A Rapid Trajectory Optimization and Control Framework for Resource-Constrained Applications

This paper presents a computationally efficient model predictive control formulation that uses an integral Chebyshev collocation method to enable rapid operations of autonomous agents. By posing the finite-horizon optimal control problem and recursive re-evaluation of the optimal trajectories, minimization of the L2 norms of the state and control errors are transcribed into a quadratic program. Control and state variable constraints are parameterized using Chebyshev polynomials and are accommodated in the optimal trajectory generation programs to incorporate the actuator limits and keepout constraints. Differentiable collision detection of polytopes is leveraged for optimal collision avoidance. Results obtained from the collocation methods are benchmarked against the existing approaches on an edge computer to outline the performance improvements. Finally, collaborative control scenarios involving multi-agent space systems are considered to demonstrate the technical merits of the proposed work.

Robust Proximity Operations using Probabilistic Markov Models

A Markov decision process-based state switching is devised, implemented, and analyzed for proximity operations of various autonomous vehicles. The framework contains a pose estimator along with a multi-state guidance algorithm. The unified pose estimator leverages the extended Kalman filter for the fusion of measurements from rate gyroscopes, monocular vision, and ultra-wideband radar sensors. It is also equipped with Mahalonobis distance-based outlier rejection and under-weighting of measurements for robust performance. The use of probabilistic Markov models to transition between various guidance modes is proposed to enable robust and efficient proximity operations. Finally, the framework is validated through an experimental analysis of the docking of two small satellites and the precision landing of an aerial vehicle.

A Scalable Tabletop Satellite Automation Testbed:Design And Experiments

This paper presents a detailed system design and component selection for the Transforming Proximity Operations and Docking Service (TPODS) module, designed to gain custody of uncontrolled resident space objects (RSOs) via rendezvous and proximity operation (RPO). In addition to serving as a free-flying robotic manipulator to work with cooperative and uncooperative RSOs, the TPODS modules are engineered to have the ability to cooperate with one another to build scaffolding for more complex satellite servicing activities. The structural design of the prototype module is inspired by Tensegrity principles, minimizing the structural mass of the modules frame. The prototype TPODS module is fabricated using lightweight polycarbonate with an aluminum or carbon fiber frame. The inner shell that houses various electronic and pneumatic components is 3-D printed using ABS material. Four OpenMV H7 R1 cameras are used for the pose estimation of resident space objects (RSOs), including other TPODS modules. Compressed air supplied by an external source is used for the initial testing and can be replaced by module-mounted nitrogen pressure vessels for full on-board propulsion later. A Teensy 4.1 single-board computer is used as a central command unit that receives data from the four OpenMV cameras, and commands its thrusters based on the control logic.