multi-Spacecraft Collision Avoidance under Communication Constraints](https://mlsanity.com/paper/2607.26570/) (2026)

## Abstract (arXiv)

Increasing conjunction frequency in low Earth orbit places growing pressure on spacecraft operators to determine not only whether an encounter requires mitigation, but whether sufficient information is available to commit to a maneuver. This work formulates this information-action tradeoff as a belief-space planning problem for conjunctions between a maneuverable spacecraft and an unmaneuverable secondary object. The planner represents the uncertain orbital states as Gaussian beliefs and uses a chance-constrained belief-space Monte Carlo tree search framework to reason over possible future tracking updates before time of closest approach (TCA). A terminal chance constraint limits the probability of reaching TCA above a prescribed collision-risk threshold, allowing the planner to wait for informative tracking while intervening when deferral becomes too risky. We evaluate the approach on eight historical conjunctions from NASA's Conjunction Assessment Risk Analysis dataset. By varying the secondary-object measurement quality and tracking cadence, we generate a total of 96 distinct evaluation scenarios. Across the evaluated conditions, the planner reaches TCA without maneuvering in approximately 40% of episodes while maintaining no terminal collision-risk violations. In contrast, fixed-time rule-based maneuver policies resolve more encounters without maneuvering when intervention is deferred closer to TCA, but at the expense of increasing terminal risk violations. The fraction of episodes reaching TCA without maneuvering depends strongly on tracking quality and measurement cadence, ranging from 76% under accurate, frequent measurements to approximately 18%-20% under the poorest tracking conditions. These results show that tracking quality and frequency are not only inputs to collision-risk estimation: they can determine when intervention becomes necessary.