Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement

In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR 2018

A modular software architecture for UAVs

There have been several attempts to create scalable and hardware independent software architectures for Unmanned Aerial Vehicles (UAV). In this work, we propose an onboard architecture for UAVs where hardware abstraction, data storage and communication between modules are efficiently maintained. All processing and software development is done on the UAV while state and mission status of the UAV is monitored from a ground station. The architecture also allows rapid development of mission-specific third party applications on the vehicle with the help of the core module

An Active helideck testbed for floating structures based on a Stewart-Gough platform

A parallel robot testbed based on Stewart-Gough platform called Active-helideck is designed, developed and tested as a helicopter floating helideck. The objective of this testbed is to show the advantages of helicopters that use an active helideck upon landing on and taking off from ships or from offshore structures. Active-helideck compensates simulated movements of a ship at sea. The main goal of this study is to maintain the robot’s end effector (helideck) in a quasi-static position in accordance to an absolute inertial frame. Compensation is carried out through the coordinate action of its six prismatic actuators in function of an inertial measurement unit. Moreover, the simulation of the sea movement is done by a parallel robot called ship platform with three degrees of freedom. The ship platform is built with a vertical oscillation along the z axis, i.e. heave, and rotates on remaining axes, i.e. roll and pitch. Active helideck is able to compensate simulated movements by considering the ship as an inertial frame as observed in the experiment

Unmanned systems interoperability standards

Over the past several years, there has been rapid growth in the development and employment of unmanned systems in military and civilian endeavors. Some military organizations have expressed concern that these systems are being fielded without sufficient capabilities to interoperate with existing systems. Despite recognition of this requirement, interoperability efforts remain diverse and disjointed across the United States and internationally. The Naval Postgraduate School (NPS), Monterey, California, was sponsored by the U.S. Office of the Secretary of Defense (OSD) Joint Ground Robotics Enterprise (JGRE) in Fiscal Year 2016 (FY16) to explore (1) enhancement of robotics education; (2) improved representation of robotic systems in combat simulations; and (3) interoperability standards for military robotics systems. This report discusses work performed in FY16 to identify current and emerging interoperability standards for unmanned systems, including interactions of robotic systems with command and control (C2) and simulation systems. The investigation included assessment of the applicability of standardization activities in the Simulation Interoperability Standards Organization (SISO) in its development of the Phase 1 Coalition Battle Management Language (C-BML) and currently in-progress Command and Control Systems - Simulation Systems Interoperation (C2SIM) standardization efforts. The report provides a recommended approach, standards, activities, and timetable for a cross-system communications roadmap.Secretary of Defense Joint Ground Robotics Enterprise, 3090 Defense Pentagon, Room 5C756, Washington, DC 20301Office of the Secretary of Defense Joint Ground Robotics Enterprise.Approved for public release; distribution is unlimited

Behavior Flexibility for Autonomous Unmanned Aerial Systems

Autonomous unmanned aerial systems (UAS) could supplement and eventually subsume a substantial portion of the mission set currently executed by remote pilots, making UAS more robust, responsive, and numerous than permitted by teleoperation alone. Unfortunately, the development of robust autonomous systems is difficult, costly, and time-consuming. Furthermore, the resulting systems often make little reuse of proven software components and offer limited adaptability for new tasks. This work presents a development platform for UAS which promotes behavioral flexibility. The platform incorporates the Unified Behavior Framework (a modular, extensible autonomy framework), the Robotic Operating System (a RSF), and PX4 (an open- source flight controller). Simulation of UBF agents identify a combination of reactive robotic control strategies effective for small-scale navigation tasks by a UAS in the presence of obstacles. Finally, flight tests provide a partial validation of the simulated results. The development platform presented in this work offers robust and responsive behavioral flexibility for UAS agents in simulation and reality. This work lays the foundation for further development of a unified autonomous UAS platform supporting advanced planning algorithms and inter-agent communication by providing a behavior-flexible framework in which to implement, execute, extend, and reuse behaviors

High fidelity progressive reinforcement learning for agile maneuvering UAVs

In this work, we present a high fidelity model based progressive reinforcement learning method for control system design for an agile maneuvering UAV. Our work relies on a simulation-based training and testing environment for doing software-in-the-loop (SIL), hardware-in-the-loop (HIL) and integrated flight testing within photo-realistic virtual reality (VR) environment. Through progressive learning with the high fidelity agent and environment models, the guidance and control policies build agile maneuvering based on fundamental control laws. First, we provide insight on development of high fidelity mathematical models using frequency domain system identification. These models are later used to design reinforcement learning based adaptive flight control laws allowing the vehicle to be controlled over a wide range of operating conditions covering model changes on operating conditions such as payload, voltage and damage to actuators and electronic speed controllers (ESCs). We later design outer flight guidance and control laws. Our current work and progress is summarized in this work

aColor: Mechatronics, Machine Learning, and Communications in an Unmanned Surface Vehicle

The aim of this work is to offer an overview of the research questions, solutions, and challenges faced by the project aColor ("Autonomous and Collaborative Offshore Robotics"). This initiative incorporates three different research areas, namely, mechatronics, machine learning, and communications. It is implemented in an autonomous offshore multicomponent robotic system having an Unmanned Surface Vehicle (USV) as its main subsystem. Our results across the three areas of work are systematically outlined in this paper by demonstrating the advantages and capabilities of the proposed system for different Guidance, Navigation, and Control missions, as well as for the high-speed and long-range bidirectional connectivity purposes across all autonomous subsystems. Challenges for the future are also identified by this study, thus offering an outline for the next steps of the aColor project.Comment: Paper was originally submitted to and presented in the 8th Transport Research Arena TRA 2020, April 27-30, 2020, Helsinki, Finlan