Onboard trajectory generation for the unpowered landing of autonomous Reusable Launch Vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.Includes bibliographical references (p. 167-168).Onboard trajectory generation dispenses with the pre-defined trajectories used. in Reusable Launch Vehicle (RLV) guidance since the early days of the Shuttle era. This shift, enabled by a new breed of algorithms harnessing modern computer power, offers improvements in performance, robustness, operational cost, and safety. This thesis develops a set of algorithms providing onboard trajectory generation for low lift-over-drag gliding RLVs in subsonic flight below 40,000 ft. The NASA/Orbital Sciences X-34 is used as a representative model for which feasible trajectories are designed over a range of initial conditions without human intervention. In addition to being autonomous, the guidance output of the onboard trajectory generator differs from current Shuttle-based approaches, providing a realistic "future history" in a propagated plan, rather than output commands reacting to perceived instantaneous vehicle needs. Hence, this approach serves an enabling role in a larger research effort to develop a next generation guidance system using an integrated control function. To assess feasibility, the onboard trajectory generator is benchmarked against traditional X-34 guidance for a drop test scenario. The results match in basic form, with differences showcasing the autonomous algorithms' preference for maximum robustness. The true strength of the onboard trajectory generator lies in its ability to handle off-nominal conditions. A series of test cases highlight the ability of the algorithms to effectively cope with anomalous initial drop conditions, reach the desired terminal states, and provide maximum late-trajectory robustness. Computation time is sufficiently brief to suggest a real-time application, after straightforward improvements are made.by André R. Girerd.S.M

Deep Drone Racing: From Simulation to Reality with Domain Randomization

Dynamically changing environments, unreliable state estimation, and operation under severe resource constraints are fundamental challenges that limit the deployment of small autonomous drones. We address these challenges in the context of autonomous, vision-based drone racing in dynamic environments. A racing drone must traverse a track with possibly moving gates at high speed. We enable this functionality by combining the performance of a state-of-the-art planning and control system with the perceptual awareness of a convolutional neural network (CNN). The resulting modular system is both platform- and domain-independent: it is trained in simulation and deployed on a physical quadrotor without any fine-tuning. The abundance of simulated data, generated via domain randomization, makes our system robust to changes of illumination and gate appearance. To the best of our knowledge, our approach is the first to demonstrate zero-shot sim-to-real transfer on the task of agile drone flight. We extensively test the precision and robustness of our system, both in simulation and on a physical platform, and show significant improvements over the state of the art.Comment: Accepted as a Regular Paper to the IEEE Transactions on Robotics Journal. arXiv admin note: substantial text overlap with arXiv:1806.0854

Aggressive Quadrotor Flight through Narrow Gaps with Onboard Sensing and Computing using Active Vision

We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap

Analysis of navigation performance for the Earth Observing System (EOS) using the TDRSS Onboard Navigation System (TONS)

Use of the Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) was proposed as an alternative to the Global Positioning System (GPS) for supporting the Earth Observing System (EOS) mission. The results are presented of EOS navigation performance evaluation with respect to TONS based orbit, time, and frequency determination (OD/TD/FD). Two TONS modes are considered: one uses scheduled TDRSS forward link service to derive one way Doppler tracking data for OD/FD support (TONS-I); the other uses an unscheduled navigation beacon service (proposed for Advanced TDRSS) to obtain pseudorange and Doppler data for OD/TD/FD support (TONS-II). Key objectives of the analysis were to evaluate nominal performance and potential sensitivities, such as suboptimal tracking geometry, tracking contact scheduling, and modeling parameter selection. OD/TD/FD performance predictions are presented based on covariance and simulation analyses. EOS navigation scenarios and the contributions of principal error sources impacting performance are also described. The results indicate that a TONS mode can be configured to meet current and proposed EOS position accuracy requirements of 100 and 50 m, respectively

Hypersonic Research Vehicle (HRV) real-time flight test support feasibility and requirements study. Part 2: Remote computation support for flight systems functions

The requirements are assessed for the use of remote computation to support HRV flight testing. First, remote computational requirements were developed to support functions that will eventually be performed onboard operational vehicles of this type. These functions which either cannot be performed onboard in the time frame of initial HRV flight test programs because the technology of airborne computers will not be sufficiently advanced to support the computational loads required, or it is not desirable to perform the functions onboard in the flight test program for other reasons. Second, remote computational support either required or highly desirable to conduct flight testing itself was addressed. The use is proposed of an Automated Flight Management System which is described in conceptual detail. Third, autonomous operations is discussed and finally, unmanned operations