Development of an Indoor Multirotor Testbed for Experimentation on Autonomous Guidance Strategies

Despite the vast popularity of rotary wing unmanned aerial vehicles and research centres that develop their guidance software, there are only a limited number of references that provide an exhaustive description of a step-by-step procedure to build-up a multirotor testbed. In response to such need, the first part of this thesis aims to describe, in detail, the complete procedure to establish and operate an autonomous multirotor unmanned aerial vehicle indoor experimental platform to test and validate guidance, navigation and control strategies. Both hardware and software aspects of the testbed are described to offer a complete understanding of the different aspects. The second part of this thesis focuses on two benchmarks multirotor guidance, navigation and control problems. Initially, the guidance law for an accurate landing manoeuvre is studied. Multirotor usually have a flight time limited to a few minutes. Autonomous landing and docking to a charging station could extend the mission duration of these vehicles. Subsequently, the guidance strategy for the formation flight between two multirotors is considered. In this case, the fundamental goal is an accurate autonomous alignment between two vehicles, each of them behaving as a target and chaser simultaneously. In the last part of this thesis, the problem of minimum energy manoeuvres is tackled. Again, in this case, the motive is to address the limitation in multirotor flight duration. The fundamental objective of this guidance, navigation and control strategy is to determine and implement, in real-time, the minimum energy control histories that transfer the multirotor from its initial point to a given final point. As opposed to conventional guidance strategies, mostly based on proportional-integral-derivative laws, a minimum energy controller allows the vehicle to execute the manoeuvre with a minimum electrical power expenditure

Optimized Endpoint Delivery Via Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) are remotely piloted aircraft with a range of varying applications. Though early adoption of UAVs focused on military applications, surveillance, photography, and agricultural applications are presently on the rise. This work aims to ascertain how UAVs may be employed to elicit deceased transportation times, increased power efficiency, and improved safety. Resulting in optimized end point delivery. A combination of tools and techniques, involving a mathematical model, UAV simulations, redundant control systems, and custom designed electrical and mechanical components were used towards reaching the goal of a 10-kilogram maximum payload delivered 10 miles under 30 minutes. Two UAV prototypes were developed, the second of which (V2) showed promising results. Velocities achieved in V2, in combination with a versatile payload connector and proper networking, allowed for 5-10 mile deliveries of goods less than 8-kilograms to be achieved within a metropolis faster than the 30-minute benchmark

MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV

This paper focuses on a novel robotic system MorphoLander representing heterogeneous swarm of drones for exploring rough terrain environments. The morphogenetic leader drone is capable of landing on uneven terrain, traversing it, and maintaining horizontal position to deploy smaller drones for extensive area exploration. After completing their tasks, these drones return and land back on the landing pads of MorphoGear. The reinforcement learning algorithm was developed for a precise landing of drones on the leader robot that either remains static during their mission or relocates to the new position. Several experiments were conducted to evaluate the performance of the developed landing algorithm under both even and uneven terrain conditions. The experiments revealed that the proposed system results in high landing accuracy of 0.5 cm when landing on the leader drone under even terrain conditions and 2.35 cm under uneven terrain conditions. MorphoLander has the potential to significantly enhance the efficiency of the industrial inspections, seismic surveys, and rescue missions in highly cluttered and unstructured environments.Comment: Accepted paper at the 2023 IEEE Conference on Systems, Man, and Cybernetic

Vision-Based UAV Landing with Guaranteed Reliability in Adverse Environment

open access articleSafe and accurate landing is crucial for Unmanned Aerial Vehicles (UAVs). However, it is a challenging task, especially when the altitude of the landing target is different from the ground and when the UAV is working in adverse environments, such as coasts where winds are usually strong and changing rapidly. UAVs controlled by traditional landing algorithms are unable to deal with sudden large disturbances, such as gusts, during the landing process. In this paper, a reliable vision-based landing strategy is proposed for UAV autonomous landing on a multi-level platform mounted on an Unmanned Ground Vehicle (UGV). With the proposed landing strategy, visual detection can be retrieved even with strong gusts and the UAV is able to achieve robust landing accuracy in a challenging platform with complex ground effects. The effectiveness of the landing algorithm is verified through real-world flight tests. Experimental results in farm fields demonstrate the proposed method’s accuracy and robustness to external disturbances (e.g., wind gusts)

ODDISY Drone Dispatch System

Commercial applications that require autonomous air platforms are becoming more prevalent, however there is a lack of commercially available ground stations that enable remote takeoff and landing. This project served to design and fabricate a ground station and custom UAV interface to allow remote landing, storage, and takeoff of autonomous drones for commercial applications. This included hardware in the base station responsible for charging and protecting the drone with a weatherproof enclosure for storage. The drone was autonomously controlled using a high accuracy GNSS combined with custom control software to follow flight paths and land within the ground station. The drone is extendable and can mount various standard sensor suites to serve a wide range of commercial applications

Automated multi-rotor draft survey of large vessels

In maritime sector draft survey has a significant importance as it is used to determine many important factors used in maritime transportation. Draft is the vertical displacement from the bottom of the keel (the bottom-most element of a vessel) to the water line (the line of meeting point of hull and the water surface). It is used to measure the minimum water depth for safe navigation of vessel and to evaluate mass of cargo in the vessel by the change in displacement on the draft scale after loading of the cargo in the vessel. Draft measurement of a vessel has a vital role in maritime sector to ensure a safe equilibrium between maximum and minimum cargo that can be loaded in the vessel. Draft survey performed at the time of loading and unloading of cargo (Iron Ore) at the Narvik port to read out draft markings traditionally involved a round trip around the vessel in a small crew boat and it is a time consuming and challenging task specially in darkness (during night), shadows and when difficult to safely reach the crew boat close enough due to anchors and buoys. The goal of this study is to develop an autonomous multi-rotor system that can survey the large vessel to capture all the necessary draft measurements by reaching close enough even in challenging environments like nighttime and in presence of obstacles. This involves developing the solution for path planning to perform flight operation autonomously, developing guidance and control algorithm for the flight operation to enable the multi-rotor to follow the designated path and perform the inspection while avoiding all the hurdles using collision avoidance system. Along with developing the specifications for a multi-rotor that can perform the inspection and suggest necessary system components including multi-rotor itself and additional components such as sensors, lights and camera, and necessities for on-board data handling

Biologically inspired perching for aerial robots

2021 Spring.Includes bibliographical references.Micro Aerial Vehicles (MAVs) are widely used for various civilian and military applications (e.g., surveillance, search, and monitoring, etc.); however, one critical problem they are facing is the limited airborne time (less than one hour) due to the low aerodynamic efficiency, low energy storage capability, and high energy consumption. To address this problem, mimicking biological flyers to perch onto objects (e.g., walls, power lines, or ceilings) will significantly extend MAVs' functioning time for surveillance or monitoring related tasks. Successful perching for aerial robots, however, is quite challenging as it requires a synergistic integration of mechanical and computational intelligence. Mechanical intelligence means mechanical mechanisms to passively damp out the impact between the robot and the perching object and robustly engage the robot to the perching objects. Computational intelligence means computation algorithms to estimate, plan, and control the robot's motion so that the robot can progressively reduce its speed and adjust its orientation to perch on the objects with a desired velocity and orientation. In this research, a framework for biologically inspired perching is investigated, focusing on both computational and mechanical intelligence. Computational intelligence includes vision-based state estimation and trajectory planning. Unlike traditional flight states such as position and velocity, we leverage a biologically inspired state called time-to-contact (TTC) that represents the remaining time to the perching object at the current flight velocity. A faster and more accurate estimation method based on consecutive images is proposed to estimate TTC. Then a trajectory is planned in TTC space to realize the faster perching while satisfying multiple flight and perching constraints, e.g., maximum velocity, maximum acceleration, and desired contact velocity. For mechanical intelligence, we design, develop, and analyze a novel compliant bistable gripper with two stable states. When the gripper is open, it can close passively by the contact force between the robot and the perching object, eliminating additional actuators or sensors. We also analyze the bistability of the gripper to guide and optimize the design of the gripper. At the end, a customized MAV platform of size 250 mm is designed to combine computational and mechanical intelligence. A Raspberry Pi is used as the onboard computer to do vision-based state estimation and control. Besides, a larger gripper is designed to make the MAV perch on a horizontal rod. Perching experiments using the designed trajectories perform well at activating the bistable gripper to perch while avoiding large impact force which may damage the gripper and the MAV. The research will enable robust perching of MAVs so that they can maintain a desired observation or resting position for long-duration inspection, surveillance, search, and rescue