Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning with Refueling and Adaptive Collision Avoidance

This study proposes a masked Proximal Policy Optimization (PPO) reinforcement learning framework that optimizes fuel-efficient, adaptive collision avoidance and refueling strategies for small satellites conducting multi-debris active removal missions, demonstrating superior performance over traditional heuristic approaches in complex orbital environments.

The Gist

Imagine Earth's orbit as a busy, chaotic highway in space. Over the years, thousands of old satellites and chunks of metal (space junk) have piled up, creating a dangerous traffic jam. If a satellite crashes into this junk, it creates even more debris, leading to a chain reaction that could make space travel impossible for decades. This is known as the "Kessler Syndrome."

To fix this, we need "Active Debris Removal" (ADR) missions. Think of these as space tow trucks designed to grab these pieces of junk and drag them out of the way. But here's the problem: The highway is moving fast, the traffic is unpredictable, and the tow truck has a limited tank of gas.

This paper presents a new way to plan these missions using a "smart brain" called Reinforcement Learning (RL). Instead of using old, rigid rules, the researchers taught a computer agent to learn how to drive this space tow truck through trial and error, just like a video game character learning to beat a level.

Here is how their system works, broken down into simple concepts:

1. The "Smart Driver" (The AI Agent)

The researchers created a digital agent that acts as the mission planner. Instead of following a pre-written map, this agent learns by playing the game millions of times.

• The Goal: Visit as many pieces of junk as possible before running out of fuel or time.
• The Challenge: The "traffic" (other debris) can suddenly appear in the path, creating a danger zone. The agent must decide: "Do I go straight, do I take a detour, or do I stop to get gas?"

2. The Three Big Moves

The agent has to make three types of decisions, and it does them all at once:

• Picking the Next Target: Which piece of junk should I visit next? The agent learns the most efficient order to visit them, similar to a delivery driver figuring out the best route to drop off packages without backtracking.
• Refueling: The tow truck can't go forever. The agent learned that it can stop at a "gas station" (a refueling point), but only after it has successfully picked up at least one piece of junk. It learned to balance stopping for gas (which takes time) against the risk of running out of fuel.
• Dodging Danger: Sometimes, a new piece of junk appears right in the path. The agent learned to instantly perform a "dodge maneuver." It can steer slightly higher or slightly lower (like changing lanes on a highway) to go around the danger zone while keeping a safe 5-kilometer distance.

3. The "Masked" Brain

One of the clever tricks in this paper is something called a "Masked" algorithm.
Imagine you are playing a game where you can only choose from the buttons that are lit up. If a button is broken or illegal, it stays dark.

• In this system, the AI is "masked" so it can't make illegal moves. It physically cannot choose to visit a piece of junk it has already picked up, or try to refuel before it's allowed to. This stops the AI from wasting time learning bad habits and helps it learn faster.

4. The Results: How Did It Do?

The researchers tested this "Smart Driver" against older, simpler methods (like a robot that just picks the closest junk without thinking ahead).

• The Old Way: The simple robots often got stuck in traffic, ran out of gas, or crashed because they didn't plan for the future.
• The New Way: The Reinforcement Learning agent was much better. It visited more pieces of junk, avoided collisions more often, and managed its fuel much more efficiently. It learned to be flexible, changing its route instantly when a new danger appeared.

The Bottom Line

This paper shows that we can teach computers to be better space traffic managers than we can with old, rigid rules. By letting an AI learn through practice, we can send small, agile satellites to clean up space junk more safely and efficiently.

What the paper does NOT claim:

• It does not say this technology is currently flying on a real satellite tomorrow.
• It does not claim this will solve all space problems immediately.
• It focuses strictly on the planning and simulation of these missions, proving that this "smart brain" approach works better than traditional math-based planning in a computer simulation.

In short, the authors built a virtual training ground where an AI learned to be a master space janitor, and it proved to be much smarter than the old ways of doing things.

Technical Summary

Technical Summary: Optimizing Mission Planning for Multi-Debris Rendezvous Using Reinforcement Learning

1. Problem Statement

The paper addresses the critical challenge of Active Debris Removal (ADR) in Low Earth Orbit (LEO), where the increasing density of cataloged objects and sub-centimeter fragments creates a high risk of in-orbit collisions. The specific problem is formulated as a sequential decision-making problem under uncertainty, akin to a modified Dynamic Traveling Salesman Problem (TSP).

The objective is to determine an optimal sequence of maneuvers for a single small satellite to rendezvous with multiple debris targets while satisfying strict constraints:

• Limited Resources: Finite fuel and time budgets.
• Dynamic Hazards: Probabilistic collision risks (modeled as cuboidal danger zones) that can appear during transfers.
• Operational Complexity: The need to integrate refueling strategies (which extend mission lifespan but incur costs) and adaptive collision avoidance (requiring trajectory replanning).

Traditional approaches, such as brute-force enumeration or greedy heuristics, are deemed insufficient. Brute-force methods are computationally infeasible for complex missions, while greedy heuristics often yield suboptimal solutions by failing to anticipate future constraints or dynamic collision risks.

2. Methodology

The authors propose a Reinforcement Learning (RL) framework utilizing a Masked Proximal Policy Optimization (PPO) algorithm. The problem is modeled as a Markov Decision Process (MDP).

A. State and Action Space

• State Representation ($s_t$): Includes the spacecraft's position, velocity, and normalized fuel level; a binary mask indicating visited debris; relative Keplerian elements of all debris targets; distance to refueling stations; refueling eligibility flags; and collision risk proximity vectors.
• Action Space ($A$): A discrete, masked action space comprising:
  • Rendezvous: Selecting an unvisited debris target ($d_j$) that is currently outside danger zones.
  • Refueling: Executing a refuel action (only permitted if the agent has visited at least one debris).
  • Collision Avoidance (CA): Selecting "CA Above" or "CA Below" maneuvers when a planned transfer arc intersects a probabilistically triggered danger zone.

B. Core Algorithm: Masked PPO

The framework employs PPO for its stability in continuous control domains. A key innovation is the application of invalid action masking. Before the softmax layer, logits for invalid actions (e.g., visiting already visited debris or refueling without eligibility) are set to $-\infty$. This dynamically restricts the action space at each timestep, ensuring the agent only considers feasible actions based on the current mission state.

C. Simulation Environment

• Dynamics: Uses Hohmann transfer arcs for fuel-efficient rendezvous and patched-conic approximations for collision avoidance.
• Collision Modeling: Danger zones (5×5×5 km) are triggered with a 33% probability upon selecting a target. If a trajectory intersects a zone, the agent must replan using elliptical detours ("CA Above" or "CA Below") maintaining a 5 km clearance.
• Refueling Logic: Agents can refuel only after visiting at least one debris. Premature refueling is penalized.
• Training: The agent is trained over 10 million steps using Stable-Baselines3 with distributed sampling and entropy regularization. Scenarios are derived from the Iridium 33 debris dataset.

D. Reward Function

The agent maximizes long-term cumulative rewards defined by:
$$r_t = \delta_{visit} - C_t - T_{penalty}$$
Where $\delta_{visit}$ is a reward for visiting new debris, $C_t$ is a penalty for collisions, and $T_{penalty}$ is a penalty for exhausting fuel or time.

3. Key Contributions

The paper identifies four primary contributions:

1. Adaptive Collision Avoidance: Implementation of probabilistically triggered cuboidal risk zones that force the agent to replan using detour maneuvers with a minimum 5 km clearance.
2. Integrated Refueling Logic: Embedding refueling as a learnable decision checkpoint that extends mission lifespan but is penalized if used prematurely.
3. Fuel-Efficient Transfers: Utilization of Hohmann maneuvers and ellipse-based avoidance arcs for trajectory generation.
4. Custom Reward Shaping: A reward structure that balances mission efficiency, safety, and full debris coverage.

4. Results and Evaluation

The framework was evaluated against four distinct planning modes over 100 unique test cases:

1. RL-RL: RL handles both sequencing and collision avoidance.
2. RL-Greedy: RL handles sequencing; a deterministic greedy planner handles avoidance.
3. Greedy-RL: A greedy heuristic handles sequencing; RL handles avoidance.
4. Greedy-Greedy: Both sequencing and avoidance are handled by heuristics.

Key Findings:

• Performance: The RL-RL configuration achieved the highest average debris coverage (approx. 30.4 debris per mission in the test case) compared to hybrid and greedy baselines (which ranged from 19.3 to 29.5).
• Robustness: The RL agent demonstrated the ability to adapt to dynamic hazards. In case studies, the agent successfully replanned routes when collision risks were triggered, maintaining mission completion within constraints.
• Convergence: Training showed a steep reward gain in the first 1–2 million steps, stabilizing after 8 million steps, indicating policy convergence.
• Comparison: Hybrid strategies relying on greedy heuristics for sequencing underperformed due to an inability to anticipate future constraints. Conversely, using RL solely for avoidance (Greedy-RL) failed to match the performance of full RL, highlighting the importance of learning the visitation sequence globally.

5. Significance and Claims

The paper claims that this research provides a practical and scalable solution for planning complex ADR missions targeting multiple debris.

• Beyond Heuristics: The study demonstrates that RL-based decision-making outperforms traditional heuristic approaches in both safety compliance and mission completeness (debris coverage).
• Generalizability: While focused on ADR, the authors state the framework offers broader applicability to other multi-target rendezvous scenarios, such as on-orbit servicing, collaborative inspection, and asteroid sample return campaigns.
• Autonomy: The work validates RL as a viable method for next-generation autonomous space operations, capable of handling resource constraints and probabilistic environmental hazards in real-time.

The authors conclude that the framework's ability to jointly learn visitation sequences, refueling logic, and collision avoidance makes it a robust tool for future missions in increasingly congested orbital environments.