Multiple Moving Obstacles Avoidance for USV using Velocity Obstacle Method

Motion Planning of Mobile Robots for Occluded Obstacles

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2018.p0485 ◽

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

ASME 2009 Dynamic Systems and Control Conference, Volume 1 ◽

10.1115/dscc2009-2570 ◽

2009 ◽

Cited By ~ 6

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.

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Intelligent Mobile Robot Navigation in an Uncertain Dynamic Environment

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.367.388 ◽

2013 ◽

Vol 367 ◽

pp. 388-392 ◽

Cited By ~ 5

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.

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Uncertain estimation-based motion-planning algorithms for mobile robots

ACTA IMEKO ◽

10.21014/acta_imeko.v10i3.1035 ◽

2021 ◽

Vol 10 (3) ◽

pp. 51

Author(s):

Zoltán Bálint Gyenes ◽

Emese Gincsainé Szádeczky-Kardoss

Keyword(s):

Motion Planning ◽

Mobile Agents ◽

Dynamic Environment ◽

Target Position ◽

Free Motion ◽

Artificial Potential Field ◽

Moving Obstacles ◽

Other Information ◽

Planning Algorithms ◽

Velocity Obstacle

Collision-free motion planning for mobile agents is a challenging task, especially when the robot has to move towards a target position in a dynamic environment. The main aim of this paper is to introduce motion-planning algorithms using the changing uncertainties of the sensor-based data of obstacles. Two main algorithms are presented in this work. The first is based on the well-known velocity obstacle motion-planning method. In this method, collision-free motion must be achieved by the algorithm using a cost-function-based optimisation method. The second algorithm is an extension of the often-used artificial potential field. For this study, it is assumed that some of the obstacle data (e.g. the positions of static obstacles) are already known at the beginning of the algorithm (e.g. from a map of the enviroment), but other information (e.g. the velocity vectors of moving obstacles) must be measured using sensors. The algorithms are tested in simulations and compared in different situations.

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Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

Journal of Marine Science and Engineering ◽

10.3390/jmse9070761 ◽

2021 ◽

Vol 9 (7) ◽

pp. 761

Author(s):

Liang Zhang ◽

Junmin Mou ◽

Pengfei Chen ◽

Mengxia Li

Keyword(s):

Path Planning ◽

Local Minimum ◽

Hybrid Approach ◽

Artificial Potential Field ◽

Whole Process ◽

International Regulations ◽

Local Path ◽

Velocity Obstacle ◽

Global And Local

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

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