Unmanned Systems

UAVForge was a Defense Advanced Research Projects Agency (DARPA) and Space and Naval Warfare Systems Center, Atlantic (SSC Atlantic) initiative to leverage the exchange of ideas among an international community united through common interests and inspired by creative thought. More than 140 teams and 3500 registered citizen scientists from 153 countries participated in this year long event. From several selection rounds, a core of nine teams competed in the fly-off event and in June 2012 Team HALO from the UK was declared the winner scoring 47.7 points out of a total of 60 points, with their co-axial tri-rotor, Y6 (VTOL) small Unmanned Aircraft System (sUAS).

This paper looks into conceptualization, implementation and validation of the potential role that hybrid quad tilt rotor unmanned aerial vehicles (UAV) may undertake in the near future, especially so in military applications. A tilt rotor is designed, built and analyzed with wind tunnel data, theoretical calculations and flight log data. In addition, the paper will discuss the flight mechanics involved during its transition from hover to forward flight. As the tandem wing tilt rotor encompasses two unconventional designs, numerous mathematical models were developed in the design of the UAV.

From September 2011 through May 2012, icarusLabs competed in the UAVForge Competition, sponsored by the United States Defense Advanced Research Projects Agency (DARPA). The objective of the competition was to design and build a small unmanned aerial vehicle (UAV) that could complete a local surveillance mission. A unique feature of the competition was an experimental crowdsourcing model, in which competing teams were able to provide technical feedback to each other as part of the design process. The icarusLabs team's solution to the challenge combined a tricopter and blended wing body aircraft into a single hybrid airframe. This paper presents the rationale and theory behind the design as well as the lessons learned from the technical and logistical challenges faced. In addition, the authors reflect on the crowdsourcing aspect of the challenge from a competitor's perspective. Overall, incentives for individual teams' success directly conflicted with the essence of crowdsourcing, but the competition was successful at stimulating interest to fill a void in current UAV solutions, bringing together passionate individuals from the global unmanned systems community, and generating a broad range of novel approaches to address the presented challenges.

In this paper, we present a comprehensive design for a fully functional unmanned rotorcraft system: GremLion. GremLion is a new small-scale unmanned aerial vehicle (UAV) concept using two contra-rotating rotors and one cyclic swash-plate. It can fit within a rucksack and be easily carried by a single person. GremLion is developed with all necessary avionics and a ground control station. It has been employed to participate in the 2012 UAVForge competition. The proposed design of GremLion consists of hardware construction, software development, dynamics modeling and flight control design, as well as mission algorithm investigation. A novel computer-aided technique is presented to optimize the hardware construction of GremLion to realize robust and efficient flight behavior. Based on the above hardware platform, a real-time flight control software and a ground control station (GCS) software have been developed to achieve the onboard processing capability and the ground monitoring capability respectively. A GremLion mathematical model has been derived for hover and near hover flight conditions and identified from experimental data collected in flight tests. We have combined H_∞ technique, a robust and perfect tracking (RPT) approach, and custom-defined flight scheduling to design a comprehensive nonlinear flight control law for GremLion and successfully realized the automatic control which includes take-off, hovering, and a variety of essential flight motions. In addition, advanced mission algorithms have been presented in the paper, including obstacle detection and avoidance, as well as target following. Both ground and flight experiments of the complete system have been conducted including autonomous hovering, waypoint flight, etc. The test results have been presented in this paper to verify the proposed design methodology.

This research paper is about team Dhaksha's accomplishment in designing, developing and testing a slew of Rotary Wing Mini Unmanned Aerial Systems for entry into various international aerial robotics/unmanned aerial vehicle (UAV) competitions and civilian applications. Dhaksha, the Unmanned Aircraft System (UAS), developed by the team at Madras Institute of Technology (MIT) campus of Anna University, Chennai, Tamil Nadu, India, with its stable design presented stiff competition to other contestants during the May 2012 technology demonstration called UAVForge organized by Defense Advanced Research Project Agency (DARPA), Department of Defense, USA. Team Dhaksha on behalf of national defense research agency deployed their system as a test bed for Acoustic field testing and analysis. The team also deployed their UAS, for state police, in a religious festival, over a crowd of 20 lakh pilgrims during November 2012, to avail instant aerial images. UAS Dhaksha was deployed for investigation of structural strength of the India's tallest structure, a 300 m high Reinforced Cement Concrete (RCC) tower. Recently during the flash floods in the Himalayan river Mandakini at an altitude of 4200 m above mean sea level, Dhaksha assisted the forces in the relief and rescue operations by providing instant thermal/video images of the scene of disaster.

A dynamic inversion-based three-dimensional nonlinear aiming point guidance law is presented in this paper for reactive collision avoidance of unmanned aerial vehicles. When an obstacle is detected in the close vicinity and collision is predicted, an artificial safety sphere is put around the center of the obstacle. Next, the velocity vector of the vehicle is realigned towards an 'aiming point' on the surface of the sphere in such a way that passing through it can guarantee safe avoidance of the obstacle. The guidance command generation is based on angular correction between the actual and the desired direction of the velocity vector. Note that the velocity vector gets aligned along the selected aiming point quickly (i.e., within a fraction of the available time-to-go), which makes it possible to avoid pop-up obstacles. The guidance algorithm has been verified with simulations carried out both for single obstacles as well as for multiple obstacles on the path and also with different safety sphere sizes around the obstacles. The proposed algorithm has been validated using both kinematic as well as point mass model of a prototype unmanned aerial vehicle. For better confidence, results have also been validated by incorporating a first-order autopilot models for the velocity vector magnitude and directions.

A successful mission for an Unmanned Air System (UAS) often depends on the ability of human operators to utilize data collected from onboard imaging sensors. Many hours are spent preparing and executing flight objectives, putting a tremendous burden on human operators both before and during the flight. We seek to automate the planning process to reduce the workload for UAS operators while also optimizing the quality of the collected video stream. We first propose a metric based on an existing image utility metric to estimate the utility of video captured by onboard cameras. We then use this metric to not only plan the UAS flight path, but also the path of the camera's optical axis projected along the terrain and the zoom level. Since computing an optimal solution is NP-hard and therefore infeasible, we subsequently describe a staged sub-optimal path planning approach to autonomously plan the UAS flight path and sensor schedule. We apply these algorithms to precompute UAS and sensor paths for a surveillance mission over a specified region. Simulated and actual flight test results are included.