scholarly journals IMPROVEMENT OF CONTROL ALGORITHM FOR MOBILE ROBOT USING MULTI-LAYER SENSOR FUSION

2021 ◽  
Vol 59 (1) ◽  
pp. 110
Author(s):  
Ha Xuan Nguyen ◽  
Huy Van Nguyen ◽  
Tung Thanh Ngo ◽  
Anh Duy Nguyen
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Obstacle Avoidance ◽  
Sensor Fusion ◽  
Control Algorithm ◽  
Experimental Implementation ◽  
Different Shapes ◽  
High Repeatability ◽  
Very High

Mobile robots have received much of attention in the last three decades due to their very high potential of applications such as smart logistics, exploration, and intelligent services. One of important functions of mobile robots is the navigation in which robot must know their location, the maps of environment and perform path planning with obstacle avoidance. In this work, we introduce an improvement of control algorithm for mobile robot using multi-layer sensor fusion toward the target of efficient obstacle avoidance. Based on our method, we used three layers of sensors arranging in three height-different planes of robot’s housing for sensor fusion. A control algorithm, which is extended from the so-called bubble rebound algorithm and uses signal from sensor system, was proposed. Experimental implementation on a mobile robot, named EAI, shows that our algorithm can control the robot to navigate and avoid obstacles much efficiently, in which obstacles in forms of different shapes and height can also be avoided. A high repeatability and stability of the algorithm is obtained.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

Space robot motion path planning based on fuzzy control algorithm

2021 ◽  
pp. 1-8
Author(s):  
Baoyu Shi ◽  
Hongtao Wu
Keyword(s):  
Path Planning ◽  
Fuzzy Control ◽  
Obstacle Avoidance ◽  
Control Algorithm ◽  
Space Robot ◽  
Robot Motion ◽  
Robot Path Planning ◽  
Behavior Based ◽  
Fuzzy Control Algorithm ◽  
Robot Path

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.

Navigation of a wheeled mobile robotic agent using modified grey wolf optimization controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chittaranjan Paital ◽  
Saroj Kumar ◽  
Manoj Kumar Muni ◽  
Dayal R. Parhi ◽  
Prasant Ranjan Dhal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Real Time ◽  
Autonomous Navigation ◽  
Simulation Software ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Content Type ◽  
Cluttered Environment

PurposeSmooth and autonomous navigation of mobile robot in a cluttered environment is the main purpose of proposed technique. That includes localization and path planning of mobile robot. These are important aspects of the mobile robot during autonomous navigation in any workspace. Navigation of mobile robots includes reaching the target from the start point by avoiding obstacles in a static or dynamic environment. Several techniques have already been proposed by the researchers concerning navigational problems of the mobile robot still no one confirms the navigating path is optimal.Design/methodology/approachTherefore, the modified grey wolf optimization (GWO) controller is designed for autonomous navigation, which is one of the intelligent techniques for autonomous navigation of wheeled mobile robot (WMR). GWO is a nature-inspired algorithm, which mainly mimics the social hierarchy and hunting behavior of wolf in nature. It is modified to define the optimal positions and better control over the robot. The motion from the source to target in the highly cluttered environment by negotiating obstacles. The controller is authenticated by the approach of V-REP simulation software platform coupled with real-time experiment in the laboratory by using Khepera-III robot.FindingsDuring experiments, it is observed that the proposed technique is much efficient in motion control and path planning as the robot reaches its target position without any collision during its movement. Further the simulation through V-REP and real-time experimental results are recorded and compared against each corresponding results, and it can be seen that the results have good agreement as the deviation in the results is approximately 5% which is an acceptable range of deviation in motion planning. Both the results such as path length and time taken to reach the target is recorded and shown in respective tables.Originality/valueAfter literature survey, it may be said that most of the approach is implemented on either mathematical convergence or in mobile robot, but real-time experimental authentication is not obtained. With a lack of clear evidence regarding use of MGWO (modified grey wolf optimization) controller for navigation of mobile robots in both the environment, such as in simulation platform and real-time experimental platforms, this work would serve as a guiding link for use of similar approaches in other forms of robots.

Research on path planning technology of mobile robot based on visual obstacle avoidance

2021 ◽  
Author(s):  
Zai Luo ◽  
Yiwen Chen ◽  
Wensong Jiang ◽  
Xiaofeng Hu ◽  
Li Yang ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance

scholarly journals 2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuexi Zhang ◽  
Jiajun Lai ◽  
Dongliang Xu ◽  
Huaijun Li ◽  
Minyue Fu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Basic System ◽  
Rescue Robot ◽  
Planning Algorithm ◽  
Local Path ◽  
Slam Algorithm ◽  
Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

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