Albert F. Niessner

Optimal Path Planning of UAVs Using Direct Collocation with Nonlinear Programming

A trajectory generation algorithm using direct collocation with nonlinear programming is successfully demonstrated in simulation. Direct collocation, which approximates the states and controls with piecewise polynomials, has been widely used in space and manned aircraft applications, but has only seen limited use in UAV applications. The algorithm is successfully applied to the generation of a UAV trajectory that provides maximum viewing time for a camera mounted on the UAV. The target can be stationary or moving. Multiple UAVs are considered. In this case, the objective is to provide maximum sensor coverage time using a combination of the UAVs. No specific initial guesses are required to ensure the algorithm is successful.

Flight Testing a Real-Time Direct Collocation Path Planner

A path-planning algorithm using direct collocation with nonlinear programming is demonstrated in both simulation and flight tests. Direct collocation, which approximates the states and controls with piecewise polynomials, has been widely applied in space vehicles and manned aircraft, but has only seen limited use in unmanned aerial vehicle applications. The algorithm is successfully used to generate a path that produces maximal surveillance time of a moving or stationary ground target by a sensor mounted on an unmanned aerial vehicle while compensating for aircraft performance or mission constraints. Flight tests of the path-planning algorithm operating in real time onboard an unmanned aerial vehicle are also presented. These tests include surveilling a stationary and moving target with a video camera while compensating for any wind effects. Additionally, the effect of the use of road data in planning the path is simulated by tracking a second unmanned aerial vehicle flying a predefined pattern.

Intelligent Unmanned Air Vehicle Flight Systems

This paper describes an intelligent autonomous airborne flight capability that is being used as a test bed for future technology development. The unmanned air vehicles (UAVs) fly under autonomous control of both an onboard computer and an autopilot. The onboard computer provides the mission control and runs the autonomous Intelligent Controller (IC) software while the autopilot controls the vehicle navigation and flight control. The autonomous airborne flight system is described in detail. An IC architecture directly applicable to the design of unmanned vehicles is also presented. The UAVs may operate independently or in cooperation with one another to carry out a specified mission. The intelligent UAV flight system is used to evaluate and study autonomous UAV control as well as multi-vehicle collaborative control.

Vision-Based Target Geolocation and Optimal Surveillance on an Unmanned Aerial Vehicle

A real-time computer vision algorithm for the identification and geolocation of ground targets was developed and implemented on the Penn State University / Applied Research Laboratory Unmanned Aerial Vehicle (PSU/ARL UAV) system. The geolocation data is filtered using a linear Kalman filter, which provides a smoothed estimate of target location and target velocity. The vision processing routine and estimator are coupled with an onboard path planning algorithm that optimizes the vehicle trajectory to maximize surveillance coverage of the targets. The vision processing and estimation routines were flight tested onboard a UAV system with a human pilot-in-the-loop. It was found that GPS latency had a significant effect on the geolocation error, and performance was significantly improved when using latency compensation. The combined target geolocation and path planning system was tested on the ground using a hardware-in-the-loop simulation, and resulted successful tracking and observation of a fixed target. Timing results showed that is is feasible to implement total system in real time.

Flight Testing a Real Time Implementation of a UAV Path Planner Using Direct Collocation

Flight tests of a path planning algorithm using direct collocation with nonlinear programming (DCNLP) are presented. The path planner operates in real time onboard an unmanned aerial vehicle for these tests. The method plans a path that maximizes the time a target is in view of a camera onboard the aircraft. Tests include surveilling a stationary and moving target while compensating for any wind effects. Additionally, the effect of the use of road data in planning the path is simulated by tracking a second UAV flying a predefined pattern. Finally, a method of commanding the observation of a target from a specific line of sight is presented.

An Intelligent Controller for Collaborative Unmanned Air Vehicles

This paper describes an implementation of an autonomous intelligent controller (IC) architecture for collaborative control of multiple unmanned aerial vehicles (UAVs). Collaborative capabilities include formation flying, search of an area, and cooperative investigation of a target. The IC provides capabilities for sensor data fusion, internal representation of the real-world, and autonomous decision making based on the ICs world model and mission goals. Results of flight tests demonstrating these capabilities are presented. Future work, such as integration of different sensors and collaboration with heterogeneous vehicles, is discussed.

Intelligent Systems Software for Unmanned Air Vehicles

This paper describes a software architecture for mission-level control of autonomous unmanned air vehicles (UAVs). The architecture provides for sensor data fusion, worldview generation, and mission planning and execution. Details about the airborne platform and a high-level description of the control architecture are provided. As an example of the architecture’s versatility a formation flight behavior is described.

Fusion of Unmanned Aerial Vehicle Range and Vision Sensors Using Fuzzy Logic and Particles

This paper presents a novel method for fusing data from a UAV’s range and vision sensors. The range sensor is used to build an elevation map of the flying area. Fuzzy logic is used to detect red barrels in camera images. The world location of a target on the ground is found by fusing the terrain map with image data using both an extended Kalman filter and a particle filter. The target detection system has been tested using images collected onboard a UAV. The terrain mapping system and the fusion system were both tested in simulation.

An Undergraduate Course in Unmanned Air Vehicles

This paper describes some recent hands-on teaching and learning experiences at the Pennsylvania State University in the area of unmanned air vehicles. A two semester course at the senior-level in the Aerospace Engineering Department has been developed to introduce students to unmanned aircraft. The first semester is designed to teach the students about aircraft construction, electric power systems, servos, transmitters and receivers, and aircraft performance. During this semester, each student built and learned to fly a small aircraft with low wing loading. In the second semester, teams of students worked together to build a larger aircraft from an almost-ready-to-fly kit and add onboard sensors such as flight data recorders and wireless cameras to the aircraft. The teams carried out flight tests using these onboard sensors. These hands-on experiences were designed to help students better appreciate their other courses (such as aerodynamics, dynamics and control, structures, and propulsion) and understand aircraft as complex systems. The past, current, and future of unmanned air vehicles and the importance of computer, information, and communication systems in aerospace engineering were also discussed.