Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach

Research in the field of automated driving has created promising results in the last years. Some research groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. Yet, what is often missing are general-purpose path- or trajectory planners which are not designed for a specific purpose. In this paper we look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks can contribute to generalized path- and trajectory generation approaches for generating safe trajectories even in cases of system failures.Comment: Presented at IEEE Intelligent Vehicles Symposium 2017, Los Angeles, CA, US

A Planning Pipeline for Large Multi-Agent Missions

In complex multi-agent applications, human operators are often tasked with planning and managing large heterogeneous teams of humans and autonomous vehicles. Although the use of these autonomous vehicles broadens the scope of meaningful applications, many of their systems remain unintuitive and difficult to master for human operators whose expertise lies in the application domain and not at the platform level. Current research focuses on the development of individual capabilities necessary to plan multi-agent missions of this scope, placing little emphasis on the integration of these components in to a full pipeline. The work presented in this paper presents a complete and user-agnostic planning pipeline for large multiagent missions known as the HOLII GRAILLE. The system takes a holistic approach to mission planning by integrating capabilities in human machine interaction, flight path generation, and validation and verification. Components modules of the pipeline are explored on an individual level, as well as their integration into a whole system. Lastly, implications for future mission planning are discussed

Hypersonic Research Vehicle (HRV) real-time flight test support feasibility and requirements study. Part 2: Remote computation support for flight systems functions

The requirements are assessed for the use of remote computation to support HRV flight testing. First, remote computational requirements were developed to support functions that will eventually be performed onboard operational vehicles of this type. These functions which either cannot be performed onboard in the time frame of initial HRV flight test programs because the technology of airborne computers will not be sufficiently advanced to support the computational loads required, or it is not desirable to perform the functions onboard in the flight test program for other reasons. Second, remote computational support either required or highly desirable to conduct flight testing itself was addressed. The use is proposed of an Automated Flight Management System which is described in conceptual detail. Third, autonomous operations is discussed and finally, unmanned operations

Definition of avionics concepts for a heavy lift cargo vehicle. Volume 1: Executive summary

A cost effective, multiuser simulation, test, and demonstration facility to support the development of avionics systems for future space vehicles is examined. The technology needs and requirements of future Heavy Lift Cargo Vehicles (HLCVs) are analyzed and serve as the basis for sizing of the avionics facility, although the lab is not limited in use to support of HLCVs. Volume 1 provides a summary of the vehicle avionics trade studies, the avionics lab objectives, a summary of the lab's functional requirements and design, physical facility considerations, and cost estimates

Proceedings of the 2nd Annual Conference on NASA/University Advanced Space Design Program

Topics discussed include: lunar transportation system, Mars rover, lunar fiberglass production, geosynchronous space stations, regenerative system for growing plants, lunar mining devices, lunar oxygen transporation system, mobile remote manipulator system, Mars exploration, launch/landing facility for a lunar base, and multi-megawatt nuclear power system

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

Trajectory design for autonomous underwater vehicles based on ocean model predictions for feature tracking

Trajectory design for Autonomous Underwater Vehicles (AUVs) is of great importance to the oceanographic research community. Intelligent planning is required to maneuver a vehicle to high-valued locations for data collection. We consider the use of ocean model predictions to determine the locations to be visited by an AUV, which then provides near-real time, in situ measurements back to the model to increase the skill of future predictions. The motion planning problem of steering the vehicle between the computed waypoints is not considered here. Our focus is on the algorithm to determine relevant points of interest for a chosen oceanographic feature. This represents a first approach to an end to end autonomous prediction and tasking system for aquatic, mobile sensor networks. We design a sampling plan and present experimental results with AUV retasking in the Southern California Bight (SCB) off the coast of Los Angeles