Design and development of a novel autonomous scaled multiwheeled vehicle

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Aaron Hao Tan ◽  
Michael Peiris ◽  
Moustafa El-Gindy ◽  
Haoxiang Lang
Keyword(s):  
Autonomous Navigation ◽  
Visual Servoing ◽  
Control Algorithms ◽  
Road Vehicles ◽  
Design And Development ◽  
Passenger Vehicles ◽  
Wheel Drive ◽  
Research Platform ◽  
High Level ◽  
Path Planner

Abstract This article proposes the design and development of a novel custom-built, autonomous scaled multiwheeled vehicle that features an eight-wheel drive and eight-wheel steer system. In addition to the mechanical and electrical design, high-level path planning and low-level vehicle control algorithms are developed and implemented including a two-stage autonomous parking algorithm is developed. A modified position-based visual servoing algorithm is proposed and developed to achieve precise pose correction. The results show significant gains in accuracy and efficiency comparing with an open-source path planner. It is the aim of this work to expand the research of autonomous platforms taking the form of commercial and off-road vehicles using actuated steering and other mechanisms attributed to passenger vehicles. The outcome of this work is a unique autonomous research platform that features independently driven wheels, steering, autonomous navigation, and parking.

Navigation and Obstacle Avoidance of Snake-Robot Guided by a Co-Robot UAV Visual Servoing

2020 ◽  
Author(s):  
Mahdi Haghshenas-Jaryani ◽  
Hakki Erhan Sevil ◽  
Liang Sun
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Visual Servoing ◽  
Integrated System ◽  
Ground Vehicles ◽  
Snake Robot ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Path Planner ◽  
Snake Robots

Abstract This paper presents the concept of teaming up snake-robots, as unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs) for autonomous navigation and obstacle avoidance. Snake robots navigate in cluttered environments based on visual servoing of a co-robot UAV. It is assumed that snake-robots do not have any means to map the surrounding environment, detect obstacles, or self-localize, and these tasks are allocated to the UAV, which uses visual sensors to track the UGVs. The obtained images were used for the geo-localization and mapping the environment. Computer vision methods were utilized for the detection of obstacles, finding obstacle clusters, and then, mapping based on Probabilistic Threat Exposure Map (PTEM) construction. A path planner module determines the heading direction and velocity of the snake robot. A combined heading-velocity controller was used for the snake robot to follow the desired trajectories using the lateral undulatory gait. A series of simulations were carried out for analyzing the snake-robot’s maneuverability and proof-of-concept by navigating the snake robot in an environment with two obstacles based on the UAV visual servoing. The results showed the feasibility of the concept and effectiveness of the integrated system for navigation.

scholarly journals HUMANOID LOCOMOTION PLANNING FOR VISUALLY GUIDED TASKS

2012 ◽  
Vol 09 (02) ◽  
pp. 1250009 ◽  
Author(s):  
JEAN-BERNARD HAYET ◽  
CLAUDIA ESTEVES ◽  
GUSTAVO ARECHAVALETA ◽  
OLIVIER STASSE ◽  
EIICHI YOSHIDA
Keyword(s):  
Visual Servoing ◽  
Nonholonomic Constraints ◽  
Humanoid Robots ◽  
Body Motion ◽  
Whole Body ◽  
Visually Guided ◽  
Open Spaces ◽  
The Gaze ◽  
High Level ◽  
Path Planner

In this work, we propose a landmark-based navigation approach that integrates (1) high-level motion planning capabilities that take into account the landmarks position and visibility and (2) a stack of feasible visual servoing tasks based on footprints to follow. The path planner computes a collision-free path that considers sensory, geometric, and kinematic constraints that are specific to humanoid robots. Based on recent results in movement neuroscience that suggest that most humans exhibit nonholonomic constraints when walking in open spaces, the humanoid steering behavior is modeled as a differential-drive wheeled robot (DDR). The obtained paths are made of geometric primitives that are the shortest in distance in free spaces. The footprints around the path and the positions of the landmarks to which the gaze must be directed are used within a stack-of-tasks (SoT) framework to compute the whole-body motion of the humanoid. We provide some experiments that verify the effectiveness of the proposed strategy on the HRP-2 platform.

scholarly journals Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2534
Author(s):  
Oualid Doukhi ◽  
Deok-Jin Lee
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Autonomous Navigation ◽  
Control Technique ◽  
Local Motion ◽  
Aerial Robots ◽  
Novel Approach ◽  
Outdoor Environments ◽  
Aerial Vehicle ◽  
High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

Using Motivational Modelling With an App Designed to Increase Student Performance and Retention

2020 ◽  
pp. 161-176
Author(s):  
Leon Sterling ◽  
Sonja Pedell ◽  
Grainne Oates
Keyword(s):  
Student Performance ◽  
Mobile Learning ◽  
Design And Development ◽  
Software Applications ◽  
Emotional Factors ◽  
Learning Platform ◽  
High Level ◽  
Modelling Approach

Quitch is software designed to increase student performance and retention. It is a content-neutral, gamified mobile learning platform used across many disciplines, including accounting, chemistry, and engineering. The aim of Quitch is to ensure students feel engaged with their learning. Motivational modelling is a high-level approach to understand the purpose of a system. It is novel in its incorporation of emotional factors. This chapter discusses how the authors applied motivational modelling to Quitch to explain its purpose and potential. The chapter then more generally discusses how their modelling approach can help with the design and development of new software applications especially in the education space.

scholarly journals A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3245
Author(s):  
Tianyao Zhang ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Guofeng Zhang
Keyword(s):  
Machine Learning ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Accuracy ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
High Level

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.

scholarly journals Diversity Maintenance for Efficient Robot Path Planning

2020 ◽  
Vol 10 (5) ◽  
pp. 1721
Author(s):  
Petar Ćurković ◽  
Lovro Čehulić
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Degrees Of Freedom ◽  
Population Based ◽  
Difficult Problem ◽  
Fitness Sharing ◽  
Diversity Maintenance ◽  
Novelty Search ◽  
High Level ◽  
Path Planner

Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.

scholarly journals High-Level Path Planning for an Autonomous Sailboat Robot Using Q-Learning

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1550 ◽  
Author(s):  
Andouglas Gonçalves da Silva Silva Junior ◽  
Davi Henrique dos Santos ◽  
Alvaro Pinto Fernandes de Negreiros ◽  
João Moreno Vilas Boas de Souza Silva ◽  
Luiz Marcos Garcia Gonçalves
Keyword(s):  
Path Planning ◽  
Dead Zone ◽  
Weather Conditions ◽  
Target Point ◽  
Q Learning ◽  
Ground Station ◽  
Global Route ◽  
Global Planning ◽  
High Level ◽  
Path Planner

Path planning for sailboat robots is a challenging task particularly due to the kinematics and dynamics modelling of such kinds of wind propelled boats. The problem is divided into two layers. The first one is global were a general trajectory composed of waypoints is planned, which can be done automatically based on some variables such as weather conditions or defined by hand using some human–robot interface (a ground-station). In the second local layer, at execution time, the global route should be followed by making the sailboat proceed between each pair of consecutive waypoints. Our proposal in this paper is an algorithm for the global, path generation layer, which has been developed for the N-Boat (The Sailboat Robot project), in order to compute feasible sailing routes between a start and a target point while avoiding dangerous situations such as obstacles and borders. A reinforcement learning approach (Q-Learning) is used based on a reward matrix and a set of actions that changes according to wind directions to account for the dead zone, which is the region against the wind where the sailboat can not gain velocity. Our algorithm generates straight and zigzag paths accounting for wind direction. The path generated also guarantees the sailboat safety and robustness, enabling it to sail for long periods of time, depending only on the start and target points defined for this global planning. The result is the development of a complete path planner algorithm that, together with the local planner solved in previous work, can be used to allow the final developments of an N-Boat making it a fully autonomous sailboat.

Research on Camera-Oriented Smart Car and Intelligent Control

2012 ◽  
Vol 260-261 ◽  
pp. 342-347
Author(s):  
Zheng Ma ◽  
Guan Bo Wang
Keyword(s):  
Intelligent Control ◽  
Autonomous Navigation ◽  
Least Squares Method ◽  
Least Square Method ◽  
Least Square ◽  
Control Algorithms ◽  
Processing Unit ◽  
Threshold Method ◽  
Central Processing ◽  
Dc Motor Control

This paper designs a camera-oriented smart car along with specialized intelligent control algorithms. MC9S12XS128 is chosen as the central processing unit and a CCD sensor along with peripheral circuit is designed as the camera module. The proposed system integrates technologies of intelligent control, Micro-Electro-Mechanism System (MEMS), System on Chip (SOC), wireless communication and low power consumption embedded technology, realizing autonomous navigation while tracking the path. In the paper, the extraction of path information is streamlined, where dynamic threshold method is used for image binarization and path optimization is done with least square method. Control algorithms are highlighted, where servo control incorporates least squares method creatively and the DC motor control, forming a closed-loop system with a rotatory encoder, adopts incremental PID control algorithm.

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