GPS-denied multi-agent localization and terrain classification for autonomous parafoil systems

Guided airdrop parafoil systems depend on GPS for localization and landing. In some scenarios, GPS may be unreliable (jammed, spoofed, or disabled), or unavailable (indoor, or extraterrestrial environments). In the context of guided parafoils, landing locations for each system must be pre-programmed manually with global coordinates, which may be inaccurate or outdated, and offer no in-flight adaptability. Parafoil systems in particular have constrained motion, communication, and on-board computation and storage capabilities, and must operate in harsh conditions. These constraints necessitate a comprehensive approach to address the fundamental limitations of these systems when GPS cannot be used reliably. A novel and minimalist approach to visual navigation and multi-agent communication using semantic machine learning classification and geometric constraints is introduced. This approach enables localization and landing site identification for multiple communicating parafoil systems deployed in GPS-denied environments

GPS-denied multi-agent localization and terrain classification for autonomous parafoil systems

Guided airdrop parafoil systems depend on GPS for localization and landing. In some scenarios, GPS may be unreliable (jammed, spoofed, or disabled), or unavailable (indoor, or extraterrestrial environments). In the context of guided parafoils, landing locations for each system must be pre-programmed manually with global coordinates, which may be inaccurate or outdated, and offer no in-flight adaptability. Parafoil systems in particular have constrained motion, communication, and on-board computation and storage capabilities, and must operate in harsh conditions. These constraints necessitate a comprehensive approach to address the fundamental limitations of these systems when GPS cannot be used reliably. A novel and minimalist approach to visual navigation and multi-agent communication using semantic machine learning classification and geometric constraints is introduced. This approach enables localization and landing site identification for multiple communicating parafoil systems deployed in GPS-denied environments

Experimental Investigation of Stochastic Parafoil Guidance using a Graphics Processing Unit

Control of autonomous systems subject to stochastic uncertainty is a challenging task. In guided airdrop applications, random wind disturbances play a crucial role in determining landing accuracy and terrain avoidance. This paper describes a stochastic parafoil guidance system which couples uncertainty propagation with optimal control to protect against wind and parameter uncertainty in the presence of impact area obstacles. The algorithm uses real-time Monte Carlo simulation performed on a graphics processing unit (GPU) to evaluate robustness of candidate trajectories in terms of delivery accuracy, obstacle avoidance, and other considerations. Building upon prior theoretical developments, this paper explores performance of the stochastic guidance law compared to standard deterministic guidance schemes, particularly with respect to obstacle avoidance. Flight test results are presented comparing the proposed stochastic guidance algorithm with a standard deterministic one. Through a comprehensive set of simulation results, key implementation aspects of the stochastic algorithm are explored including tradeoffs between the number of candidate trajectories considered, algorithm runtime, and overall guidance performance. Overall, simulation and flight test results demonstrate that the stochastic guidance scheme provides a more robust approach to obstacle avoidance while largely maintaining delivery accuracy

Experimental investigation of stochastic parafoil guidance using a graphics processing unit

a b s t r a c t Control of autonomous systems subject to stochastic uncertainty is a challenging task. In guided airdrop applications, random wind disturbances play a crucial role in determining landing accuracy and terrain avoidance. This paper describes a stochastic parafoil guidance system which couples uncertainty propagation with optimal control to protect against wind and parameter uncertainty in the presence of impact area obstacles. The algorithm uses real-time Monte Carlo simulation performed on a graphics processing unit (GPU) to evaluate robustness of candidate trajectories in terms of delivery accuracy, obstacle avoidance, and other considerations. Building upon prior theoretical developments, this paper explores performance of the stochastic guidance law compared to standard deterministic guidance schemes, particularly with respect to obstacle avoidance. Flight test results are presented comparing the proposed stochastic guidance algorithm with a standard deterministic one. Through a comprehensive set of simulation results, key implementation aspects of the stochastic algorithm are explored including tradeoffs between the number of candidate trajectories considered, algorithm runtime, and overall guidance performance. Overall, simulation and flight test results demonstrate that the stochastic guidance scheme provides a more robust approach to obstacle avoidance while largely maintaining delivery accuracy

Robust planning for autonomous parafoil

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 112-119).Parafoil trajectory planning systems must be able to accurately guide the highly non-linear, under-actuated parafoil system from the drop zone to the pre-determined impact point. Parafoil planning systems are required to navigate highly complex terrain scenarios, particularly in the presence of an uncertain and potentially highly dynamic wind environment. This thesis develops a novel planning approach to parafoil terminal guidance. Building on the chance-constrained rapidly exploring random tree (CC-RRT) [1] algorithm, this planner, CC-RRT with Analytic Sampling, considers the non-linear dynamics, as well as the under-actuated control authority of the parafoil by construction. Additionally, CC-RRT with Analytic Sampling addresses two important limitations to state-of-the-art parafoil trajectory planners: (1) implicit or explicit constraints on starting altitude of the terminal guidance phase, and (2) a reactive or limitedly-proactive approach to handling the eect of wind uncertainty. This thesis proposes a novel formulation for the cost-to-go function, utilizing an approximation of the reachability set for the parafoil to account for the eect of vehicle heading on potential future states. This cost-to-go function allows for accurate consideration of partially planned paths, effectively removing strict constraints on starting altitude of the terminal guidance phase. The reachability set cost-to-go function demonstrates considerably improved performance over a simple LQR cost function, as well as cost-to-go functions with a glide-slope cone bias, demonstrating the eectiveness of utilizing the reachability set approximation as a means for incorporating heading dynamics. Furthermore, this thesis develops a multi-class model for characterizing the uncertain effect of wind. The wind model performs an online classication based on the observed wind measurements in order to determine the appropriate level of planner conservatism. Coupling this wind model with the method for sampling the analytic uncertainty distribution presented in this thesis, the CCRRT with Analytic Sampling planner is able to eciently account for the future eect of wind uncertainty and adjust trajectory plans accordingly, allowing the planner to operate in arbitrary terrain configurations without issue. CC-RRT with Analytic Sampling performs exceptionally well in complex terrain scenarios. Simulation results demonstrate signicant improvement on complex terrain relative to the state-of-the-art Band-Limited Guidance (BLG) [2], drastically reducing the worst case and average target miss distances. Simulation results demonstrate the CC-RRT with Analytic Sampling algorithm remains un-affected as terrain complexity increases, making it an ideal choice for applications where difficult terrain is an issue, as well as missions with targets with drastically dierent terrain conditions. Moreover, CC-RRT with Analytic Sampling is capable of starting terminal guidance at significantly higher altitudes than conventional approaches, while demonstrating no signicant change in performance.by Ian Sugel.S.M

Robust planning for autonomous parafoil

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 112-119).Parafoil trajectory planning systems must be able to accurately guide the highly non-linear, under-actuated parafoil system from the drop zone to the pre-determined impact point. Parafoil planning systems are required to navigate highly complex terrain scenarios, particularly in the presence of an uncertain and potentially highly dynamic wind environment. This thesis develops a novel planning approach to parafoil terminal guidance. Building on the chance-constrained rapidly exploring random tree (CC-RRT) [1] algorithm, this planner, CC-RRT with Analytic Sampling, considers the non-linear dynamics, as well as the under-actuated control authority of the parafoil by construction. Additionally, CC-RRT with Analytic Sampling addresses two important limitations to state-of-the-art parafoil trajectory planners: (1) implicit or explicit constraints on starting altitude of the terminal guidance phase, and (2) a reactive or limitedly-proactive approach to handling the eect of wind uncertainty. This thesis proposes a novel formulation for the cost-to-go function, utilizing an approximation of the reachability set for the parafoil to account for the eect of vehicle heading on potential future states. This cost-to-go function allows for accurate consideration of partially planned paths, effectively removing strict constraints on starting altitude of the terminal guidance phase. The reachability set cost-to-go function demonstrates considerably improved performance over a simple LQR cost function, as well as cost-to-go functions with a glide-slope cone bias, demonstrating the eectiveness of utilizing the reachability set approximation as a means for incorporating heading dynamics. Furthermore, this thesis develops a multi-class model for characterizing the uncertain effect of wind. The wind model performs an online classication based on the observed wind measurements in order to determine the appropriate level of planner conservatism. Coupling this wind model with the method for sampling the analytic uncertainty distribution presented in this thesis, the CCRRT with Analytic Sampling planner is able to eciently account for the future eect of wind uncertainty and adjust trajectory plans accordingly, allowing the planner to operate in arbitrary terrain configurations without issue. CC-RRT with Analytic Sampling performs exceptionally well in complex terrain scenarios. Simulation results demonstrate signicant improvement on complex terrain relative to the state-of-the-art Band-Limited Guidance (BLG) [2], drastically reducing the worst case and average target miss distances. Simulation results demonstrate the CC-RRT with Analytic Sampling algorithm remains un-affected as terrain complexity increases, making it an ideal choice for applications where difficult terrain is an issue, as well as missions with targets with drastically dierent terrain conditions. Moreover, CC-RRT with Analytic Sampling is capable of starting terminal guidance at significantly higher altitudes than conventional approaches, while demonstrating no signicant change in performance.by Ian Sugel.S.M

Development of a Night Vision Goggle Heads Up Display For Paratrooper Guidance

This thesis provides the proof of concept for the development and implementation of a Global Positioning System (GPS) display via Night Vision Goggles (NVG) Heads-Up Display (HUD) for paratroopers. The system has been designed for soldiers who will be able to utilize the technology in the form of a processing system worn in an ammo pouch and displayed via NVG HUD as a tunnel in the sky. The tunnel in the sky display design is essentially a series of boxes displayed within the goggle\u27s HUD leading the paratrooper to the desired Landing Zone (LZ). The algorithm developed receives GPS and inertial sensor data (both position and attitude), and displays the guidance information in the paratrooper\u27s NVG HUD as the tunnel in the sky. The primary goal of the project is to provide a product which allows military personnel to reach a desired LZ in obscured visibility conditions such as darkness, clouds, smoke, and other unforeseen situations. This allows missions to be carried out around the clock, even in adverse visibility conditions which would normally halt operations

Development and Viability of an Inverted Descent Quadrotor for Precision Aerial Delivery

Title from PDF of title page viewed July 7, 2021Thesis advisor: Travis FieldsVitaIncludes bibliographical references (pages 88-90)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2021The field of Precision Aerial Delivery strives for the highest standard of accuracy, with sights set on delivering payloads from high altitudes to mere feet from goal locations. Quadrotors, due to high maneuverability and precision, are a logical choice to achieve this objective, however current inefficiencies of descent prevent quadrotors from use in Precision Aerial Delivery. Described herein are the development, experimentation and implementation of a near passive, Inverted Descent Quadrotor system. The Inverted Descent Quadrotor allows for high altitude deployment of a quadrotor fitted with standard hardware with the ability to descend more rapidly and with less energy requirement than previously possible. The decreased energy requirement of the Inverted Descent Quadrotor enables quadrotors to be used in high altitude scenarios, for missions that have previously been reserved for guided parachute or parafoil systems. Quadrotor systems thrive in such missions after descent, as they accommodate the potential for complex, highly precise activities to be completed once reaching ground. The development process consisted of wind tunnel thrust testing, a proof of concept single axis prototype, a functional Inverted Descent Quadrotor build, real world performance testing and mission simulation. Each stage of development provided valuable insight into the governing dynamics of rapid descent environments and informed the subsequent phases of development. Wind tunnel thrust testing helped to identify a potentially optimal baseline throttle to apply when facing high speed descent to maintain control authority, as well as quantify potential power savings. The single axis prototype proved control potential in an artificial descent environment, further quantified power usage, characterized potential glide performance and suggested control gains for the rapid descent environment. A functional Inverted Descent Quadrotor was then built and tested to prove initial functionality, and verify the results from single axis testing. The system was then tested to quantify control, power and glide performance against various configurations of normal quadrotor descent, as well as current comparable Precision Aerial Delivery Systems. Finally, the Inverted Descent Quadrotor's performance results were extrapolated to simulation to display the increased performance of the Inverted Descent Quadrotor in a mission setting. The Inverted Descent Quadrotor proved to be successful in reducing descent energy, which endowed high precision quadrotor systems the capability for descents far higher than previously possible. With far less sensitivity to wind prediction, coupled with the potential for payload capacity, the Inverted Descent Quadrotor showed promise worthy of future development and research.Overview -- Background and literature review -- Wind tunnel testing -- Full system baseline throttle testing -- Descent testing -- Mission simulation -- Conclusions and future work -- Appendi

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas