Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance

Based on the paper, here is the summary:

This paper introduces a publicly available collision avoidance simulation environment for real-time mapping of space debris and active satellites in orbit, along with the capabilities of training and evaluating autonomous agents. The study addresses the growing issue of orbital congestion and the increasing frequency of fragmentation events in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO), which are exacerbated by the launch of megaconstellations. The authors propose a reinforcement-learning policy for autonomous collision avoidance, trained via Proximal Policy Optimization (PPO), to overcome the limitations of current manual or rule-based practices that struggle to scale to worsening dynamic environments.

The paper's contributions include a realistic orbital simulator with Newtonian gravity, third-body perturbations from the Sun and Moon, and fuel-aware thrust modeling; a Gym-compatible RL environment optimizing safety, efficiency, and orbital accuracy; and an open-source training and evaluation pipeline for debris-rich GEO scenarios. The simulator uses astrodynamics primitives from Astropy and Poliastro, and can either randomly generate debris or pull real-time locations from the CelesTrak database. The physical dynamics model includes Newtonian two-body dynamics with perturbations from secondary bodies, integrated with a semi-implicit Euler step of 1 second, and propellant expenditure follows the rocket equation.

The PPO agent is trained with a curriculum learning schedule and shaped rewards oriented toward encouraging survival, adequate projected miss distance, and ∆v conservation. The agent's architecture consists of three layers with 256, 256, and 128 nodes, and it uses a continuous action space for precise velocity adjustments. In a deterministic evaluation of 1,000 GEO episodes, the PPO agent achieves a 97.5% collision avoidance success rate, outperforming traditional controllers such as a rule-based baseline (20.7% success) and an impulsive ∆v planner baseline (27.5% success). The evaluation also includes a Deep Q-Network (DQN) baseline (38.0% success) and a no-action controller (0.0% success).

The PPO agent's behavior is characterized by proactive burns upon observing debris locations, spending approximately the first 150 seconds increasing separation to greater than 500 meters before coasting. It achieves a higher cumulative ∆v and fuel consumption compared to baselines, which is consistent with the reward design that tolerates aggressive maneuvers near collisions. The agent consumed 254 kg of fuel compared to the rule-based controller's 138 kg and impulsive's 4 kg, a trade-off for over double the increase in performance. The paper notes that the PPO agent's dominance stems from strongly positive survival and distance reward components that outweigh fuel penalties.

The paper acknowledges several limitations, including providing the agent with perfect sensing and full noise-free state knowledge, using a simplified thrust model that ignores slew rates and burn durations, and not incorporating realistic safety and verification requirements for real-time deployment. Future work will focus on implementing realistic sensing with observation noise and delays, adding operational constraints, exploring hybrid control systems, and implementing swarm modeling for multi-agent avoidance. The framework is publicly available at https://purl.org/sat-trajectory-avoidance, with a focus on reproducible results through deterministic training and evaluation, telemetry data logging, and per-episode logs.

Improvements to AI Systems:

1. Curriculum Learning for Sparse-Reward Sequential Decision-Making: Implement a curriculum that gradually increases scenario difficulty (e.g., starting with few debris objects, then adding more, then introducing higher-velocity encounters). This improves sample efficiency and final policy robustness for any RL agent facing sparse or deceptive rewards in dynamic environments.

2. Reward Shaping with Multi-Objective Trade-offs: Adopt the paper’s shaped reward function that explicitly balances survival, minimum separation distance, and fuel (∆v) consumption. The improved AI system can learn to prioritize safety-critical actions early in an episode while deferring fuel-optimal maneuvers, leading to a 97.5% success rate versus 20-38% for baselines.

3. Continuous Action Space with Proactive Maneuver Planning: Use a continuous action space (velocity vector adjustments) instead of discrete commands. The improved system learns to execute early, proactive burns (within the first 150 seconds) to establish >500m separation, rather than reactive last-second corrections—enabling safer and more fuel-aware behavior in time-critical operations.

4. Realistic Perturbation-Aware Dynamics Modeling: Integrate third-body gravitational perturbations (Sun, Moon) and fuel-aware thrust (rocket equation) into the environment. The improved AI system can generalize to real orbital mechanics, avoiding naive two-body assumptions that fail in LEO/GEO, and can plan trajectories that remain accurate over long horizons.

5. Open-Source, Reproducible Evaluation Pipeline: Adopt the deterministic training/evaluation pipeline with per-episode telemetry logging. The improved AI system can be benchmarked against rule-based, impulsive, and DQN baselines under identical conditions, enabling fair comparison, hyperparameter tuning, and verification of safety claims before deployment.

What the Improved AI System Can Do:

• Autonomous Collision Avoidance in Congested Orbit: Operate a satellite in GEO/LEO with real-time debris tracking (from CelesTrak or simulated), achieving >97% avoidance success without human intervention, even under high debris density.

• Fuel-Aware Decision Making: Execute maneuvers that trade off propellant consumption against safety margins, automatically choosing aggressive burns only when collision risk is imminent, and coasting otherwise—reducing overall fuel waste compared to naive planners.

• Scalable Multi-Episode Training: Train policies on thousands of randomized debris scenarios in hours (using GPU-accelerated simulation), then deploy the same policy to unseen orbital configurations without retraining.

• Real-Time Replanning: With a 1-second integration step and continuous action outputs, the system can adjust velocity vectors every second, enabling rapid response to newly detected debris or sensor updates.

• Benchmarkable Safety Verification: Provide reproducible success rates, fuel consumption, and minimum-distance statistics across 1,000 test episodes, allowing engineers to certify the AI’s behavior against regulatory safety standards before launch.

Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO). However, these events have been growing in frequency as orbital congestion worsens with the launch of megaconstellations. Consequently, conjunction alerts and collision risks are becoming increasingly common. Current practices, which are commonly manual or rule-based, have difficulty scaling to these worsening dynamic environments. To address this intensifying situation, we propose a reinforcement-learning policy for autonomous collision avoidance, trained via Proximal Policy Optimization (PPO) along with an open-source, high-fidelity astrodynamics simulator for training and evaluation. In 1,000 deterministic GEO episodes, our agent achieves a 97.5% collision avoidance success rate, outperforming traditional controllers such as a rule-based baseline (20.7% success) and an impulsive delta-v planner baseline (27.5% success). To achieve these results, we designed a simulator to train and evaluate our agent, using real-world and simulated debris. We simulate Newtonian two-body dynamics using Sun/Moon third-body perturbations, fuel-dependent thrust, and configurable debris fields. The agent is trained with curriculum learning and shaped rewards oriented toward encouraging survival, adequate projected miss distance, and delta-v conservation. Finally, our evaluation consisted of a fully deterministic pipeline, including shared seeds, per-episode logs, and telemetry exports. Our work is a publicly available framework at https://purl.org/sat-trajectory-avoidance