Adaptive obstacle avoidance algorithm of collaborative unmanned vehicles integrated monocular cameras in dynamic scene

2021 ◽  
Author(s):  
Yuqi Han
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Vehicles ◽  
Dynamic Scene

UNMANNED VEHICLE OBSTACLE DETECTION AND AVOIDANCE USING DANGER ZONE APPROACH

2013 ◽  
Vol 37 (3) ◽  
pp. 529-538 ◽  
Author(s):  
Ta-Chung Wang ◽  
Tz-Jian Lin
Keyword(s):  
Obstacle Avoidance ◽  
Relative Velocity ◽  
Unmanned Vehicles ◽  
Obstacle Detection ◽  
Servo Motor ◽  
Danger Zone ◽  
Unmanned Vehicle ◽  
Algebraic Sets ◽  
Time Period ◽  
Distance Data

This paper proposes an obstacle avoidance algorithm for unmanned vehicles in unknown environment. The vehicle uses an ultrasonic sensor and a servo motor which rotates from 0 to 180 degrees to obtain the distance data, and the profile of the obstacle. In this avoidance algorithm we will use the danger zone concept to judge whether the obstacle will cause a possible collision. The danger zone concept surrounds the vehicle through the intersection of semi-algebraic sets. These semi-algebraic sets use the relative velocity of the obstacle to calculate the area in which obstacles will collide with the vehicle within a pre-specified time period. Combining the profile of the boundary of the obstacle with the danger zone concept, a method for determining the safe maneuvers to avoid collisions is also provided.

Visual automatic obstacle avoidance technology research in unmanned vehicles

2019 ◽  
Vol 11 (1) ◽  
pp. 65
Author(s):  
Qing Liu ◽  
Rui Jia ◽  
Bo Liu ◽  
Piqiang Tan ◽  
Liguang Li
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Vehicles ◽  
Technology Research

Heterogeneous Bi-Directional Cooperative Unmanned Vehicles for Obstacle Avoidance

2016 ◽  
Author(s):  
Jonathan Lwowski ◽  
Liang Sun ◽  
Daniel Pack
Keyword(s):  
Obstacle Avoidance ◽  
Indoor Environment ◽  
Sliding Mode ◽  
Unmanned Vehicles ◽  
Unmanned Ground Vehicle ◽  
A Algorithm ◽  
Ground Vehicle ◽  
Cluttered Environment ◽  
Aerial View ◽  
Stable Sliding

In this paper, we present a novel bi-directional cooperative obstacle avoidance system of heterogeneous unmanned vehicles, consisting of an unmanned ground vehicle (UGV) and a microaerial vehicle (MAV), equipped with cameras, operating in an indoor environment without Global Positioning System (GPS) signals. The system demonstrates the synergistic relationship between the two platforms by sharing different perspectives and information collected independently using their on-board sensors in performing a navigation task in an indoor environment, including avoiding obstacles and entering narrow pathways. The MAV uses an aerial view of the environment to develop an obstacle free path for the UGV using the A* algorithm. The UGV deploys the planned path in conjunction with information gathered from its own front facing camera to navigate through a cluttered environment using a Lyapunov stable sliding mode controller. The UGV is responsible for detecting low and narrow pathways and to guide the MAV to move through them. The bidirectional cooperation has been tested in hardware as well as in simulation, showing the system’s effectiveness.

scholarly journals Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Robotica ◽  
2014 ◽  
Vol 33 (4) ◽  
pp. 807-827 ◽  
Author(s):  
Sivaranjini Srikanthakumar ◽  
Wen-Hua Chen
Keyword(s):  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Case Analysis ◽  
Unmanned Vehicles ◽  
Optimization Techniques ◽  
Worst Case Analysis ◽  
Physical Parameters ◽  
Worst Case ◽  
Automatic Search ◽  
Moving Obstacle

SUMMARYThis paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

scholarly journals Obstacle Avoidance for a Game Theoretically Controlled Formation of Unmanned Vehicles

2011 ◽  
Vol 44 (1) ◽  
pp. 6023-6028 ◽  
Author(s):  
Mehmet Eren Erdoğan ◽  
Mario Innocenti ◽  
Lorenzo Pollini
Keyword(s):  
Obstacle Avoidance ◽  
Unmanned Vehicles

scholarly journals A Real-Time Hydrodynamic-Based Obstacle Avoidance System for Non-holonomic Mobile Robots with Curvature Constraints

2018 ◽  
Vol 8 (11) ◽  
pp. 2144 ◽  
Author(s):  
Pei-Li Kuo ◽  
Chung-Hsun Wang ◽  
Han-Jung Chou ◽  
Jing-Sin Liu
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Vector Fields ◽  
Unmanned Vehicles ◽  
Integrated System ◽  
Smooth Transition ◽  
Laplace’S Equation ◽  
Laplace's Equation ◽  
Curvature Constraint ◽  
Pure Pursuit

The harmonic potential field of an incompressible nonviscous fluid governed by the Laplace’s Equation has shown its potential for being beneficial to autonomous unmanned vehicles to generate smooth, natural-looking, and predictable paths for obstacle avoidance. The streamlines generated by the boundary value problem of the Laplace’s Equation have explicit, easily computable, or analytic vector fields as the path tangent or robot heading specification without the waypoints and higher order path characteristics. We implemented an obstacle avoidance approach with a focus on curvature constraint for a non-holonomic mobile robot regarded as a particle using curvature-constrained streamlines and streamline changing via pure pursuit. First, we use the potential flow field around a circle to derive three primitive curvature-constrained paths to avoid single obstacles. Furthermore, the pure pursuit controller is implemented to achieve a smooth transition between the streamline paths in the environment with multiple obstacles. In addition to comparative simulations, a proof of concept experiment implemented on a two-wheel driving mobile robot with range sensors validates the practical usefulness of the integrated system that is able to navigate smoothly and safely among multiple cylinder obstacles. The computational requirement of the obstacle avoidance system takes advantage of an a priori selection of fast computing primitive streamline paths, thus, making the system able to generate online a feasible path with a lower maximum curvature that does not violate the curvature constraint.

Unmanned Vehicle Obstacle Avoidance Using Single Sensor Scan Method and Danger Zone Estimation

2013 ◽  
Vol 284-287 ◽  
pp. 1976-1980
Author(s):  
Tz Jian Lin ◽  
Ta Chung Wang
Keyword(s):  
Obstacle Avoidance ◽  
Relative Velocity ◽  
Unmanned Vehicles ◽  
Ultrasonic Sensor ◽  
Servo Motor ◽  
Danger Zone ◽  
Unmanned Vehicle ◽  
Time Period ◽  
Single Sensor ◽  
Distance Data

This paper proposes an obstacle avoidance algorithm for unmanned vehicles in unknown environment by a single sensor. The scan system is composed of an ultrasonic sensor and a servo motor which rotates from 0 to 180 degrees to obtain the distance data, and the profile of the obstacle can be depicted by a histogram which we use to find out the boundary of the obstacle. In this avoidance algorithm we will use the danger zone concept to judge whether the obstacle will cause a possible collision. The danger zone concept surrounds the vehicle by a sphere and uses the relative velocity to calculate the area in which obstacles will collide with the vehicle within a pre-specified time period. Combining the profile of the boundary of the obstacle with the danger zone concept, we can determine the maneuvers to avoid collisions.

Innovative Systems for Human Supervisory Control of Unmanned Vehicles

2012 ◽  
Author(s):  
Andrew S. Clare ◽  
Jason C. Ryan ◽  
Kimberly F. Jackson ◽  
M. L. Cummings
Keyword(s):  
Supervisory Control ◽  
Unmanned Vehicles ◽  
Human Supervisory Control
Sign in / Sign up

Export Citation Format

Share Document