Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation

2013 ◽  
Vol 3 (4) ◽  
pp. 1-29
Author(s):  
Priyanka Meel ◽  
Ritu Tiwari ◽  
Anupam Shukla
Keyword(s):  
Path Planning ◽  
Wave Front ◽  
Dynamic Environment ◽  
New Method ◽  
Safety Zone ◽  
Color Sensitivity ◽  
Focused Wave ◽  
Local Path ◽  
Global And Local ◽  
Multi Robot

Robotics is a field which includes multiple disciplines such as environment mapping, localization, path planning, path execution, area exploration etc. Path planning is the elementary requirement for all the above mentioned diversified fields. This paper presents a new method for motion planning of mobile robots which carry forward the best features of Focused Wave Front and other wave front based path planners, at the same time optimizes the algorithm in terms of path length, energy consumption and memory requirements. This research introduces a method of choosing every next step in grid based environment and also proposes a backtracking procedure to minimize turns by means of identifying landmark points in the path. Further, the authors have enhanced the functionality of Focused Wave Front algorithm by applying it in uncertain dynamic environment. The proposed method is a combination of global and local path planning as well as online and offline navigation process. A new method based on bidirectional wave propagation along the walls of obstacle and wall following behavior is being proposed for avoiding uncertain static obstacles. Considering the criticalness of moving obstacles a colored safety zone is assumed to have around them and the robot is equipped with color sensitivity. Based on the particular color (red, green, yellow) that has sensed the robot will make intelligent decisions to avoid them. The simulation result reflects how the proposed method has efficiently and safely navigates a robot towards its destination by avoiding all known and unknown obstacles. Finally the algorithms are extended for multi-robot environment.

Optimization of Focused Wave Front Algorithm in Unknown Dynamic Environment for Multi-Robot Navigation

2019 ◽  
pp. 553-581
Author(s):  
Priyanka Meel ◽  
Ritu Tiwari ◽  
Anupam Shukla
Keyword(s):  
Path Planning ◽  
Wave Front ◽  
Path Length ◽  
Dynamic Environment ◽  
New Method ◽  
Focused Wave ◽  
Local Path ◽  
Global And Local ◽  
Grid Based ◽  
Multi Robot

Robotics is a field which includes multiple disciplines such as environment mapping, localization, path planning, path execution, area exploration etc. Path planning is the elementary requirement for all the above mentioned diversified fields. This paper presents a new method for motion planning of mobile robots which carry forward the best features of Focused Wave Front and other wave front based path planners, at the same time optimizes the algorithm in terms of path length, energy consumption and memory requirements. This research introduces a method of choosing every next step in grid based environment and also proposes a backtracking procedure to minimize turns by means of identifying landmark points in the path. Further, the authors have enhanced the functionality of Focused Wave Front algorithm by applying it in uncertain dynamic environment. The proposed method is a combination of global and local path planning as well as online and offline navigation process. A new method based on bidirectional wave propagation along the walls of obstacle and wall following behavior is being proposed for avoiding uncertain static obstacles. Considering the criticalness of moving obstacles a colored safety zone is assumed to have around them and the robot is equipped with color sensitivity. Based on the particular color (red, green, yellow) that has sensed the robot will make intelligent decisions to avoid them. The simulation result reflects how the proposed method has efficiently and safely navigates a robot towards its destination by avoiding all known and unknown obstacles. Finally the algorithms are extended for multi-robot environment.

A joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring

2021 ◽  
Vol 193 ◽  
pp. 107913
Author(s):  
Yuan Tang ◽  
Yiming Miao ◽  
Ahmed Barnawi ◽  
Bander Alzahrani ◽  
Reem Alotaibi ◽  
...  
Keyword(s):  
Path Planning ◽  
Task Scheduling ◽  
Air Monitoring ◽  
Local Path Planning ◽  
Planning Optimization ◽  
Local Path ◽  
Global And Local

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

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.

A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

2021 ◽  
Author(s):  
N.P. Demenkov ◽  
Kai Zou
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Urban Environment ◽  
Predictive Control ◽  
Potential Field ◽  
Dynamic Environment ◽  
Field Method ◽  
Artificial Potential Field ◽  
Physical Constraints ◽  
Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

Artificial moment method using attractive points for the local path planning of a single robot in complicated dynamic environments

Robotica ◽  
2013 ◽  
Vol 31 (8) ◽  
pp. 1263-1274 ◽  
Author(s):  
Wang-bao Xu ◽  
Jie Zhao ◽  
Xue-bo Chen ◽  
Ying Zhang
Keyword(s):  
Path Planning ◽  
Dynamic Environment ◽  
Motion Controller ◽  
Motion Direction ◽  
Local Path Planning ◽  
Full Speed ◽  
Direction Line ◽  
Local Path ◽  
Path Planner ◽  
Sampling Times

SUMMARYA novel path planner is presented for the local path planning of a single robot (represented with R) in a complicated dynamic environment. Here a series of attractive points are computed based on attractive segments for guiding R to move along a shorter path. Each attractive segment is obtained by using the full environmental knowledge and will be used for several sampling times in general. A motion controller, which is designed based on artificial moments and a robot model that has a principal motion direction line(PMDline), makes R move closely to attractive points while away from obstacles. Attractive and repulsive moments are designed, which only make R's PMDline face toward attractive points and opposite to obstacles in general, as in most cases, R will move along its PMDline with its full speed. Because of the guidance of attractive points and R's full-speed motion, the global convergence is guaranteed. Simulations indicate that the proposed path planner meets the requirements of real-time property while can optimize R's traveling path.

scholarly journals Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

2021 ◽  
Vol 50 (2) ◽  
pp. 357-374
Author(s):  
Novak Zagradjanin ◽  
Aleksandar Rodic ◽  
Dragan Pamucar ◽  
Bojan Pavkovic
Keyword(s):  
Path Planning ◽  
High Efficiency ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Dynamic Environment ◽  
Cloud Services ◽  
Robot System ◽  
Inference System ◽  
Crowded Environment ◽  
Multi Robot

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

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