Quaternion Feedback Based Autonomous Control of a Quadcopter UAV with Thrust Vectoring Rotors

Kumar, Rumit; Bhargavapuri, Mahathi; Deshpande, Aditya M.; Sridhar, Siddharth; Cohen, Kelly; Kumar, Manish

doi:10.23919/acc45564.2020.9147794

Cited by 15 publications

(3 citation statements)

References 23 publications

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“…Furthermore, it can be argued that more conservative acceleration profiles lead to an increased power efficiency. This assumption can be done by looking at Equation 3, which relates the required thrust to the linear acceleration, as shown in (Kumar et al, 2020).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Fueling Glocal: Optimization-Based Path Planning for Indoor Uavs in an Autonomous Exploration Framework

Cella

d'Apolito

Fanta-Jende

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Exploration is a fundamental problem in robotics that requires robots to navigate through unknown environments to autonomously gather information about their surroundings while executing collision-free paths. In this paper, we propose a method for producing smooth paths during the exploration process in indoor environments using UAVs to improve battery efficiency and enhance the quality of pose estimation. The developed framework is built by merging two approaches that represent the state of the art in the field of autonomous exploration with UAVs. The overall exploration logic is given by GLocal, a paper that introduces an hybrid, i.e. both sampling-based and frontier-based, framework that is able to cope with the issue of odometry drift when exploring indoor environments due to the absence of absolute localization, e.g. through GNSS. The second paper is FUEL, which introduces a frontier-based exploration methodology which computes the drone’s path as an optimized non-uniform B-Spline. The framework described in this paper borrows the optimized B-Spline trajectory generation from FUEL and implements it in GLocal. The presented system is evaluated in two different simulated environments, which show the pros and the cons of such method.

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Section: Experiments and Resultsmentioning

confidence: 99%

Fueling Glocal: Optimization-Based Path Planning for Indoor Uavs in an Autonomous Exploration Framework

Cella

d'Apolito

Fanta-Jende

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…The quaternion representation is used to avoid problems associated to the gimbal lock [39]. Then, the normalised orientation quaternion…”

Section: B Orientation Estimationmentioning

confidence: 99%

An Approach to Acquire Path-Following Skills by Industrial Robots From Human Demonstration

et al. 2021

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Industrial robots have mainly been programmed by operators using teach pendants in a pointto-point scheme with limited sensing capabilities. New developments in robotics have attracted a lot of attention to robot motor skill learning via human interaction using Learning from Demonstration (LfD) techniques. Robot skill acquisition using LfD techniques is characterised by a high-level stage in charge of learning connected actions and a low-level stage concerned with motor coordination and reproduction of an observed path. In this paper, we present an approach to acquire a path-following skill by a robot in the low-level stage which deals with the correspondence of mapping links and joints from a human operator to a robot so that the robot can actually follow a path. We present the design of an Inertial Measurement Unit (IMU) device that is primarily used as an input to acquire the robot skill. The approach is validated using a motion capture system as ground truth to assess the spatial deviation from the human-taught path to the robot's final trajectory.

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“…The mathematical dynamic modeling of the tilt-rotor UAV are discussed in [17]. The PD based control methods and non-linear sliding mode controller design for the tilt-rotor UAV were studied in [18,19,20]. This system has also been described as capable of handling single motor failure during flight [19].…”

Section: Introductionmentioning

confidence: 99%

Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV With Thrust Vectoring Rotors

Deshpande

Kumar

Minai

et al. 2020

Volume 2: Intelligent Transportation/Vehicles; Manufacturing; Mechatronics; Engine/After-Treatment Systems; Soft Actuators/Mani

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In this paper, we present a novel developmental reinforcement learning-based controller for a quadcopter with thrust vectoring capabilities. This multirotor UAV design has tilt-enabled rotors. It utilizes the rotor force magnitude and direction to achieve the desired state during flight. The control policy of this robot is learned using the policy transfer from the learned controller of the quadcopter (comparatively simple UAV design without thrust vectoring). This approach allows learning a control policy for systems with multiple inputs and multiple outputs. The performance of the learned policy is evaluated by physics-based simulations for the tasks of hovering and way-point navigation. The flight simulations utilize a flight controller based on reinforcement learning without any additional PID components. The results show faster learning with the presented approach as opposed to learning the control policy from scratch for this new UAV design created by modifications in a conventional quadcopter, i.e., the addition of more degrees of freedom (4-actuators in conventional quadcopter to 8-actuators in tilt-rotor quadcopter). We demonstrate the robustness of our learned policy by showing the recovery of the tilt-rotor platform in the simulation from various non-static initial conditions in order to reach a desired state. The developmental policy for the tilt-rotor UAV also showed superior fault tolerance when compared with the policy learned from the scratch. The results show the ability of the presented approach to bootstrap the learned behavior from a simpler system (lower-dimensional action-space) to a more complex robot (comparatively higher-dimensional action-space) and reach better performance faster.

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Quaternion Feedback Based Autonomous Control of a Quadcopter UAV with Thrust Vectoring Rotors

Cited by 15 publications

References 23 publications

Fueling Glocal: Optimization-Based Path Planning for Indoor Uavs in an Autonomous Exploration Framework

Fueling Glocal: Optimization-Based Path Planning for Indoor Uavs in an Autonomous Exploration Framework

An Approach to Acquire Path-Following Skills by Industrial Robots From Human Demonstration

Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV With Thrust Vectoring Rotors

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