A Unified Approach to Configuration-based Dynamic Analysis of Quadcopters for Optimal Stability

A special type of rotary-wing Unmanned Aerial Vehicles (UAV), called Quadcopter have prevailed to the civilian use for the past decade. They have gained significant amount of attention within the UAV community for their redundancy and ease of control, despite the fact that they fall under an under-actuated system category. They come in a variety of configurations. The "+" and "x" configurations were introduced first. Literature pertinent to these two configurations is vast. However, in this paper, we define 6 additional possible configurations for a Quadcopter that can be built under either "+" or "x" setup. These configurations can be achieved by changing the angle that the axis of rotation for rotors make with the main body, i.e., fuselage. This would also change the location of the COM with respect to the propellers which can add to the overall stability. A comprehensive dynamic model for all these configurations is developed for the first time. The overall stability for these configurations are addressed. In particular, it is shown that one configuration can lead to the most statically-stable platform by adopting damping motion in Roll/Pitch/Yaw, which is described for the first time to the best of our knowledge.Comment: 6 page, 9 figure

Robot to Human Object Handover using Vision and Joint Torque Sensor Modalities

We present a robot-to-human object handover algorithm and implement it on a 7-DOF arm equipped with a 3-finger mechanical hand. The system performs a fully autonomous and robust object handover to a human receiver in real-time. Our algorithm relies on two complementary sensor modalities: joint torque sensors on the arm and an eye-in-hand RGB-D camera for sensor feedback. Our approach is entirely implicit, i.e., there is no explicit communication between the robot and the human receiver. Information obtained via the aforementioned sensor modalities is used as inputs to their related deep neural networks. While the torque sensor network detects the human receiver's "intention" such as: pull, hold, or bump, the vision sensor network detects if the receiver's fingers have wrapped around the object. Networks' outputs are then fused, based on which a decision is made to either release the object or not. Despite substantive challenges in sensor feedback synchronization, object, and human hand detection, our system achieves robust robot-to-human handover with 98\% accuracy in our preliminary real experiments using human receivers.Comment: Note: This paper is submitted to RITA 2022 conference and waiting for result

Propeller Performance In Presence Of Freestream

This paper presents mathematical modeling for thrust force and moments generated by a propeller. In particular, the effects of freestream on propeller’s performance are investigated. We introduce some of the applications of the proposed model in modeling multi-rotor UAVs which helps to increase stability or maneuverability of the vehicle. In the end, simulation results for thrust force and moments of an example propeller in presence of a uniform freestream are presented

Navigation-guidance-based robot trajectory planning for interception of moving objects

grantor: University of TorontoMotivated by current trends in automation of industrial applications, the general problem area addressed in this thesis is "on-line robot-motion planning for intercepting randomly moving objects." The specific objective of the thesis is "the use of navigation-guidance techniques in the development of a generalized scheme that can intercept a randomly moving object with time-optimality". Two novel methods are presented for on-line-robotic-interception of fast-maneuvering objects which do not require a priori information on the moving-object's motion. Both techniques combine a navigation-guidance-based method with a conventional object-tracking technique. Thus, they are classified as 'hybrid' interception schemes with two phases: Phase I during which the robot is under the control of a navigation-guidance-based technique, and Phase II during which the robot's control is switched to a conventional tracking method. For the first proposed method, an Ideal Proportional Navigation Guidance (IPNG) technique is used during Phase I. This technique moves the interceptor rapidly toward the rendezvous point. For the second proposed method, the augmented form of the IPNG technique is suggested for Phase I, when a reliable estimation of the target's acceleration can be provided to the interceptor. Both techniques are modified in this thesis to reflect the greater mobility of a robotic manipulator over an airborne missile for robotic interception of fast-maneuvering targets. Since IPNG techniques have been originally designed for missile guidance, they do not attempt to match the target's velocity at the interception point. In this thesis, for smooth interception, a tracking method is proposed to be switched on to at an optimal time in order to bring the robot to the interception point matching both target's position and velocity. On-line selection of this time-optimal switching point is discussed in the dissertation. The convergence of the proposed interception methods under ideal conditions is addressed. The effect of noise in target's position readings on the on-line estimation of the interception time, and, subsequently, on the overall performance of the proposed technique is also discussed. Extensive computer simulations illustrate the effectiveness of the IPNG-based interception methods developed in this thesis over pure tracking-based techniques proposed in the cited literature.Ph.D

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A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising