Real-Time Retargeting Using Controllability Boundary for Chandrayaan-3 Lunar Landing

2026-05-28

AI Summary

This work proposes a data-driven, real-time retargeting guidance strategy for lunar landing missions, which was successfully deployed for the first time in an actual mission—Chandrayaan-3—to address scenarios where the nominal landing site becomes infeasible. The approach integrates a near-fuel-optimal descent trajectory with a high-level decision-making mechanism to rapidly redirect the lander toward an alternative safe zone. Feasibility of candidate targets is efficiently assessed using a convex representation of controllability boundaries, enabling swift evaluation without compromising computational tractability. The framework combines convex optimization with real-time trajectory replanning to ensure responsive and robust guidance updates. Both pre-flight simulations and in-flight telemetry from Chandrayaan-3 confirm the effectiveness and reliability of the proposed architecture under operational constraints.

Abstract

This paper presents the real-time retargeting guidance policy developed for the Chandrayaan-3 lunar landing mission. The baseline guidance generates approximate fuel-optimal descent trajectories, while a high-level policy enables safe retargeting to alternate sites when the nominal site becomes infeasible. The retargeting strategy leverages a convex representation of the controllability boundary, allowing rapid feasibility checks and real-time target updates. To the best of the authors knowledge, this represents the first application of a data-driven retargeting framework in an operational lunar landing mission. Pre-flight simulations and Chandrayaan-3 flight results validate the effectiveness of the proposed approach.

Problem

• real-time retargeting
• lunar landing
• controllability boundary
• feasibility check
• landing site selection

Innovation

• real-time retargeting
• controllability boundary
• convex optimization
• lunar landing
• data-driven guidance