Multi-robot path planning based on improved artificial potential field and fuzzy inference system1

2020 ◽  
Vol 39 (5) ◽  
pp. 7621-7637 ◽  
Author(s):  
Tao Zhao ◽  
Haodong Li ◽  
Songyi Dian
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Fuzzy Inference ◽  
Target Position ◽  
Assessment Method ◽  
Artificial Potential Field ◽  
Collision Risk ◽  
Inference System ◽  
Field Function ◽  
Multi Robot

In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems.

scholarly journals UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1162 ◽  
Author(s):  
Yang Huang ◽  
Jun Tang ◽  
Songyang Lao
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Group Formation ◽  
Target Position ◽  
Field Method ◽  
Second Order ◽  
Consensus Algorithm ◽  
Artificial Potential Field ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The problem of collision avoidance of an unmanned aerial vehicle (UAV) group is studied in this paper. A collision avoidance method of UAV group formation based on second-order consensus algorithm and improved artificial potential field is proposed. Based on the method, the UAV group can form a predetermined formation from any initial state and fly to the target position in normal flight, and can avoid collision according to the improved smooth artificial potential field method when encountering an obstacle. The UAV group adopts the “leader–follower” strategy, that is, the leader UAV is the controller and flies independently according to the mission requirements, while the follower UAV follows the leader UAV based on the second-order consensus algorithm and formations gradually form during the flight. Based on the second-order consensus algorithm, the UAV group can achieve formation maintenance easily and the Laplacian matrix used in the algorithm is symmetric for an undirected graph. In the process of obstacle avoidance, the improved artificial potential field method can solve the jitter problem that the traditional artificial potential field method causes for the UAV and avoids violent jitter. Finally, simulation experiments of two scenarios were designed to verify the collision avoidance effect and formation retention effect of static obstacles and dynamic obstacles while the two UAV groups fly in opposite symmetry in the dynamic obstacle scenario. The experimental results demonstrate the effectiveness of the proposed method.

scholarly journals Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships

2020 ◽  
Vol 10 (11) ◽  
pp. 3919 ◽  
Author(s):  
Sung Wook Ohn ◽  
Ho Namgung
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Membership Function ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Collision Risk ◽  
Inference System ◽  
Interval Type

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.

A Searching and Tracking Framework for Multi-Robot Observation of Multiple Moving Targets

2004 ◽  
Vol 8 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Zheng Liu ◽  
◽  
Marcelo H. Ang Jr. ◽  
Winston Khoon Guan Seah ◽  
◽  
...  
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Motion Control ◽  
Potential Field ◽  
Research Topic ◽  
Artificial Potential Field ◽  
Moving Targets ◽  
Real Time Tracking ◽  
Multiple Moving Targets ◽  
Multi Robot

The "museum problem" is a typical research topic on multi-robot observation of multiple moving targets. The objective of museum problem is to optimize the distribution of robots, such that the maximal moving targets can be observed. In this paper, we present our memory based searching and artificial potential field based tracking framework for museum problem. For searching, a memory table, either local or shared, can help shorten the searching time for targets. For tracking, our artificial potential field based motion control provides real-time tracking of moving targets with collision avoidance. Qualitative simulations demonstrate the capability of our searching and tracking framework.

scholarly journals Research on Early Warning of Ship Danger Based on Composition Fuzzy Inference

2020 ◽  
Vol 8 (12) ◽  
pp. 1002
Author(s):  
Zhiying Guan ◽  
Yan Wang ◽  
Zheng Zhou ◽  
Hongbo Wang
Keyword(s):  
Collision Avoidance ◽  
Early Warning ◽  
Fuzzy Inference ◽  
Domain Size ◽  
Domain Model ◽  
Collision Risk ◽  
International Regulation ◽  
Inference System ◽  
Ship Collision ◽  
Marine Accidents

Ship collision avoidance measures are important for reducing marine accidents caused by human factors and various natural environmental factors and can also prevent property loss and casualties. In recent years, various methods have been used to study collision avoidance, including ship domain models. This paper proposes a ship domain model based on fuzzy logic aimed at providing early warning of ship collision risk and a reasonable reference that can be used in combination with the International Regulation for Preventing Collisions at Sea (COLREGs). The composition fuzzy inference combining more than one fuzzy inference process is first used to introduce as many factors as possible related to ship collision risk for calculating the ship domain. In this way, the calculation of the ship domain size is more accurate, and a more accurate reference can be provided to sailors, which could save both time and labor by reducing errors. A fuzzy inference system based on if-then fuzzy rules was established in MATLAB and simulation experiments were conducted. The simulation results suggest that the proposed method is feasible and can help sailors make subjective decisions to effectively avoid the occurrence of collision accidents.

scholarly journals Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1499
Author(s):  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Fuzzy Inference ◽  
Target Position ◽  
Inequality Constraints ◽  
Robot System ◽  
Inference System ◽  
Genetic Fuzzy System ◽  
Object Transportation ◽  
The Stability ◽  
Inference Systems ◽  
Multi Robot

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.

scholarly journals Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021 ◽  
Vol 11 (7) ◽  
pp. 3103
Author(s):  
Kyuman Lee ◽  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Unmanned Aircraft ◽  
Artificial Potential Field ◽  
Tree Search ◽  
Local Minima ◽  
Collision Risk ◽  
Motion Primitives ◽  
Multiple Uavs ◽  
Tree Search Algorithm

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

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