Velocity Occupancy Space: Robot Navigation and Moving Obstacle Avoidance With Sensor Uncertainty

2009 ◽  
Author(s):  
Rachael Bis ◽  
Huei Peng ◽  
Galip Ulsoy
Keyword(s):  
Robot Navigation ◽  
Search Space ◽  
Sensor Data ◽  
Occupancy Grid ◽  
Moving Obstacles ◽  
Robotic Vehicle ◽  
Stationary Obstacles ◽  
Velocity Obstacle ◽  
Moving Obstacle ◽  
Limited Accuracy

In order to autonomously navigate in an unknown environment, a robotic vehicle must be able to sense obstacles, determine their velocities, and follow a clear path to a goal. However, the perceived location and motion of the obstacles will be uncertain due to the limited accuracy of the robot’s sensors. Thus, it is necessary to develop a system that can avoid moving obstacles using uncertain sensor data. The method proposed here is based on a certainty occupancy grid—which has been used to avoid stationary obstacles in an uncertain environment—in conjunction with the velocity obstacle concept—which allows a robot to avoid well-known moving obstacles. The combination of these two techniques leads to velocity occupancy space: a search space which allows the robot to avoid moving obstacles and navigate efficiently to a goal using uncertain sensor data.

Autonomous mobile robot navigation between static and dynamic obstacles using multiple ANFIS architecture

2019 ◽  
Vol 16 (2) ◽  
pp. 275-286 ◽  
Author(s):  
Anish Pandey ◽  
Abhishek Kumar Kashyap ◽  
Dayal R. Parhi ◽  
B.K. Patle
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Experimental Studies ◽  
Sensor Data ◽  
Mobile Robot Navigation ◽  
Control Technique ◽  
Inference System ◽  
Content Type ◽  
Moving Obstacles ◽  
Moving Obstacle

PurposeThis paper aims to design and implement the multiple adaptive neuro-fuzzy inference system (MANFIS) architecture-based sensor-actuator (motor) control technique for mobile robot navigation in different two-dimensional environments with the presence of static and moving obstacles.Design/methodology/approachThe three infrared range sensors have been mounted on the front, left and right side of the robot, which reads the forward, left forward and right forward static and dynamic obstacles in the environment. This sensor data information is fed as inputs into the MANFIS architecture to generate appropriate speed control commands for right and left motors of the robot. In this study, we have taken one assumption for moving obstacle avoidance in different scenarios the speed of the mobile robot is at least greater than or equal to the speed of moving obstacles and goal.FindingsGraphical simulations have designed through MATLAB and virtual robot experimentation platform (V-REP) software and experiments have been done on Arduino MEGA 2560 microcontroller-based mobile robot. Simulation and experimental studies demonstrate the effectiveness and efficiency of the proposed MANFIS architecture.Originality/valueThis paper designs and implements MANFIS architecture for mobile robot navigation between a static and moving obstacle in different simulation and experimental environments. Also, the authors have compared this developed architecture to the other navigational technique and found that our developed architecture provided better results in terms of path length in the same environment.

Intelligent Mobile Robot Navigation in an Uncertain Dynamic Environment

2013 ◽  
Vol 367 ◽  
pp. 388-392 ◽  
Author(s):  
Aydin Azizi ◽  
Farshid Entesari ◽  
Kambiz Ghaemi Osgouie ◽  
Mostafa Cheragh
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Dynamic Environment ◽  
Mobile Robot Navigation ◽  
Moving Target ◽  
Fuzzy Decision ◽  
Moving Obstacles ◽  
Navigation Algorithm ◽  
Modified Sensor ◽  
Velocity Obstacle

This paper presents a modified sensor-based online method for mobile robot navigation generating paths in dynamic environments. The core of the navigation algorithm is based on the velocity obstacle avoidance method and the guidance-based tracking algorithm. A fuzzy decision maker is designed to combine the two mentioned algorithms intelligently. Hence the robot will be able to decide intelligently in various situations when facing the moving obstacles and moving target. A noble noise cancellation algorithm using Neural Network is designed to navigate the robot in an uncertain dynamic environment safely. The results show that the robot can track a moving target while maneuvering safely in dynamic environment and avoids stationary and moving obstacles.

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

Robot navigation algorithms using learned spatial graphs

Robotica ◽  
1986 ◽  
Vol 4 (2) ◽  
pp. 93-100 ◽  
Author(s):  
S. S. Iyengar ◽  
C. C. Jorgensen ◽  
S. V. N. Rao ◽  
C. R. Weisbin
Keyword(s):  
Robot Navigation ◽  
Sensor Data ◽  
Two Dimensional ◽  
Continuous Transition ◽  
Spatial Graph ◽  
Spatial Graphs ◽  
Navigation Algorithms ◽  
Sensor Information ◽  
Navigation Planning ◽  
Available Information

SUMMARYFinding optimal paths for robot navigation in a known terrain has been studied for some time but, in many important situations, a robot would be required to navigate in completely new or partially explored terrain. We propose a method of robot navigation which requires no pre-learned model, makes maximal use of available information, records and synthesizes information from multiple journeys, and contains concepts of learning that allow for continuous transition from local to global path optimality. The model of the terrain consists of a spatial graph and a Voronoi diagram. Using acquired sensor data, polygonal boundaries containing perceived obstacles shrink to approximate the actual obstacles surfaces, free space for transit is correspondingly enlarged, and additional nodes and edges are recorded based on path intersections and stop points. Navigation planning is gradually accelerated with experience since improved global map information minimizes the need for further sensor data acquisition. Our method currently assumes obstacle locations are unchanging, navigation can be successfully conducted using two-dimensional projections, and sensor information is precise.

An EKF-based multiple data fusion for mobile robot indoor localization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guangbing Zhou ◽  
Jing Luo ◽  
Shugong Xu ◽  
Shunqing Zhang ◽  
Shige Meng ◽  
...  
Keyword(s):  
Data Fusion ◽  
Mobile Robot ◽  
Autonomous Navigation ◽  
Indoor Localization ◽  
Robot Navigation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Content Type ◽  
Multiple Data

Purpose Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments. Design/methodology/approach Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments. Findings The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation. Originality/value Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

Motion Planning of Mobile Robots for Occluded Obstacles

2018 ◽  
Vol 30 (3) ◽  
pp. 485-492
Author(s):  
Satoshi Hoshino ◽  
◽  
Tomoki Yoshikawa
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Dynamic Environments ◽  
Simulation Experiments ◽  
Moving Obstacles ◽  
Moving Direction ◽  
Velocity Obstacle

Motion planning of mobile robots for occluded obstacles is a challenge in dynamic environments. The occlusion problem states that if an obstacle suddenly appears from the occluded area, the robot might collide with the obstacle. To overcome this, we propose a novel motion planner, the Velocity Obstacle for occlusion (VOO). The VOO is based on a previous motion planner, the Velocity Obstacle (VO), which is effective for moving obstacles. In the proposed motion planner, information uncertainties about occluded obstacles, such as position, velocity, and moving direction, are quantitatively addressed. Thus, the robot based on the VOO is able to move not only among observed obstacles, but also among the occluded ones. Through simulation experiments, the effectiveness of the VOO for the occlusion problem is demonstrated by comparison with the VO.

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