Small Sat Stack A New Era of Open Source Missions

With the advent of open source small satellite software, as well as space-grade Commercial off-the-shelf (COTS) parts, the barrier to entry for new and educational missions has been greatly reduced. Case studies are now available on space programs currently utilizing some, or solely open source components, with documented challenges and opportunities. Of particular interest is NASA’s open source flight software, F Prime, well suited for small satellites and systems. By analyzing and spotlighting trends in mission development, features can be preemptively developed by the community at large, and promote greater collaboration among spacefaring institutions. A free, open web platform to serve as a resource for the small satellite community, with compatibility checking and an intuitive way to share in-progress builds for review could accelerate development. To prevent bias, industry manufacturers could be consulted for public part specifications, and the majority of data could come solely from flight heritage and retrospectives. Distinguishing features of this resource should include support for decentralization, international licensing considerations, flight heritage, ground station capabilities, hardware and software compatibilities, and open source availability for ongoing support. Additionally, a professional network could be fostered to support new ties between industry and academia, allowing for a longer term vision of talent development

A Low-Cost Attitude Determination and Control System and Hardware-in-the-Loop Testbed for CubeSats

The attitude determination and control system (ADCS) for a satellite is responsible for multiple key roles in a satellite’s mission, including detumbling the satellite after deployment, pointing payload sensors, and orienting antennas and solar panels for effective communication and power generation. Designing an effective ADCS is crucial to a mission’s success; however, current methods often rely on actuators and sensors that are bulky and expensive, such as reaction wheels and star trackers. While these systems can provide high accuracy, they often cannot be used on CubeSats due to volume, weight, and cost restrictions. This work builds upon PyCubed, a radiation-tolerant avionics platform for CubeSats that is programmable entirely in Python, by adding a low-cost, open-source attitude determination and control system that is scalable to smaller spacecraft like 1U CubeSats. This system relies on simple consumer-grade magnetometers, gyroscopes, and sun sensors to estimate the orientation of the satellite, along with a set of magnetic torque coils for actuation. By combining these low-cost sensors and actuators with sophisticated calibration, estimation, motion planning, and control software, we are able to achieve full three-axis attitude determination and control. The system is also completely solid-state, with no moving parts or need for consumable propellant, greatly reducing the chance of hardware failure. To further improve the development cycle and increase success rates for CubeSat missions, we have also developed an open-source hardware-in-the-loop simulator to enable rapid testing of ADCS algorithms and other flight software. The result is a robust, open-source development suite for CubeSats that is low cost, easy to program, and reliable

PyCubed Mini: An Open-Source, Computational Platform for Education and Research in the Pocketqube Form Factor

As more CubeSat missions incorporate the hardware and software framework standardized by PyCubed, constraints driving future mission deliverables will become increasingly dependent upon on-board computation. We have developed new embedded tools for PyCubed that facilitate increased computational performance in satellite microcontrollers, provide advanced attitude control capability, and maintain the project\u27s original emphasis on usability and educational approachability. The PyCubed Mini project incorporates the refined hardware and software of PyCubed into a PocketQube spacecraft that is scheduled to launch in Q4 2020. These advancements in spacecraft performance are accomplished while developing the mission as part of a year-long Stanford undergraduate capstone course

Direct Policy Optimization using Deterministic Sampling and Collocation

We present an approach for approximately solving discrete-time stochastic optimal-control problems by combining direct trajectory optimization, deterministic sampling, and policy optimization. Our feedback motion-planning algorithm uses a quasi-Newton method to simultaneously optimize a reference trajectory, a set of deterministically chosen sample trajectories, and a parameterized policy. We demonstrate that this approach exactly recovers LQR policies in the case of linear dynamics, quadratic objective, and Gaussian disturbances. We also demonstrate the algorithm on several nonlinear, underactuated robotic systems to highlight its performance and ability to handle control limits, safely avoid obstacles, and generate robust plans in the presence of unmodeled dynamics.Comment: revisions for RA-L 202

ALGAMES: A Fast Solver for Constrained Dynamic Games

Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. This paper introduces ALGAMES (Augmented Lagrangian GAME-theoretic Solver), a solver that handles trajectory optimization problems with multiple actors and general nonlinear state and input constraints. Its novelty resides in satisfying the first order optimality conditions with a quasi-Newton root-finding algorithm and rigorously enforcing constraints using an augmented Lagrangian formulation. We evaluate our solver in the context of autonomous driving on scenarios with a strong level of interactions between the vehicles. We assess the robustness of the solver using Monte Carlo simulations. It is able to reliably solve complex problems like ramp merging with three vehicles three times faster than a state-of-the-art DDP-based approach. A model predictive control (MPC) implementation of the algorithm demonstrates real-time performance on complex autonomous driving scenarios with an update frequency higher than 60 Hz.Comment: 10 pages, 8 figures, submitted to Robotics: Science and Systems Conference (RSS) 202