Autonomous Rendezvous and Proximity Operations (RPO) Workflow

Traditional RPO requires continuous ground station coverage and step-by-step human approval, creating a ~20-minute command loop for lunar distances and hours for Mars. A custom multi-agent workflow fuses LiDAR, visual, and relative GPS data onboard to make millisecond navigation corrections, enabling operations in communication blackouts and reducing the critical approach phase from days to hours.

Manual trajectory planning is conservative, leading to fuel-inefficient, multi-burn approaches. An autonomous workflow uses model-predictive control (MPC) agents to compute fuel-optimal paths in real-time, continuously adjusting for navigation uncertainties. This minimizes propellant use—a direct cost saving—while the integrated Conjunction Assessment and Collision Avoidance agent actively monitors and plans evasive maneuvers, slashing the risk of a catastrophic mission-ending collision.

The high cost and risk of human-tended RPO have constrained the market for satellite servicing, inspection, and assembly. A production-grade autonomous RPO workflow is the foundational enabler for scalable, economically viable services like life-extension, debris removal, and modular space station construction. It turns a bespoke, one-off engineering feat into a repeatable product feature.

A single RPO campaign can tie up a dedicated flight control team and premium deep-space network assets for weeks. Autonomous execution offloads the continuous monitoring and micro-managing to onboard agents, requiring ground teams only for high-level supervision and contingency support. This reallocates scarce engineering talent and reduces millions in annual operational overhead for constellation operators and agencies.

Final approach and soft-docking are sensitive to last-minute anomalies like a faulty sensor or unexpected plume impingement. A custom workflow architectures fault detection, isolation, and recovery (FDIR) agents directly into the control loop. If a primary navigation sensor fails, the system seamlessly transitions to a fused backup solution or executes a safe abort-and-hold, preserving both the chaser and target assets—a critical risk mitigation for high-value missions.

Building a custom RPO workflow isn't a one-mission cost. The core architecture—sensor fusion engine, relative navigation Kalman filter, MPC agent, and secure command interpreter—becomes a reusable autonomy platform. This allows subsequent missions, from lunar logistics to asteroid rendezvous, to inherit proven capabilities, slashing development time, cost, and risk for future programs.

This core agent fuses data from lidar, optical cameras, and GPS-relative signals to maintain a centimeter-accurate, six-degree-of-freedom state estimate of the target. It continuously validates sensor health, switches between navigation modes (e.g., from far-field to close-in), and outputs the relative pose and velocity to the guidance agent. Implementation requires Kalman filters or factor graphs running on radiation-hardened compute, with failover logic to handle sensor dropout.

Using the state estimate, this agent calculates a fuel-optimal, collision-free approach trajectory. It models complex constraints like keep-out zones, plume impingement, and solar array obstructions. The agent replans in real-time based on new navigation data or contingency triggers (e.g., unexpected target motion). Built on convex optimization or model-predictive control libraries, it outputs thrust vectors and attitude targets to the flight control agent.

This low-level control agent translates guidance commands into precise thruster firings and reaction wheel torques. It manages actuator constraints, handles control allocation across redundant thrusters, and executes closed-loop tracking of the trajectory. The agent includes fault detection and isolation (FDI) to identify a failed thruster and reconfigure control logic on the fly. Implementation integrates with the spacecraft's flight software bus via a well-defined API.

The supervisory agent that sequences the entire RPO workflow through phases (far-range, close-in, final approach, docking). It monitors the health and confidence scores of all sub-agents, manages approval gates (e.g., 'hold' points), and executes predefined contingency scripts for anomalies like loss of navigation or unexpected obstacle. Built as a state machine (e.g., using LangGraph or NASA's Core Flight System), it provides the critical audit trail and the interface for ground override commands.

A dedicated perception agent continuously analyzes visual and lidar data of the target vehicle to detect anomalies like unexpected deployment (e.g., a solar array not in the expected position), debris, or signs of damage. It provides a 'go/no-go' confidence score to the Mission Manager. This agent uses lightweight convolutional neural networks or classical computer vision pipelines trained on synthetic and historical rendezvous imagery to identify out-of-family conditions.

A custom RPO stack is not just flight software. It requires a ground-based digital twin and automated testing pipeline. This component involves high-fidelity simulation (using tools like GMAT or STK) to run millions of Monte Carlo tests across varying initial conditions and fault injections. Agents are first validated in software-in-the-loop (SIL) and hardware-in-the-loop (HIL) benches against the digital twin before flight. This pipeline is essential for certifying the autonomous workflow and quantifying its residual risk.