Real-Time Safe Navigation in Crowded Dynamic Environments Using Generalized Velocity Obstacles

2014 ◽  
Author(s):  
Mostafa Mahmoodi ◽  
Khalil Alipour ◽  
Mehdi Tale Masouleh ◽  
Hadi Beik Mohammadi
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Time Horizon ◽  
Dynamic Environments ◽  
Local Motion ◽  
Safe Navigation ◽  
Changing Environments ◽  
Wide Range ◽  
Planning Problems ◽  
Navigation Method

This paper aims at developing a real-time, robust, and reliable navigation method for an omnidirectional robot, the so-called MRL-SSL RoboCup robot, can be used in crowded dynamically-changing environments. To this end, a local motion planner will be introduced which combining the Generalized Velocity Obstacles (GVO) notion and a heuristic approach for determining the time horizon such that the inevitable collision states can successfully be avoided. The proposed method considers not only the kinematics of the robot but also its dynamics. Moreover, it could be extended to a wide range of practical path planning problems containing uncertainties. Finally, in order to demonstrate the performance and effectiveness of the proposed motion planner for the omnimobile robots, some practical scenarios are simulated.

scholarly journals Robot Path Planning using Dynamic Programming with Accelerating Nodes

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Dynamic Programming ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environments ◽  
Experimental Results ◽  
Robot Path Planning ◽  
Optimal Paths ◽  
Additional Processing ◽  
Update Frequency ◽  
Robot Path

AbstractWe solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments.

scholarly journals Real-time path planning in dynamic environments for unmanned aerial vehicles using the curve-shortening flow method

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142096868
Author(s):  
Marcel Huptych ◽  
Sascha Röck
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Time Integration ◽  
Dynamic Environments ◽  
Implicit Time ◽  
Industrial Control ◽  
Curve Shortening Flow ◽  
Flow Method ◽  
Time Path ◽  
Curve Shortening

This article proposes a new algorithm for real-time path planning in dynamic environments based on space-discretized curve-shortening flows. The so-called curve-shortening flow method shares working principles with the well-established elastic bands method and overcomes some of its drawbacks concerning numerical robustness and parameterability. This is achieved by efficiently applying semi-implicit time integration for evolving the path and secondly by developing a methodology for setting the algorithm’s parameters based on physical quantities. Different short- and long-term validation scenarios are performed with three interlinked instances of the curve-shortening flow method each running on an individual industrial control and driving a real or a simulated unmanned aerial vehicle.

scholarly journals Real-time path planning for virtual agents in dynamic environments

2008 ◽  
Author(s):  
Avneesh Sud ◽  
Erik Andersen ◽  
Sean Curtis ◽  
Ming Lin ◽  
Dinesh Manocha
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Dynamic Environments ◽  
Virtual Agents ◽  
Time Path

scholarly journals Deep reinforcement learning for map-less goal-driven robot navigation

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199262
Author(s):  
Matej Dobrevski ◽  
Danijel Skočaj
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Real World ◽  
Robot Navigation ◽  
Dynamic Environments ◽  
Learning Framework ◽  
Local Navigation ◽  
Real Robot ◽  
Navigation Method

Mobile robots that operate in real-world environments need to be able to safely navigate their surroundings. Obstacle avoidance and path planning are crucial capabilities for achieving autonomy of such systems. However, for new or dynamic environments, navigation methods that rely on an explicit map of the environment can be impractical or even impossible to use. We present a new local navigation method for steering the robot to global goals without relying on an explicit map of the environment. The proposed navigation model is trained in a deep reinforcement learning framework based on Advantage Actor–Critic method and is able to directly translate robot observations to movement commands. We evaluate and compare the proposed navigation method with standard map-based approaches on several navigation scenarios in simulation and demonstrate that our method is able to navigate the robot also without the map or when the map gets corrupted, while the standard approaches fail. We also show that our method can be directly transferred to a real robot.

scholarly journals Multi-Domain Informative Coverage Path Planning for A Hybrid Aerial Underwater Vehicle in Dynamic Environments

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 278
Author(s):  
Xueyao Liang ◽  
Chunhu Liu ◽  
Zheng Zeng
Keyword(s):  
Path Planning ◽  
Dynamic Environments ◽  
Underwater Vehicles ◽  
Neighborhood Search ◽  
The Novel ◽  
Coverage Path Planning ◽  
Planning Framework ◽  
Wide Range ◽  
New Frontier ◽  
Tree Methods

Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle autonomy. However, covering an entire mission region is a challenge to HAUVs because of the possibility of a multidomain environment. This paper presents an informative trajectory planning framework for planning paths and generating trajectories for HAUVs performing multidomain missions in dynamic environments. We introduce the novel heuristic generalized extensive neighborhood search GLNS–k-means algorithm that uses k-means to cluster information into several sets; then through the heuristic GLNS algorithm, it searches the best path for visiting these points, subject to various constraints regarding path budgets and the motion capabilities of the HAUV. With this approach, the HAUV is capable of sampling and focusing on regions of interest. Our method provides a significantly more optimal trajectory (enabling collection of more information) than ant colony optimization (ACO) solutions. Moreover, we introduce an efficient online replanning scheme to adapt the trajectory according to the dynamic obstacles during the mission. The proposed replanning scheme based on KD tree enables significantly shorter computational times than the scapegoat tree methods.

scholarly journals Real-time Path Planning for Virtual Agents in Dynamic Environments

2007 ◽  
Author(s):  
Avneesh Sud ◽  
Erik Andersen ◽  
Sean Curtis ◽  
Ming Lin ◽  
Dinesh Manocha
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Dynamic Environments ◽  
Virtual Agents ◽  
Time Path

On-Line Centralized Path Planning of Multiple Mobile Robots in Dynamic Environments

2012 ◽  
Vol 155-156 ◽  
pp. 1074-1079
Author(s):  
Zi Hui Zhang ◽  
Yue Shan Xiong
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Dynamic Environments ◽  
Characteristic Line ◽  
Multiple Mobile Robots ◽  
Time Performance ◽  
Planning Algorithm ◽  
On Line ◽  
Path Planning Algorithm

To study the path planning problem of multiple mobile robots in dynamic environments, an on-line centralized path planning algorithm is proposed. It is difficult to obtain real-time performance for path planning of multiple robots in dynamic environment. The harmonic potential field for multiple mobile robots is built by using the panel method known in fluid mechanics, which represents the outward normal velocity of each line of a polygonal obstacle as a function of the length of its characteristic line. The simulation results indicate that it is a simple, efficient and effective path planning algorithm for multiple mobile robots in the dynamic environments that the geometries and trajectories of obstacles are known in advance, and can achieve real-time performance.

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