Device for Autonomous Rocketry

Abstract

Rocket control is a difficult and unpredictable task in environments with inclement weather. As a result, launch missions are often strictly limited based on weather conditions. The present invention provides a device for controlling a rocket to account for environmental uncertainties and maintain optimal mission performance. In embodiments, the device is a radiation hardened field programmable gate array with an embedded artificial intelligence program contained in a graphics processing unit that is used for rocket reaction control by generating thrust vector commands.

Claims

1. A computing device for rocket reaction control, the computing device comprising a simulation trained artificial intelligence computer program, the artificial intelligence computing program being embedded on a radiation hardened processor, the radiation hardened processor further comprising at least one graphics processing unit, the radiation hardened processor further storing an artificial intelligence computer program processing real time sensor data, the artificial intelligence computer program generalizing about the rocket's trajectory environment, the artificial intelligence computer program further comprising at least one deep learning program to optimize commands for reaction control, the commands for reaction control controlling thrust vectors for the rocket, wherein wiring connects the radiation hardened processor to the rocket's thrust vectors further comprising a fuel injector, the fuel injector injecting fuel to one or more engines according to the commands produced by the deep learning computer program, the deep learning computer program further comprising at least on neural network.

2. The device of claim 1, wherein, a second artificial intelligence computer program optimizes metrics corresponding to distance, time, and impact, informing the command sequences for controlling the thrust vectors for the rocket, maintaining an optimal trajectory course throughout the rocket's flight, from point-to-point, wherein the first point is the launch pad, and the final point is the landing pad.

3. The device of claim 1, wherein a second software containing a convolutional neural network integrated with a reinforcement learning program is embedded on the radiation hardened processor and optimizes control metrics corresponding to distance, time, and impact.

4. The device of claim 1, wherein a reinforcement learning agent is embedded in software on the radiation hardened processor and controls thrust vectors to manipulate thrust control and steer the rocket's pitch, attitude, roll, and yaw.

5. The device of claim 1, wherein the engines receiving fuel injections from the fuel injectors according to commands from thrust vectors, propel the rocket using chemical propellants, generating thrust throughout the rocket's point-to-point trajectory.

6. The device of claim 1, wherein the engines receiving fuel injections from the fuel injectors according to commands from thrust vectors, propel the rocket using nuclear propulsion, generating thrust throughout the rocket's point-to-point trajectory.

7. The device of claim 1, wherein the radiation hardened processor, processes data using a graphics processing unit with embedded software performing convolutional operations on real-time sensor data, the embedded software further predicting associations between thrust vector commands and flight path accuracy according to an optimal trajectory, and further signaling the thrust vector commands from launch to landing for optimal performance.

8. A computing device for rocket reaction control, the computing device comprising a simulation trained artificial intelligence computer program, the artificial intelligence computing program being embedded on a radiation hardened processor, the radiation hardened processor further comprising one graphic processing unit, the radiation hardened processor further storing the artificial intelligence computer program processing real time sensor data, the artificial intelligence computer program generalizing about the rocket's trajectory environment, the artificial intelligence computer program further comprising one reinforcement learning program to optimize commands for reaction control, the commands for reaction control controlling thrust vectors for the rocket, wherein wiring connects the radiation hardened processor to the rocket's thrust vectors further comprising a fuel injector, the fuel injector injecting fuel to one or more engines according to the commands produced by the reinforcement learning computer program.

9. The device of claim 8, wherein a second artificial intelligence computer program optimizes metrics corresponding to distance, time, and impact, informing the command sequences for controlling the thrust vectors for the rocket, maintaining an optimal trajectory course throughout the rocket's flight, from point-to-point, wherein the first point is the launch pad, and the final point is the landing pad.

10. The device of claim 8, wherein a second software containing a convolutional neural network integrated with the reinforcement learning program is embedded on the radiation hardened processor and optimizes control metrics corresponding to distance, time, and impact.

11. The device of claim 8, wherein a second reinforcement learning agent is embedded in software on the radiation hardened processor and controls thrust vectors to manipulate thrust control and steer the rocket's pitch, attitude, roll, and yaw.

12. The device of claim 8, wherein the engines receiving fuel injections from the fuel injectors according to commands from thrust vectors, propel the rocket using chemical propellants, generating thrust throughout the rocket's point-to-point trajectory.

13. The device of claim 8, wherein the engines receiving fuel injections from the fuel injectors according to commands from thrust vectors, propel the rocket using nuclear propulsion, generating thrust throughout the rocket's point-to-point trajectory.

14. The device of claim 8, wherein the engines receiving fuel injections from the fuel injectors according to commands from thrust vectors, propel the rocket using nuclear propulsion, generating thrust throughout the rocket's extraterrestrial trajectory.

15. The device of claim 8 wherein, the radiation hardened processor, processes data using a graphics processing unit with embedded software performing convolutional operations on real-time sensor data, the embedded software further predicting associations between thrust vector commands and flight path accuracy according to an optimal trajectory, and further signaling the thrust vector commands from launch to landing for optimal performance.

16. A computing device for rocket reaction control, the computing device comprising a simulation trained artificial intelligence computer program, the artificial intelligence computing program being embedded on a radiation hardened processor, the radiation hardened processor further storing the artificial intelligence computer program processing real time sensor data, the artificial intelligence computer program generalizing about the rocket's trajectory environment, the artificial intelligence computer program optimizing commands for reaction control, the commands for reaction control controlling thrust vectors for the rocket to minimize error in reaction control.

17. The device of claim 16, wherein a second artificial intelligence computer program optimizes metrics corresponding to distance, time, and impact, informing the command sequences for controlling the thrust vectors for the rocket, maintaining an optimal trajectory course throughout the rocket's flight, from point-to-point, wherein the first point is the launch pad, and the final point is the landing pad.

18. The device of claim 16, wherein a second software containing a convolutional neural network integrated with a reinforcement learning program is embedded on the radiation hardened processor and optimizes control metrics corresponding to distance, time, and impact.

19. The device of claim 16, wherein a reinforcement learning agent is embedded in software on the radiation hardened processor and controls thrust vectors to manipulate thrust control and steer the rocket's pitch, attitude, roll, and yaw.

20. The device of claim 16, wherein the engines receiving fuel injections from the fuel injectors according to commands from thrust vectors, propel the rocket using chemical propellants, generating thrust throughout the rocket's point-to-point trajectory.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG. 1 illustrates embodiments of the present disclosure wherein a device for autonomous rocketry controls a rocket or end-to-end delivery of a satellite to orbit.

[0032] FIG. 2 illustrates embodiments of the present disclosure as a computing device for autonomous rocket control.

[0033] FIG. 3 illustrates the present disclosure as an information flow model for receiving sensor data.

DETAILED DESCRIPTION OF THE INVENTION

[0034] FIG. 1 illustrates embodiments of the present disclosure wherein a device for autonomous rocketry controls a rocket or end-to-end delivery of a satellite to orbit. In such embodiments, a rocket 101 starts a launch pad 100. The rocket launches to space 102 and delivers 103 a satellite 104 to orbit, wherein the capsule 105 detaches from the booster stage. The booster stage then returns to Earth 106 and lands safely 107 on the landing pad 108.

[0035] FIG. 2 illustrates embodiments of the present disclosure as a computing device for autonomous rocket control. In such embodiments, a radiation hardened processor 200 may use a connection device 201 to combine computation of a CPU 203 and a GPU 202. The processor may connect to incoming and outgoing electrical circuits via a plug-in port 204.

[0036] FIG. 3 describes embodiments of the present disclosure as an information flow model for receiving sensor data. In certain embodiments of the present disclosure, an onboard database and processor receive LIDAR sensor data 300 from LIDAR sensors on the rocket. The data is then stored in radiation hardened FPGA 301 which processes the sensor data with a trained deep intelligence 302. The deep intelligence then sends signals to the thrust vector control system 303. In turn, the thrust vector control system manipulates thrust vector valves 304. As a result, the rocket's control is optimized from launch to landing 305.

[0037] In certain embodiments of the present disclosure, an onboard database and processor receive LIDAR sensor data 300 from LIDAR sensors on the rocket. The data is then stored in radiation hardened FPGA 301 which processes the sensor data with a trained deep intelligence 302. The deep intelligence then sends signals to the thrust vector control system 303. In turn, the thrust vector control system manipulates thrust vector valves 304. As a result, the rocket's control is optimized from launch to landing 305.

[0038] In certain embodiments, a radiation hardened processor 200 may use a GPU 202 with a trained deep intelligence software program 302 to optimize rocket control. In such embodiments, the GPU may produce commands or signals for the thrust vector control system 303. These thrust vector control signals may be used to optimize and ensure a safe rocket landing 107 on a rocket landing pad 108.

[0039] In certain embodiments, a radiation hardened processor 200 may use a GPU 202 with a trained deep intelligence software program 302 to optimize rocket control. The GPU may work in conjunction with a CPU 203 to optimize control signal outputs and commands for the reaction control system. The reaction control outputs and commands may be used to optimize and ensure a safe rocket landing 107 on a rocket landing pad 108.

[0040] In certain embodiments, a radiation hardened processor 200 may use a GPU 202 with a trained deep intelligence software program 302 to optimize rocket control. The GPU may work in conjunction with a CPU 203 to optimize control signal outputs and commands for the reaction control system. The reaction control outputs and commands may be distributed through wiring and transferred throughout the rocket via a plug-in port connection mechanism 204.

[0041] In certain embodiments, the present disclosure is a method for rocket control. In such embodiments, the method includes a rocket with several LIDAR or other data sensors, which record information about the rocket's environment in real time 300. The LIDAR and other data sensors may then transmit the data to a database and processor 301. The processor may in certain embodiments include and embedded machine learning algorithm, using a convolutional neural network to generate an accurate point cloud environment. Additionally, in certain embodiments, the processor may include a second machine learning algorithm, such as a deep reinforcement learning algorithm for producing commands for thrust vector valve manipulation 304.

[0042] In certain embodiments, the present disclosure may converge hardware and software components including both a radiation hardened FGPA and a deep reinforcement learning algorithm, which may be fastened in the rocket to control the rocket's thrust output 304. In certain embodiments, electric wiring from the FGPA may carry signals from the deep reinforcement learning control algorithm to fuel injectors throughout the point-to-point journey 303. In such embodiments, the entire trajectory, from launch to landing may be controlled by the deep reinforcement learning control algorithm manipulating thrust vector commands corresponding to thrust output.

[0043] In certain embodiments, the present disclosure includes using LiDAR sensors 300 for perception includes convolutional neural networks 302 for generating a digital environment. Programming code for the convolutional neural networks may be written in various programming languages including Python, C, and C++ depending on mission need. The software may be developed in a simulation environment prior to flight and subsequently embedded to the rocket's on-board processor 200.

[0044] In certain embodiments, the present disclosure is a device for commanding a reaction control system. The device comprises a simulation trained artificial intelligence program, which operates on a radiation hardened processor 200. The artificial intelligence program processes real time sensor data and generalizes about the rocket's trajectory environment. Specifically, in certain embodiments, the artificial intelligence program using a deep learning program to optimize commands for end-to-end trajectory. In such embodiments, the artificial intelligence computer program produces commands that control thrust vectors for the rocket. In such embodiments, the thrust vectors also may include a fuel injector, injecting fuel to one or more engines according to the commands produced by the deep learning program 302.

[0045] In certain embodiments, a radiation hardened processor 200 may be embedded on a rocket 101. In such embodiments, the radiation hardened processor may control the rocket using an embedded artificial intelligence program on a GPU 202 in conjunction with a CPU 203, as the rocket launches to space 102 and delivers 103 a satellite 104 to orbit.

[0046] In certain embodiments, a radiation hardened processor 200 may be embedded on a rocket 101. In such embodiments, the radiation hardened processor may control the rocket using an embedded artificial intelligence program on a GPU 202 in conjunction with a CPU 203, as the rocket returns from orbit 106 and lands 107 on a landing pad 108.

[0047] In certain embodiments, the present disclosure utilizes various hardware components. For example, certain embodiments include mounting a radiation hardened field programmable gate array (FGPA) on the rocket, with wiring connections to various thrust chambers. In certain embodiments, the FGPA may contain both a central processing unit and graphics processing unit to perform computations. Commands from the FGPA move to control vector units which may open and close thrust chambers on the rocket, or limit thrust output to a certain degree 304.

[0048] In certain embodiments of the present disclosure, the FGPA may be embedded with a deep learning algorithm. The embedded deep algorithm may be expressed as software code written in one of several programming languages, including Python, C, C++ or other machine code. The deep learning algorithm may be trained in a simulation environment before being embedded to the hardware processor. Throughout the mission, the algorithm may correct for differences in the actual flight path and the optimal flight path by issuing commands corresponding to thrust vector control.

[0049] In certain embodiments, the present disclosure may include sensors collecting data about the rocket's environment. The sensor data may be processed and stored in the rocket's database, and subsequently processed by convolutional neural networks to create a digital environment. The sensor data may be further processed and manipulated by a reinforcement learning agent, which performs optimal control commands to manipulate rocket trajectory 305.

[0050] In certain embodiments, the present disclosure may include the hardware for the rocket may use a niobium alloy metal with a protective heat shield for the rocket body 101. In such embodiments, the inside of the rocket is made up of a chemical propellant engine, with thrust chambers relaying force through a nozzle. The control systems are embedded on a radiation hardened processor 301 with electrical wiring sending signals throughout the rocket 303.

[0051] In certain embodiments, the present disclosure may be composed of three parts, reflecting the three flight stages, which include launch 102, powered flight 103, and landing 205. In each stage, a separate software component may control the rocket to optimize safety and performance for point-to-point travel. Moreover, in such embodiments the software stack embedded in the rocket's hardware processors includes convolutional neural networks, reinforcement learning agents, and integrated deep reinforcement learning systems. In embodiments, the disclosure provides a way to unify computer perception and decision-making technologies for point-to-point rocket control in a singular system 302. In doing so, the methods marry software code for deep learning and reinforcement learning technologies which collaboratively control the rocket from liftoff to landing.

[0052] In certain embodiments, the present disclosure includes LiDAR sensors gathering real-time data about the rocket's environment which is stored in an on-board database and processed with a deep reinforcement learning algorithm producing instructions to optimize rocket control in uncertain environments including inclement weather conditions. In embodiments, the hardware components for the rocket include embedding LiDAR sensors on the rocket, which gather data relating to the rocket's environment. The data collected is routed to an on-board hardware processor with electrical wiring, which allows the data to be processed to create a virtual environment. Further electrical wiring connects the on-board hardware processor to thrust chamber valves which command and control propellant injectors.

[0053] In certain embodiments, the present disclosure may use hardware such as a radiation hardened processor using graphics processing units to process data. For example, certain embodiments include mounting a radiation hardened FGPA on the rocket, with wiring connections to various thrust chambers. The FGPA may contain both a central processing unit and graphics processing unit to perform computations. Commands from the FGPA move to control 301 vector units which may open and close thrust chambers on the rocket, or limit thrust output to a certain degree. The FGPA may be connected throughout the rocket and to sensors with various electrical wirings for transmitting data. Data sensors collecting information may include LiDAR 300, cameras, video, radio, or inertial instruments.

[0054] In embodiments the software control system utilizes artificial intelligence programs processing data in real time to command the rocket through space. For example, the point cloud environment may be processed with convolutional neural networks predicting probabilities and assigning associated actions to optimize the rocket's trajectory. In certain embodiments, the digital point-cloud provides real-time data regarding the rocket's environment from liftoff to landing. In processing the point-cloud data, the rocket's software stack iteratively produces commands corresponding to thrust vector controls 304 for manipulating the rocket to ensure safety and efficiency 305.

[0055] In certain embodiments of the disclosure, a rocket launches a satellite to orbit 104 and returns to Earth 106. During return, an autonomous control system activates with the push of a button. Once activated, the control system autonomously commands the rocket by processing real time data about the landing zone and adapting the rocket's mechanics, positioning, and trajectory accordingly by manipulating the rocket's thrust vector output 304. Multiple LiDAR sensors, GPS sensors, and inertial navigation sensors on the rocket, landing pad, or other locations like drones or ships, to create a 3D point-cloud environment may record data for processing. In real time, a convolutional neural network identifies the landing zone performing the rocket's vision function. Meanwhile, an embedded reinforcement learning agent maximizes a reward function defining optimal landing metrics including distance, time, and impact trajectory and force.

[0056] In certain embodiments, the present disclosure includes a radiation-hardened field-programmable gate array equipped with artificial intelligence playing a critical role in optimizing a rocket's flight path and landing performance, particularly in the harsh conditions of space. These FPGAs are designed to withstand the intense radiation and extreme temperatures encountered in space, ensuring reliable operation. Integrated AI algorithms process vast amounts of real-time data from the rocket's sensors, such as velocity, altitude, and orientation. By analyzing this data, the AI can make rapid, precise adjustments to the rocket's reaction control system. The reaction control system comprises thrusters strategically placed around the rocket and responds to these AI-driven signals to correct the rocket's trajectory and maintain stability. This continuous optimization helps achieve accurate orbital insertion, efficient fuel usage, and safe, and pinpoint landings, enhancing mission success rates and safety.

[0057] In certain embodiments, the present disclosure is a computing device for rocket reaction control. The computing device comprises a simulation trained artificial intelligence computer program. The artificial intelligence computing program is embedded on a radiation hardened processor 200. The radiation hardened processor further stores the artificial intelligence computer program processing real time sensor data. The artificial intelligence computer program generalizes about the rocket's trajectory environment. The artificial intelligence computer program optimizes commands for reaction control. The commands for reaction control controlling thrust vectors for the rocket to minimize error in reaction control.

[0058] In certain embodiments, the present disclosure is a computing device for rocket reaction control. The computing device comprises a simulation trained artificial intelligence computer program. The artificial intelligence computing program is embedded on a radiation hardened processor. The radiation hardened processor further comprises one graphic processing unit 202. The radiation hardened processor further stores the artificial intelligence computer program processing real time sensor data. The artificial intelligence computer program generalizes about the rocket's trajectory environment. The artificial intelligence computer program further comprises one reinforcement learning program to optimize commands for reaction control. The commands for reaction control controlling thrust vectors for the rocket, wherein wiring connects the radiation hardened processor to the rocket's thrust vectors further comprising a fuel injector. The fuel injector injects fuel to one or more engines according to the commands produced by the reinforcement learning computer program.

[0059] In certain embodiments, the present disclosure is a computing device for rocket reaction control. The computing device comprising a simulation trained artificial intelligence computer program. The artificial intelligence computing program being embedded on a radiation hardened processor. The radiation hardened processor further comprising at least one graphics processing unit 202, the radiation hardened processor further storing an artificial intelligence computer program processing real time sensor data. The artificial intelligence computer program generalizing about the rocket's trajectory environment. The artificial intelligence computer program further comprises at least one deep learning program 302 to optimize commands for reaction control. The commands for reaction control controlling thrust vectors for the rocket, wherein wiring connects the radiation hardened processor to the rocket's thrust vectors further comprising a fuel injector. The fuel injector injects fuel to one or more engines according to the commands produced by the deep learning computer program. The deep learning computer program further comprises at least one neural network.

[0060] It is to be understood that while certain embodiments and examples of the invention are illustrated herein, the invention is not limited to the specific embodiments or forms described and set forth herein. It will be apparent to those skilled in the art that various changes and substitutions may be made without departing from the scope or spirit of the invention and the invention is not considered to be limited to what is shown and described in the specification and the embodiments and examples that are set forth therein. Moreover, several details describing structures and processes that are well-known to those skilled in the art and often associated with rockets and rocket trajectories or other launch vehicles are not set forth in the following description to better focus on the various embodiments and novel features of the disclosure of the present invention. One skilled in the art would readily appreciate that such structures and processes are at least inherently in the invention and in the specific embodiments and examples set forth herein.

[0061] One skilled in the art will readily appreciate that the present invention is well adapted to carry out the objectives and obtain the ends and advantages mentioned herein as well as those that are inherent in the invention and in the specific embodiments and examples set forth herein. The embodiments, examples, methods, and compositions described or set forth herein are representative of certain preferred embodiments and are intended to be exemplary and not limitations on the scope of the invention. Those skilled in the art will understand that changes to the embodiments, examples, methods and uses set forth herein may be made that will still be encompassed within the scope and spirit of the invention. Indeed, various embodiments and modifications of the described compositions and methods herein which are obvious to those skilled in the art, are intended to be within the scope of the invention disclosed herein. Moreover, although the embodiments of the present invention are described in reference to use in connection with rockets or launch vehicles, ones of ordinary skill in the art will understand that the principles of the present inventions could be applied to other types of aerial vehicles or apparatus in a wide variety of environments, including environments in the atmosphere, in space, on the ground, and underwater.