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- Advances in Aerospace Guidance, Navigation and Control
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Show all documents Bipartite guidance, navigation and control architecture for autonomous aerial inspections under safety constraints 9, 1, 2, 4, 8, 12].
The conference aims at promoting new advances in aerospace GNC theory and technologies for enhancing safety, survivability, efficiency, performance, autonomy and intelligence of aerospace systems. It represents a unique forum for communication and information exchange between specialists in the fields of GNC systems design and operation, including air traffic management. This book contains the forty best papers and gives an interesting snapshot of the latest advances over the following topics:. Each paper was reviewed in compliance with standard journal practice by at least two independent and anonymous reviewers.
Advances in Aerospace Guidance, Navigation and Control
Jump to navigation. The functions of Guidance, Navigation, and Control are vital to all forms of air and space flight. The Space History collections in this area attempt to reflect that significance and illustrate the breadth of the topic.
In practice, these three functions blend into one another, and artifacts from this collection often perform multiple duties. For this collection, "guidance" shall refer to controlling a vehicle during acceleration or deceleration, mainly during the powered phase of flight, i.
Guided missiles, which are powered for most of their flight, require continuous guidance hence the name , but in a typical space mission, a rocket burns for only a fraction of the total time of the mission and would require guidance for only that short period of time.
Once the rocket engines shut off, there follows the function of "navigation," which is to get from one position in space to another. In contrast to navigation at sea or in the air, space navigation typically consists of long periods of coasting with periodic corrections.
Finally, "control" is defined as orienting the spacecraft in its rotational axes to perform its various operations, such as pointing a telescope, orienting an antenna toward Earth, preparing the vehicle for a rocket burn, etc.
Again in contrast to aircraft and ships, in the absence of an atmosphere, a spacecraft may be oriented in any direction, but it is usually not desirable to allow it to tumble with no control. Passenger aircraft fly with periodic communication with air traffic controllers on the ground, but in general, they fly with a great deal of autonomy. In contrast, spacecraft that carry a human crew are intensively managed from the ground, where controllers monitor the vehicle?
Robotic spacecraft may require less control, but during critical phases of their missions, they are also intensively controlled from Earth. The National Air and Space Museum's collections in this area attempt to show the breadth and depth of this topic by a judicious selection of artifacts.
In order to accomplish this task, reliable, fast and autonomous Guidance, Navigation, and Control GNC algorithms are necessary. In recent years, the strong capabilities of modern hardware have allowed employing deep learning models for space applications. In this work, SSEL presents an image-based powered descent guidance via deep learning to control the command acceleration along the three axes.
Hence, the whole neural network maps the sequences of images into the values of the command acceleration. The images are generated within a simulated environment with physically based ray-tracing capabilities.
More specifically, by implementing a class of CNNs and Recurrent Neural Networks RNNs capable of learning the underlying functional relationship between a sequence of optical images taking during the descent and the thrust action. The system learns in a simulated environment where optimal trajectories are computed via known optimization methods, and spacecraft position and velocity are correlated directly through ray-tracing simulation to the related optical image taken by the on-board camera.
SSEL approach aims to create a deep neural network DNN that can allow the spacecraft to autonomously land on the Moon surface with an energy optimal-based trajectory, given random initial conditions. The goal is to create a logical connection between the motion of the terrain features in the camera frame and the control action to achieve the desired trajectory.
To achieve it, we train a hybrid neural network in a supervised fashion, which means that both data and the ground truth labels are the inputs of the models. In this work, the data are sequences of images frames taken by the lander camera, supposed to always point towards the surface Figure on the right.
The respective labels are the accelerations correlated to the last frame of the sequence. Within this framework, only the translational dynamics are taken into account, while the attitude dynamics are supposed to be independent and treated separately.
However, rotational dynamics are not considered in this work. In the machine learning framework, it is well known that a generalized training-set is fundamental to ensure an unbiased estimation. For that reason, a set of trajectories has been created with an initial position varying randomly inside a sphere of radius m and a final position varying within a circle of radius m on the surface.
For what concerns the initial velocity, it is supposed to randomly change within a sphere, as well. The region chosen for the landing is the Apollo 16 landing site Figure on the left. In this work, we use Cycles renders, an open-source physically based production rendering engine developed by the Blender project, to generate a grey-scale image at each position of the trajectories.
Once the train and test-set are ready, the data must be prepared before being fed into the model. We create a 5D tensor with the following dimensions: batch-size, number of frames, number of channels, image height, image width. This number has a significant impact on the quality of temporary information extracted by the LSTM.
On the other hand, it also affects the number of parameters in the model. Therefore, a trade-off should be done to achieve a good compromise between the performances and the computational time. GNC tasks are generally performed by independent modules. In this work, reinforcement metalearning and hazard detection and avoidance are embedded into a single system to derive the optimal thrust command for a safe lunar pinpoint landing using sequences of images and radar altimeter data as inputs.
In particular, we incorporate autonomous hazard detection and avoidance and real-time GNC, which are essential for a successful landing.
The former are achieved using a machine learning model trained in a supervised fashion to recognize hazardous areas in the camera field of view and selecting a safe point accordingly. Then, within the reinforcement meta-learning framework, this information is used by the agent to learn how to optimally behave in this simulated environment and land safely.
In this work, SSEL proposes a new approach based on deep learning that integrates guidance and navigation functions providing a complete solution to the lunar landing problem that integrates an image-based navigation to an intelligent guidance.
Hazard avoidance and detection are also considered to autonomously detect safe landing sites and they are embedded in the global framework by using a CNN trained via supervised fashion. More specifically, we design a simulation environment that is able to integrate the dynamics of the system and simulate image acquisition from on-board cameras.
This is achieved by interfacing the simulator in Python with a ray tracer i. The images are then used to update a policy in real time using reinforcement learning. The hazard detection and avoidance are also taken into account in the definition of the reward function.
The proposed approach relies on a combination of deep learning, computational optimal control and hazard detection, supported by the ability to generate simulated images of the lunar surface. The overall goal is to teach a spacecraft to autonomously execute lunar landing in an unsupervised manner by processing a sequence of optical images taken by the spacecraft on-board camera and data from a radar altimeter and produce an adequate thrust command.
This is achieved using a simulation pipeline that integrates the equations of motion to simulate the dynamics and generates sensor data simultaneously in near real-time. The dynamical model employed is the classical landing model considered for a flat surface of a planetary body without atmosphere. For what concerns the hazard detection and avoidance, we use a special convolutional neural network, called Unet, that performs semantic segmentation.
This has been trained in a supervised manner to separate hazardous and safe areas in an image of the surface of the Moon. The safest spot is selected according to its distance from hazardous areas.
The reward is then calculated according to the distance of this point from the center of the frame and the vertical velocity to successfully perform a soft landing. Reinforcement learning algorithms like PPO then use this experience to learn the optimal policy.
The Figure below shows the overall framework where it is clearly shown how the dynamic simulator, the raytracer and the hazard detection model work together with the RL algorithm in closed loop. Raytracing is the state of the art in the field of realistic rendering. It has been used extensively for producing realistic and physically accurate environment renders. In this case, we use Cycles renderer, an open source physically based production rendering engine developed by the Blender project.
This, not only has shown to work extremely well in many applications but it also has some advantages over other rendering engines i. The fact that it runs in Blender makes it easy to integrate the renderer in the machine learning pipeline. Indeed, Blender natively support Python scripting which makes it easy to be interfaced with Python where the environment is simulated and learning takes place.
It should be noted in fact that PPO is an online algorithm that has to be fed continuously with new batches of samples for a successful learning. In order to select a safe zone for landing, a method for hazard identification and characterization must be developed. In this paper, we used a particular kind of neural network that is able to recognize and label different areas of an image based on a ground truth mask.
Specifically, the network is comprised of an encoder and a decoder. The encoder extracts information from the input image Fig. The decoder then upscales that information back to create a labelled image with the same size as the input. The output of the net is a labelled image in which safe and unsafe areas are identified with different colours Fig. The algorithm then calculates the minimum distance from the closest hazardous pixel in the image matrix.
The safest spot will then be the pixel with the biggest among the minimum computed distances Fig. This information is then used to create an additional image that is black everywhere except where the safe landing pixel resides Fig. This image, here referred to as the target image Fig. The white pixel, called landing pixel, represents the current landing spot to track the landing site can change if a new safer spot is found : its distance from the centre of the camera is used as a term of the reward to make the agent learn how to land in a correct landing site.
One can note that keeping the landing pixel in the centre of the field of view, always considered to point the nadir, means that a vertical landing is occurring. This is important especially during the last phase of a landing trajectory. Guidance, Navigation, and Control. Our Publications Furfaro, R. Acta Astronautica. Least-squares solution of a class of optimal space guidance problems via theory of connections.
PDF Scorsoglio, A. Actor-critic reinforcement learning approach to relative motion guidance in near-rectilinear orbit. PDF Drozd K. PDF Schiassi E. PDF Drozd, K. Integrated guidance for mars entry and powered descent using reinforcement learning and gauss pseudospectral method. Univelt Inc.. Deep learning for autonomous lunar landing. PDF Campbell, T. A deep learning approach for optical autonomous planetary relative terrain navigation. Univelt Inc.
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The challenges are mainly associated with the level of miniaturisation. A docking mechanism was designed, built and tested in the laboratory. Results show that a relative precision better than 1 cm and 2 degrees is required for the docking. The docking mechanism and metrology system, composed of a monocular camera and sets of light- emitting diodes, are contained within 0. The chaser and target satellites have a complete 3-axis attitude pointing capability and are equipped with available CubeSats attitude sensors and actuators.
PDF | The Flight Control System 20 (FCS20) is a compact, self-contained Guidance, Navigation, and Control system that has recently been.
Marine Craft Hydrodynamics and Motion Control
Skip to main content. Table of Contents. No Access Examination of the optimal nonlinear regulator problem S. No Access Nonlinear control of a twin-lift helicopter configuration P.
Guidance, navigation, and control
Skip to main content. Guidance and Control Conference 11 August - 13 August Table of Contents. No Access Control of self-adjoint distributed-parameter systems L. No Access The synthesis of control logic for parameter-insensitivity and disturbance attenuation A. No Access Optimal decentralized regulators for interconnected systems M. No Access Local distributed estimation D.
Among these systems, the GNC system communicates with all of the other components and controls the behavior of the components in order to complete a given mission. Therefore, a GNC system requires a high processing capability and multitasking capability and must support various communication methods. Sign In or Register.
PDF | The Geostationary Operational Environmental Satellite-R series This paper presents the guidance navigation & control (GN&C).