Implementation of Modified Tangent Bug Navigation Algorithm for Front Wheel Steered and Differential-Drive Robots

2020 ◽  
Author(s):  
Sofia Yousuf ◽  
Muhammad Bilal Kadri
Keyword(s):  
Front Wheel ◽  
Differential Drive ◽  
Navigation Algorithm

Design and Development of an Omnidirectional Mobile Robot

2013 ◽  
Vol 432 ◽  
pp. 494-499
Author(s):  
Tey Wei Kang ◽  
Lim Thol Yong ◽  
Yeong Che Fai ◽  
Eileen Su Lee Ming
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Pid Controller ◽  
Design And Development ◽  
Differential Drive ◽  
Omnidirectional Mobile Robot ◽  
Navigation Algorithm ◽  
Direction Of Movement ◽  
Fine Tune

Most mobile robots use differential-drive concept, where they are equipped with two actuators that permit only single-direction rotation at a time. This concept limits the robots navigation because its orientation must always change according to the direction of movement. This paper presents the development of an omnidirectional mobile robot that uses three actuators, aligned in 120 degrees separation and each attached to an omniwheel. By manipulating actuator speed, the robot can navigate to any direction without changing its orientation. UsingNIsbRIO9632xtas the main controller, navigation algorithm is implemented in LabVIEW, integrated with PID controller to fine-tune robot movements.

scholarly journals Inertial navigation algorithm for trajectory of front-wheel walker estimation

Heliyon ◽  
2019 ◽  
Vol 5 (6) ◽  
pp. e01896 ◽  
Author(s):  
Quang Vinh Doan ◽  
Duy Duong Pham
Keyword(s):  
Inertial Navigation ◽  
Front Wheel ◽  
Navigation Algorithm

Embedded System for Front Differential Drive of Rotational and Translational Vehicle Position Control

2017 ◽  
Vol 10 (4) ◽  
pp. 325
Author(s):  
Angie Julieth Valencia Castañeda ◽  
Mauricio Felipe Mauledoux Monroy ◽  
Oscar Fernando Avilés Sánchez ◽  
Paola Andrea Niño Suarez ◽  
Edgar Alfredo Portilla Flores
Keyword(s):  
Embedded System ◽  
Position Control ◽  
Differential Drive ◽  
Vehicle Position

Regular perturbations to the motion of a three-wheeled mobile robot with the front-wheel drive under restricted state variables*

2020 ◽  
Author(s):  
Roman Chertovskih ◽  
Anna Daryina ◽  
Askhat Diveev ◽  
Dmitry Karamzin ◽  
Fernando L. Pereira ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Front Wheel ◽  
Wheeled Mobile Robot ◽  
State Variables ◽  
Wheel Drive

scholarly journals Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

2021 ◽  
Author(s):  
Hao Zhang ◽  
Zongxia Jiao ◽  
Yaoxing Shang ◽  
Xiaochao Liu ◽  
Pengyuan Qi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Front Wheel ◽  
Wheel Drive

A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments

Robotica ◽  
2021 ◽  
pp. 1-27
Author(s):  
Taha Elmokadem ◽  
Andrey V. Savkin
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicles ◽  
Practical Implementation ◽  
Challenging Problem ◽  
Quadrotor Uav ◽  
Navigation Algorithm ◽  
Aerial Vehicles ◽  
Quadrotor Uavs

Abstract Unmanned aerial vehicles (UAVs) have become essential tools for exploring, mapping and inspection of unknown three-dimensional (3D) tunnel-like environments which is a very challenging problem. A computationally light navigation algorithm is developed in this paper for quadrotor UAVs to autonomously guide the vehicle through such environments. It uses sensors observations to safely guide the UAV along the tunnel axis while avoiding collisions with its walls. The approach is evaluated using several computer simulations with realistic sensing models and practical implementation with a quadrotor UAV. The proposed method is also applicable to other UAV types and autonomous underwater vehicles.

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

2020 ◽  
pp. 1-17
Author(s):  
Haiying Liu ◽  
Jingqi Wang ◽  
Jianxin Feng ◽  
Xinyao Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Prior Information ◽  
Sliding Window ◽  
Factor Graph ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sliding Windows ◽  
Navigation Algorithm ◽  
Almost All

Abstract Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

Robotica ◽  
2021 ◽  
pp. 1-19
Author(s):  
H. S. Hewawasam ◽  
M. Yousef Ibrahim ◽  
Gayan Kahandawa ◽  
T. A. Choudhury
Keyword(s):  
Free Space ◽  
Motion Tracking ◽  
Dynamic Environment ◽  
Experimental Tests ◽  
Cluttered Environments ◽  
Navigation Algorithm ◽  
Prediction Module ◽  
Future Space ◽  
Free Spaces ◽  
Advanced Development

Abstract This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests.

scholarly journals TDMA Datalink Cooperative Navigation Algorithm Based on INS/JTIDS/BA

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 782
Author(s):  
Shuo Cao ◽  
Honglei Qin ◽  
Li Cong ◽  
Yingtao Huang
Keyword(s):  
Distribution System ◽  
Large Scale ◽  
Satellite System ◽  
Time Of Arrival ◽  
Military Operations ◽  
Position Information ◽  
Cooperative Navigation ◽  
Navigation Algorithm ◽  
Geometric Dilution Of Precision ◽  
Global Navigation Satellite

Position information is very important tactical information in large-scale joint military operations. Positioning with datalink time of arrival (TOA) measurements is a primary choice when a global navigation satellite system (GNSS) is not available, datalink members are randomly distributed, only estimates with measurements between navigation sources and positioning users may lead to a unsatisfactory accuracy, and positioning geometry of altitude is poor. A time division multiple address (TDMA) datalink cooperative navigation algorithm based on INS/JTIDS/BA is presented in this paper. The proposed algorithm is used to revise the errors of the inertial navigation system (INS), clock bias is calibrated via round-trip timing (RTT), and altitude is located with height filter. The TDMA datalink cooperative navigation algorithm estimate errors are stated with general navigation measurements, cooperative navigation measurements, and predicted states. Weighted horizontal geometric dilution of precision (WHDOP) of the proposed algorithm and the effect of the cooperative measurements on positioning accuracy is analyzed in theory. We simulate a joint tactical information distribution system (JTIDS) network with multiple members to evaluate the performance of the proposed algorithm. The simulation results show that compared to an extended Kalman filter (EKF) that processes TOA measurements sequentially and a TDMA datalink navigation algorithm without cooperative measurements, the TDMA datalink cooperative navigation algorithm performs better.

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