Mobile-manipulating UAVs for Sensor Installation, Bridge Inspection and Maintenance

Mobile manipulating UAVs have great potential for bridge inspection and maintenance. Since 2002, the PI has developed UAVs that could fly through in-and-around buildings and tunnels. Collision avoidance in such cluttered near-Earth environments has been a key challenge. The advent of light-weight, computationally powerful cameras led to breakthroughs in SLAM even though SLAM-based autonomous aerial navigation around bridges remains an unsolved problem. In 2007, the PI integrated a mobile manipulation function into UAVs, greatly extending the capabilities of UAVs from passive survey of environments with cameras to active interaction with environments using limbs. Mobile-manipulating UAVs have since been demonstrated to successfully turn valves, install sensors, open doors, and drag ropes. Their research and development face several challenges. First, limbs add weight to aircraft. Second, rotorcraft, like a quadcopter, is an under-actuated system whose stability can be easily affected by limb motions. Third, when performing a task like turning a valve, limbs demand compensation for torque-force interactions. Thus, even if battery technologies afford the additional payload of limbs, current knowledge for manipulation with under-actuated systems remains sparse. This project aims to develop and prototype a mobile-manipulating UAV for bridge maintenance and disaster cleanup through further study on SLAM technology for robust navigation, impedance controllers to ensure UAV’s stability with limb motion, and coordinated and cooperative motions of multiple limbs to perform simple tasks like bearings cleaning and crack sealing in concrete bridges. Two strategies will be explored for bridge maintenance: (a) A UAV brings and uses a can of compressed air for bridge cleaning, and (2) Two UAVs airlift, position, and operate hoses from ground, and clean bridges with air or water. The latter can be potentially implemented by including a station-keeping, lighter-than-air UAV like blimp that can airlift a hose and remain airborne for extended periods. The mobile-limbed UAVs can then pull-and-drag the hose into areas that need to be cleaned. The blimp-based approach is attractive because it is easier for a UAV to drop hose lengths rather than pull the hose up in air

Autonomous cargo transport system for an unmanned aerial vehicle, using visual servoing

ABSTRACT This paper presents the design and testing of a system for autonomous tracking, pickup, and delivery of cargo via an unmanned helicopter. The tracking system uses a visual servoing algorithm and is tested using open loop velocity control of a six degree of freedom gantry system with a camera mounted via a pan-tilt unit on the end effecter. The pickup system uses vision to direct the camera pan tilt unit to track the target, and uses a hook attached to a second pan tilt unit to pick up the cargo. The ability of the pickup system to hook a target is tested by mounting it on the Systems Integrated Sensor Test Rig gantry system while recorded helicopter velocities are played back by the test rig.

Visual and Kinematic Coordinated Control of Mobile Manipulating Unmanned Aerial Vehicles

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Historically, UAVs have been employed in ways that avoid interaction with the environment at all costs. The recent trend of increasing small UAV lift capacity and the reduction of the weight of manipulator components make the realization of mobile manipulating UAVs imminent. Despite recent work, several major challenges remain to be overcome before it will be common practice to manipulate objects from UAVs. Among these challenges, the constantly moving UAV platform and compliance of manipulator arms make it difficult to position the UAV and end-effector relative to an object of interest precisely enough for reliable manipulation. Solving this challenge will bring UAVs one step closer to being able to perform meaningful tasks such as infrastructure repair, disaster response, law enforcement, and personal assistance. Toward a solution to this challenge, this thesis describes a way forward that uses the UAV as a means to crudely position a manipulator within reach of the end-effector's goal position in the world. The manipulator then performs the fine positioning of the end-effector, rejecting position perturbations caused by UAV motions. An algorithm to coordinate the redundant degrees of freedom of an aerial manipulation system is described that allows the motions of the manipulator to serve as inputs to the UAV's position controller. To demonstrate this algorithm, the manipulator's six degrees of freedom are servoed using visual sensing to drive an eye-in-hand camera to a specified pose relative to a target while treating motions of the host platform as perturbations. Simultaneously, the host platform's degrees of freedom are regulated using kinematic information from the manipulator. This ultimately drives the UAV to a position that allows the manipulator to assume a pose relative to the UAV that maximizes reachability, thus facilitating the arm's ability to compensate for undesired UAV motions. Maintaining this loose kinematic coupling between the redundant degrees of freedom of the host UAV and manipulator allows this type of controller to be applied to a wide variety of platforms, including manned aircraft, rather than a single instance of a purpose-built system. As a result of this loose coupling, careful consideration must be given to the manipulator design so that it can achieve useful poses while minimally influencing the stability of the host UAV. Accordingly, the novel application of a parallel manipulator mechanism is described.Ph.D., Mechanical Engineering -- Drexel University, 201

Application of Machine Vision in UAVs for Autonomous Target Tracking

This research presents experimental results for the application of Machine Vision (MV) techniques to address the problem of target detection and tracking. The main objective is the design of a prototype UAV surveillance environment to emulate real-life conditions. The model environment for this experiment consists of a target simulated by a small electric train system, located at ground level, and a MV camera mounted on a motion-based apparatus located directly above the model setup. This system is meant to be a non-flying mockup of an aerial robot retrofitted with a MV sensor. Therefore, the final design is a two degree-of-freedom gantry simulating aircraft motions above the ground level at a constant altitude. On the ground level, the design of the landscape is an attempt to achieve a realistic natural landscape within a laboratory setting. Therefore, the scenery consists of small scale trees, bushes, a mountain, and a tunnel system within a 914 mm by 1066 mm boundary. To detect and track the moving train, MV algorithms are implemented in a Matlab/SimulinkRTM based simulation environment. Specifically, image pre-processing techniques and circle detection algorithms are implemented to detect and identify the chimney stack on the train engine. The circle detection algorithms analyzed in this research effort consists of a least squares based method and the Hough transform (HT) method for circle detection. The experimental results will show that the solution to the target detection problem could produce a positive detection rate of 90% during each simulation while utilizing only 56% of the input image. Tracking and timing data also shows that the least squares based target detection method performs substantially better then the HT method. This is evident from the result of using a 1--2 Hz frequency update rate for the SimulinkRTM scheme which is acceptable, in some cases, for use in navigation for a UAV performing scouting and reconnaissance missions. The development of vision-based control strategies, similar to the approach presented in this research, allows UAVs to participate in complex missions involving autonomous target tracking

A hardware-in-the-loop testing facility for unmanned aerial vehicle sensor suites and control algorithms

In the past decade Unmanned Aerial Vehicles (UAVs) have rapidly grown into a major field of robotics in both industry and academia. Many well established platforms have been developed, and the demand continues to grow. However, the UAVs utilized in industry are predominately remotely piloted aircraft offering very limited levels of autonomy. In contrast, fully autonomous flight has been achieved in research, and the degree of autonomy continues to grow, with research now focusing on advanced tasks such as navigating cluttered terrain and formation ying.The gap between academia and industry is the robustness of control algorithms. Academic research often focuses on proof of concept demonstrations with little or no consideration to real world concerns such as adverse weather or sensor integration.One of the goals of this thesis is to integrate real world issues into the design process. A testing environment was designed and built that allows sensors and control algorithms to be tested against real obstacles and environmental conditions in a controlled, repeatable fashion. The use of this facility is demonstrated in the implementation of a safe landing zone algorithm for a robotic helicopter equipped with a laser scanner. Results from tests conducted in the testing facility are used to analyze results from ights in the field.Controlling the testing environment also provides a baseline to evaluate different control solutions. In the current research paradigm, it is difficult to determine which research questions have been solved because the testing conditions vary from researcher to researcher. A common testing environment eliminates ambiguities and allows solutions to be characterized based on their performance in different terrains and environmental conditions.This thesis explores how flight tests can be conducted in the lab using the actual hardware and control algorithms. The sensor package is attached to a 6 DOF gantry whose motion is governed by the dynamic model of the aircraft. To provide an expansive terrain over which the flight can be conducted, a scaled model of the environment was created.The the feasibility of using a scaled environment is demonstrated with a common sensor package and control task: using computer vision to guide an autonomous helicopter. The effcts of scaling are investigated, and the approach validated by comparing results in the scaled model to actual flights. Finally, it is demonstrated how the facility can be used to investigate the effect of adverse conditions on control algorithm performance. The overarching philosophy of this work is that incorporating real world concerns into the design process leads to more fully developed and robust solutions.Ph.D., Mechanical Engineering -- Drexel University, 201

P.Y.,”A Hardware-in-the-Loop Test Rig for Designing Near-Earth Aerial Robotics”,IEEE

Today’s aerial robots are being tasked to fly in near-Earth environments such as caves, forests and buildings. The lack of flight data and performance metrics poses a gap that prevents the analytical design of such robots. This paper describes a test rig with a full-scale diorama in its workspace. Lamps, fans, and generators allow the control of lighting, gust and obscurants to emulate conditions found in near-Earth environments. The rig’s motions resemble the actual robotic aircraft through model reference adaptive control; sensor data feed into a high-fidelity math model of the aircraft’s dynamics to generate rig motion response.