678638 event 1732560027 1732560055 Abinay J. Brown
(Advisor: Prof. Kyriakos Vamvoudakis)
will propose a master’s thesis entitled,
Real-Time Vision-Based Hazard Detection with Safe
Trajectory Optimization via Successive Convexification
for Lunar Landing
On
Wednesday, December 3rd at 2:00 p.m.
Montgomery Knight Building 317
Abstract
With the renewed interest in returning to the Moon, achieving autonomous and safe lunar descent and
landing is paramount for the success of future missions. Safe and autonomous descent also reduces the
overhead logistics of pre-planning, surveying, and mapping lunar landing sites. During the descent phase
of a lunar mission, the lander must steer away from rough terrain hazards and descend safely to the
surface while constrained by fuel, maneuverability, and localization uncertainty. This thesis presents a
framework for real-time state estimation, hazard detection from descent imagery, and safetyconstrained
trajectory optimization. The proposed framework navigates using dead reckoning and
Kalman filter-based sensor fusion to track the descent path using accelerometer and altimetry data
while leveraging image segmentation computer vision models to identify lunar hazards such as craters
and hills. Finally, a full-horizon minimum-time trajectory optimization problem with control and terminal
state constraints is approached using successive convexification (SCvx) optimization scheme. Hazardous
regions identified from descent imagery are represented as ellipse-based chance constraints enforced at
the terminal state, ensuring the lander avoids hazards with high confidence despite positional
uncertainty. The use of SCvx enables the decomposition of the nonlinear thrust vectoring dynamics and
constraints into a series of convex subproblems, significantly enhancing computational efficiency and
achieving real-time feasibility. This optimization is performed in a receding-horizon control loop,
allowing the lander to continuously reoptimize and adapt its descent trajectory in response to updated
hazard maps generated from real-time imagery. Simulations of the proposed framework are
implemented in a computer-generated 3D lunar environment to validate the approach.
Committee
 Prof. Kyriakos Vamvoudakis – School of Aerospace Engineering (advisor)
 Prof. Lu Gan– School of Aerospace Engineering
 Prof. Glen Chou– School of Aerospace Engineering

]]> Real-Time Vision-Based Hazard Detection with Safe
Trajectory Optimization via Successive Convexification
for Lunar Landing

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