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.

Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

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.

Bio-Inspired Snake Robots

2018 ◽  
pp. 246-275 ◽  
Author(s):  
Mohammadali Javaheri Koopaee ◽  
Cid Gilani ◽  
Callum Scott ◽  
XiaoQi Chen
Keyword(s):  
Future Research ◽  
Snake Robot ◽  
Cluttered Environments ◽  
Flat Surfaces ◽  
Robot Locomotion ◽  
Control Schemes ◽  
Unstructured Environment ◽  
And Control ◽  
World Environment ◽  
Snake Robots

This chapter concerns modelling and control of snake robots. Specifically, the authors' main goal is introducing some of the fundamental design, modelling, and control approaches introduced for efficient snake robot locomotion in cluttered environments. This is a critical topic because, unlike locomotion in flat surfaces, where pre-specified gait equations can be employed, for locomotion in unstructured environment more sophisticated control approaches should be used to achieve intelligent and efficient mobility. To reach this goal, shape-based modelling approaches and a number of available control schemes for operation in unknown environments are presented, which hopefully motivates more scholars to start working on snake robots. Some ideas about future research plans are also proposed, which can be helpful for fabricating a snake robot equipped with the necessary features for operation in a real-world environment.

scholarly journals Method for SLAM Based on Omnidirectional Vision: A Delayed-EKF Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rodrigo Munguía ◽  
Carlos López-Franco ◽  
Emmanuel Nuño ◽  
Adriana López-Franco
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Autonomous Navigation ◽  
Real Data ◽  
Omnidirectional Vision ◽  
Wide Field ◽  
Visual Sensor ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Wide Field Of View

This work presents a method for implementing a visual-based simultaneous localization and mapping (SLAM) system using omnidirectional vision data, with application to autonomous mobile robots. In SLAM, a mobile robot operates in an unknown environment using only on-board sensors to simultaneously build a map of its surroundings, which it uses to track its position. The SLAM is perhaps one of the most fundamental problems to solve in robotics to build mobile robots truly autonomous. The visual sensor used in this work is an omnidirectional vision sensor; this sensor provides a wide field of view which is advantageous in a mobile robot in an autonomous navigation task. Since the visual sensor used in this work is monocular, a method to recover the depth of the features is required. To estimate the unknown depth we propose a novel stochastic triangulation technique. The system proposed in this work can be applied to indoor or cluttered environments for performing visual-based navigation when GPS signal is not available. Experiments with synthetic and real data are presented in order to validate the proposal.

scholarly journals Wavefront Propagation and Fuzzy Based Autonomous Navigation

10.5772/5799 ◽  
2005 ◽  
Vol 2 (2) ◽  
pp. 10 ◽  
Author(s):  
Adel Al-Jumaily ◽  
Cindy Leung
Keyword(s):  
Obstacle Avoidance ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Possibility Theory ◽  
Laser Sensor ◽  
Wavefront Propagation ◽  
Good Path ◽  
Enhance Efficiency ◽  
Path Planner

Path planning and obstacle avoidance are the two major issues in any navigation system. Wavefront propagation algorithm, as a good path planner, can be used to determine an optimal path. Obstacle avoidance can be achieved using possibility theory. Combining these two functions enable a robot to autonomously navigate to its destination. This paper presents the approach and results in implementing an autonomous navigation system for an indoor mobile robot. The system developed is based on a laser sensor used to retrieve data to update a two dimensional world model of therobot environment. Waypoints in the path are incorporated into the obstacle avoidance. Features such as ageing of objects and smooth motion planning are implemented to enhance efficiency and also to cater for dynamic environments.

Autonomous Navigation and Obstacle Avoidance

2009 ◽  
Author(s):  
Jesse Berger ◽  
Cory Carson ◽  
Massood Towhidnejad ◽  
Richard Stansbury
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation

scholarly journals Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 230
Author(s):  
Xiangwei Dang ◽  
Zheng Rong ◽  
Xingdong Liang
Keyword(s):  
State Estimation ◽  
Autonomous Navigation ◽  
Moving Objects ◽  
Physical Simulation ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Real World Datasets

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

Trajectory Planning and Obstacle Avoidance for Hyper-Redundant Serial Robots

2017 ◽  
Vol 9 (4) ◽  
Author(s):  
Midhun S. Menon ◽  
V. C. Ravi ◽  
Ashitava Ghosal
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Medical Robotics ◽  
Theoretical Development ◽  
Wheeled Mobile Robots ◽  
Calculus Of Variation ◽  
Redundant Robot ◽  
Cluttered Environments ◽  
Serial Robots ◽  
Actuated Joints

Hyper-redundant snakelike serial robots are of great interest due to their application in search and rescue during disaster relief in highly cluttered environments and recently in the field of medical robotics. A key feature of these robots is the presence of a large number of redundant actuated joints and the associated well-known challenge of motion planning. This problem is even more acute in the presence of obstacles. Obstacle avoidance for point bodies, nonredundant serial robots with a few links and joints, and wheeled mobile robots has been extensively studied, and several mature implementations are available. However, obstacle avoidance for hyper-redundant snakelike robots and other extended articulated bodies is less studied and is still evolving. This paper presents a novel optimization algorithm, derived using calculus of variation, for the motion planning of a hyper-redundant robot where the motion of one end (head) is an arbitrary desired path. The algorithm computes the motion of all the joints in the hyper-redundant robot in a way such that all its links avoid all obstacles present in the environment. The algorithm is purely geometric in nature, and it is shown that the motion in free space and in the vicinity of obstacles appears to be more natural. The paper presents the general theoretical development and numerical simulations results. It also presents validating results from experiments with a 12-degree-of-freedom (DOF) planar hyper-redundant robot moving in a known obstacle field.

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