scholarly journals Dynamic Obstacle Avoidance for an Omnidirectional Mobile Robot

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Robert L. Williams ◽  
Jianhua Wu
Keyword(s):  
Obstacle Avoidance ◽  
Experimental Validation ◽  
Vision System ◽  
Dynamic Environment ◽  
Ohio University ◽  
Omnidirectional Mobile Robot ◽  
Dynamic Obstacle Avoidance ◽  
Novel Method ◽  
Dynamic Obstacle ◽  
Moving Obstacle

We have established a novel method of obstacle-avoidance motion planning for mobile robots in dynamic environments, wherein the obstacles are moving with general velocities and accelerations, and their motion profiles are not preknown. A hybrid system is presented in which a global deliberate approach is applied to determine the motion in the desired path line (DPL), and a local reactive approach is used for moving obstacle avoidance. A machine vision system is required to sense obstacle motion. Through theoretical analysis, simulation, and experimental validation applied to the Ohio University RoboCup robot, we show the method is effective to avoid collisions with moving obstacles in a dynamic environment.

Dynamic Obstacle Avoidance Algorithm for the Mobile Robot Based on Depth Image of Kinect Sensor

2014 ◽  
Vol 541-542 ◽  
pp. 1072-1078
Author(s):  
Yi Zhang ◽  
Xue Rong Tong ◽  
Yuan Luo
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Path Selection ◽  
Depth Image ◽  
Depth Information ◽  
Kinect Sensor ◽  
Dynamic Obstacle Avoidance ◽  
Kalman Filter Algorithm ◽  
Dynamic Obstacle

In order to solve the problem of the dynamic obstacle avoidance of the mobile robot in indoor environment, a new approach based on depth information is presented in this paper. The depth information of surrounding environment was collected and used to set the robots obstacle avoidance warning area by a Kinect sensor. When the moving obstacle accessed into the warning area, the robots obstacle avoidance direction was determined preliminary by the obstacles position, and then an improved Kalman filter algorithm was used to optimize the avoidance path. Experiments show that this approach can overcome the potential problem of path selection, and realize the mobile robot obstacle avoidance behavior in the dynamic environment.

Robot navigation based on improved A* algorithm in dynamic environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lin Zhang ◽  
Yingjie Zhang ◽  
Manni Zeng ◽  
Yangfan Li
Keyword(s):  
Obstacle Avoidance ◽  
Optimal Path ◽  
Dynamic Environment ◽  
Evaluation Function ◽  
A Algorithm ◽  
Content Type ◽  
B Spline ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Adaptive Step

Purpose The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time. Design/methodology/approach To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms. Findings The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment. Originality/value This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.

scholarly journals An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

Machines ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 50
Author(s):  
Liwei Yang ◽  
Lixia Fu ◽  
Ping Li ◽  
Jianlin Mao ◽  
Ning Guo
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Ant Colony ◽  
Complex Dynamic ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Window Approach ◽  
Search Capability

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot's navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.

Open Experimental AGV Platform for Dynamic Obstacle Avoidance in Narrow Corridors

2020 ◽  
Author(s):  
Sam Weckx ◽  
Bastiaan Vandewal ◽  
Erwin Rademakers ◽  
Karel Janssen ◽  
Kurt Geebelen ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

Recent Advances in Robot Trajectory Planning in a Dynamic Environment

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Rongzijun Shu ◽  
Guangsen Li
Keyword(s):  
Trajectory Planning ◽  
Dynamic Environment ◽  
Local Planning ◽  
Robot Trajectory ◽  
Recent Advances ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Task Collaboration ◽  
Global Planning ◽  
Robot Trajectory Planning

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.

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