Real-Time Path Planning and Terrain Obstacle Avoidance for General Aviation Aircraft

Doebbler, James; Gesting, Paul; Valasek, John

doi:10.2514/6.2005-5825

Cited by 18 publications

(7 citation statements)

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“…The output parameter of the heading controller, which represents the yaw rate command is built depending on both the range distance between the UAV and the center of the obstacle, and the perpendicular offset between the obstacle center and the current line of sight. The general form of the rules is indicated by Equation (4) and specific rules for each fuzzy logic is given in Equations (5)- (7). Five triangle fuzzy sets are selected for the distance input, and these sets are extended over the universe of discourse in the range [1 6] km as shown in Figure 11.…”

Section: Control and Guidance Systems Attitude Control Systemmentioning

confidence: 99%

“…Another challenge for the operation of autonomous UAVs is to be able to efficiently carry out the assigned tasks without compromising the success of the mission by flying into obstacles and/or restricted areas or becoming exposed to unacceptable levels of threat risk. [5][6][7] As a consequence, researchers working with advanced UAVs have moved their focus from system modeling and low level control to higher level mission planning, system supervision, and collision avoidance aspects, while simultaneously transitioning from vehicle constraints to mission constraints. 8,9 These challenges can be addressed by developing a flexible and powerful trajectory management system which includes a well behaved control and guidance system in additional to optimal path planning and tracking.…”

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confidence: 99%

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Development of UAV Trajectory Management System Based on Fuzzy Logic Concepts

Al-Shehabi

Newman

2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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An important component for any uninhabited aerial vehicle (UAV) to accomplish mission tasks, consisting of crossing several target points while overcoming obstacles along the flight path, is the trajectory management system. This paper presents new methodology for developing UAV trajectory management systems based on integrating flight path planning and generation functions along with trajectory guidance and attitude control functions, implemented with fuzzy logic principles during system design in order to make the UAV capable of accomplishing its mission, especially when damage or failure prevents usage of the primary lateral control input. A method for trajectory management system design in three-dimensional space without aileron capability is proposed and its implementation in a six degree of freedom nonlinear closed-loop flight dynamic system is presented. Examples of two-waypoint missions with stationary obstacles, using the proposed fuzzy logic based trajectory management system designed as a backup failure mode for the UAV, is presented. Simulation results demonstrate the potential of the proposed approach.

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Section: Control and Guidance Systems Attitude Control Systemmentioning

confidence: 99%

mentioning

confidence: 99%

Development of UAV Trajectory Management System Based on Fuzzy Logic Concepts

Al-Shehabi

Newman

2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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“…Simulations that once required proprietary, specialized, and expensive computer mainframe hardware is now executed using common and cheaply procured personal computers with a much wider When simulations began to exhaust the hardware and software resources of a single PC, multiple network-distributed computers began to be used for executing a simulation. The expansion to multiple PCs was due to simulation of large numbers of complex simulated agents and vehicles [14,18,19], considerably complex and high fidelity environments and vehicles [18,20,21,22], standalone commercial-off-the-shelf [17] (COTS) software integration to simulation networks [14,23], and other features and capabilities expanded upon from and by previous generations of architectures [14,24]. Distributed simulations increase scalability, flexibility, and reconfigurability of a simulation environment promoting exchange, reuse, and inter-operation between multiple simulation components [25] Provided drivers are available for the PC networking hardware in use, virtually all modern desktop and server operating systems support communication using these protocols.…”

Section: Hardwarementioning

confidence: 99%

Extensible Software Architecture for a Distributed Engineering Simulation Facility

May

Valasek

2010

AIAA Modeling and Simulation Technologies Conference

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A need has arisen for an easy-to-use, flexible, transparent, and cross-platform communication backbone for configuration and execution of distributed simulations and experiments. Open source, open architecture, and custom student written programs have extended the capabilities of educational research facilities and opened the way for the development of the architecture presented in this thesis. The architecture is known by the recursive acronym hADES: hADES Architecture for Distributed Engineering Simulation. Included in this thesis is a discussion of the design and implementation of the novel hADES software architecture for Ethernet and wireless IEEE 802.11 network-based distributed simulation and experiment facilities. The goal of this architecture is to facilitate rapid integration of new and legacy simulations and laboratory equipment to support undergraduate and graduate research projects as well as educational classroom activities and industrial simulation and experiments.

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“…Hrabar 6) studied real-time three-dimensional path planning for rotorcraft using PRM and D* algorithms. Doebbler 7) also investigated real-time path planning for aviation aircraft using an A* algorithm. Although these algorithms enable rapid path determination, they are not flight-worthy because they consider few or no issues of dynamic correctness.…”

Section: Introductionmentioning

confidence: 99%

Rapid Trajectory Planner for Navigating Complex Terrain Using Random Search and Dynamics Filter Based on Dynamic Inversion

Itakura

Yonemoto

Matsumoto

et al. 2015

AEROSPACE TECHNOLOGY JAPAN

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This paper proposes a rapid trajectory planning algorithm using random search and a dynamics filter based on dynamic inversion. It can rapidly generate a collision-free trajectory for an unmanned aircraft in a highly constrained environment, including complex terrains with obstacles or waypoints. Conventional studies on trajectory generation mainly focus on global optimization; hence, the calculation cost grows exponentially with increased complexity. The proposed trajectory planner consists of two steps to effectively generate a collision-free flight trajectory. First, a sampling-based random search algorithm rapidly determines a collision-free pathway. However, the pathway generated by the random search is calculated geometrically, so the variations in the actual flight are difficult to follow. Therefore, the second step, named dynamics filtering, modifies the initial pathway to a dynamically flyable trajectory with a one-time-only integral calculation. The dynamic inversion technique is utilized to ensure flight-worthiness. In this paper, the implementation and computer simulation results of the proposed trajectory are described. The effectiveness of the proposed method is verified by a six-degrees-of-freedom flight simulation for a Mars exploration aircraft model developed by JAXA and associated research institutes.

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Real-Time Path Planning and Terrain Obstacle Avoidance for General Aviation Aircraft

Cited by 18 publications

References 0 publications

Development of UAV Trajectory Management System Based on Fuzzy Logic Concepts

Development of UAV Trajectory Management System Based on Fuzzy Logic Concepts

Extensible Software Architecture for a Distributed Engineering Simulation Facility

Rapid Trajectory Planner for Navigating Complex Terrain Using Random Search and Dynamics Filter Based on Dynamic Inversion

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