Path Planning for Mobile Robot Navigation in Unknown Indoor Environments Using Hybrid PSOFS Algorithm

Wahab, Mohd Nadhir Ab; Lee, Ching May; Akbar, M.S.; Hassan, Fadratul Hafinaz

doi:10.1109/access.2020.3021605

Cited by 30 publications

(13 citation statements)

References 22 publications

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“…The presented method allows planning the trajectory of the MR in an unknown closed environment with obstacles, taking into account the limited performance of the on-board sensors and computing devices of the MR. Studies that offer a solution to a similar problem 9 include works [9,14]. In [9], an approach based on artificial neural networks is used to solve the trajectory planning problem.…”

Section: Discussionmentioning

confidence: 99%

“…The method described in article [14] is considered as an analogue in this work. This method is a combination of the particle swarm optimization (PSO) algorithm [15] and the Fringe Search (FS) algorithm [16].…”

Section: Introductionmentioning

confidence: 99%

“…Mathematically, this criterion consists in determining the angle between two lines using the formula shown in equation (13). In addition, before finding the angle between any two lines, the angles of rotation are calculated using the formula shown in equation (14). Finally, the average value of the angle of rotation for estimating the smoothness of the trajectory can be found from formula (15).…”

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confidence: 99%

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A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

2021

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The known algorithms for planning the trajectory of movement of mobile robots in an unknown environment have high computational complexity or do not allow finding the trajectory that is optimal along the length of the path, while maintaining a safe distance from obstacles. The aim of the work is to increase the efficiency of solving the problem of planning the trajectory of movement of mobile robots from the initial position to the final position in an unknown environment with obstacles, taking into account the limited capabilities (sensory and computational) of mobile robots. The solution to this problem was carried out on the basis of step-by-step optimization of the current position of the robot relative to a given target. The proposed method analyzes the possibility of a robot moving in directions determined by means of analytical geometry based on measurements of on-board distance sensors. An element of scientific novelty is the procedure for calculating trajectory segments based on the choice of an intermediate state and correcting the trajectory taking into account the measurements of the on-board distance sensors of the mobile robot. The proposed method makes it possible to search for the trajectory of a mobile robot in an unknown environment while ensuring a given distance to obstacles. The use of the presented algorithm allows the robot to maintain a high efficiency of the task while functioning in conditions of information deficiency. The reliability of the results was confirmed in the course of software simulation. The solution of the problem, taking into account these features, made it possible to reduce the computational complexity of the method, as well as to remove restrictions on the use of trajectory planning algorithms for mobile robots with low-performance on-board sensors and computing devices. The presented algorithm is implemented in the form of software in the Python programming language, which can be used to simulate autonomous control systems for mobile robots.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

2021

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“…Literature [9,10] puts forward the square-root unscented KF (Kalman filter) based on UKF, which expands the restricted range of UKF and can be applied to Gaussian regression process. Literature [11] puts forward a new idea; that is, traditional UKF is combined with a new hotspot algorithm ParticleFilter, thus forming a high-precision unscented particle filter algorithm. Reference [12] puts forward that UKF is used to deal with the location problem of nonlinear transformation.…”

Section: Related Workmentioning

confidence: 99%

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Yating

2022

Wireless Communications and Mobile Computing

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In the industrial field, industrial robots have taken over the heavy lifting that used to be done by traditional handicraft assembly lines, greatly freeing up human resources and improving production efficiency and safety. As a result, the focus of this paper is on the SLAM-based robot localization and navigation algorithm (simultaneous localization and mapping). An attitude estimation algorithm based on KF (Kalman filtering) information fusion of vision SLAM and IMU (Inertial Measurement Unit) is proposed, and the ORB-SLAM algorithm is studied and perfected. The fusion of the two postures improves the accuracy and frequency of the robot’s attitude estimation during motion. In addition, PSO (Particle Swarm Optimization) technology is used to optimize the resampling process, and PSO optimizes the particle set to alleviate the problem of particle degradation and exhaustion caused by resampling in the FastSLAM algorithm. Finally, the algorithm is verified to meet the requirements of positioning and composition accuracy, as well as the feasibility and effectiveness of robot autonomous navigation, using the open simulation platform.

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“…Path planning is one of the most researched problems in robotics [1][2][3]. Any path planning algorithm's primary goal is to provide a collision-free path from a start state to an end state within the robot's configuration space [4,5].…”

Section: Introductionmentioning

confidence: 99%

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

Pohan

Utama

2023

IJE

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This study presents an efficient path planning method for mobile robots in a dynamic environment. The method is based on the rapidly-exploring random tree (RRT) algorithm. The two primary processes in mobile robot path planning in a dynamic environment are initial path planning and path re-planning. In order to generate a feasible initial path with fast convergence speed, we used a hybridization of rapidlyexploring random tree star and ant colony systems (RRT-ACS). When an obstacle obstructs the initial path, the path re-planner must be executed. In addition to the RRT-ACS algorithm, we proposed using a rule-template set based on the mobile robot in dynamic environment scenes during the path re-planner process. This novel algorithm is called RRT-ACS with Rule-Template Sets (RRT-ACS+RT). We conducted many benchmark simulations to validate the proposed method in a real dynamic environment. The performance of the proposed method is compared to the state-of-the-art path planning algorithms: RRT*FND and MOD-RRT*. Numerous experimental results demonstrate that the proposed method outperforms other comparison algorithms. The results show that the proposed method is suitable for the use on robots that need to navigate in a dynamic environment, such as self-driving cars.

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Path Planning for Mobile Robot Navigation in Unknown Indoor Environments Using Hybrid PSOFS Algorithm

Cited by 30 publications

References 22 publications

A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

A method for planning the trajectory of a mobile robot in an unknown environment with obstacles

Research on Robot Positioning and Navigation Algorithm Based on SLAM

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

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