Obstacles Avoidance for an Articulated Robot Using Modified Smooth Path Planning

Nayl, Thaker; Mohammed, Mohammed Q.; Muhamed, Saif Q.

doi:10.1109/comapp.2017.8079732

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…Researchers have proposed various cost functions to smoothen paths and several postsmoothing algorithms for generated paths to address these issues. Thaker et al [37] tackled the problem of excessive turning points in A-star algorithm paths by increasing the offset distance of obstacles using a buffer area centered on the robot. Experimental results confirmed that this method resulted in smoother paths.…”

Section: Research Progress Of A-star Algorithmmentioning

confidence: 99%

An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

Rong

Shi

et al. 2023

JMSE

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The influence of the maritime environment such as water currents, water depth, and traffic separation rules should be considered when conducting ship path planning. Additionally, the maneuverability constraints of the ship play a crucial role in navigation. Addressing the limitations of the traditional A-star algorithm in ship path planning, this paper proposes an improved A-star algorithm. Specifically, this paper examines the factors influencing ship navigation safety, and develops a risk model that takes into account water currents, water depth, and obstacles. The goal is to mitigate the total risk of ship collisions and grounding. Secondly, a traffic model is designed to ensure that the planned path adheres to the traffic separation rules and reduces the risk of collision with incoming ships. Then, a turning model and smoothing method are designed to make the generated path easy to track and control for the ship. To validate the effectiveness of the proposed A-star ship path-planning algorithm, three cases are studied in simulations and representative operational scenarios. The results of the cases demonstrate that the proposed A-star ship path-planning algorithm can better control the distance to obstacles, effectively avoid shallow water areas, and comply with traffic separation rules. The safety level of the path is effectively improved.

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Section: Research Progress Of A-star Algorithmmentioning

confidence: 99%

An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

Rong

Shi

et al. 2023

JMSE

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“…The neural network algorithm has been used to ensure optimal time cost of path planning [5]. T. Nayl et al, presented a new approach to find smooth path planning with obstacles avoidance in fully known environment based on modified A-Star algorithm for articulated manipulator with obstacle avoidance based on data acquisition of local range sensors, which are mounted on the manipulator arms [6]. F. Li et al, presented an improved A-star algorithm based on collision-detection algorithm for optimal and collision-free path planning.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Graph Search Algorithms for Planar Manipulator to Avoid Obstacle Collision

2022

IJCCCE

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The search algorithms are characterized by their ability to find the optimal path in a short calculation time. In this study, a comparative analysis has been conducted to perform path planning of planar manipulator for static obstacle avoidance based on graph search algorithms. Four methods have been taken into account to establish a comparison platform; namely, conventional A*, modified A*, Chaos A*, and circulation heuristic search (CHS) algorithms. The performance of comparison is evaluated in terms of length of optimal path and consumption time of calculation. All algorithms have been coded and simulated within the MATLAB software environment. According to computer simulation, the results showed that CHS algorithms outperform the other graph search ones in terms of generated path length, while the Choas A* could give the least calculation time as compared to its counterparts. Index Terms— Path planning, A-Star, Modified A-Star, Chaos A-Star, CHS, Planar manipulator.

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“…Pada saat mendekati titik goal atau tepatnya berada di koordinat (20,35) jalan yang dapat dilalui ada dua, diperbolehkan ke kanan maupun ke kiri. Berdasarkan Gambar 10 node yang bergerak ke sebelah kanan cenderung lebih sedikit apabila dibandingkan dengan Weighted A* pada Gambar 11. selain itu, pada Weighted A* terdapat jalur yang sedikit tidak optimal, tepatnya pada koordinat (18,45). Node yang dikembangkan memilih bergerak ke arah diagonal atas daripada bergerak ke arah kanan.…”

Section: Lingkungan Narrowunclassified

Analisa Penggunaan Nilai Bobot Heuristik yang Berbeda pada Algoritma Weighted A*

Diana¹,

Herdiana²

2021

Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan

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Path planning merupakan urutan keadaan untuk memindahkan objek dari keadaan awal ke keadaan akhir, serta menghindari daerah yang tidak dapat dilalui. Objek disini dapat berupa robot, mobil otonom dan yang lainnya. Algoritma A* merupakan algoritma pencarian jalur yang menggunakan estimasi jarak dengan menggunakan pencarian jalur terdekat untuk mencapai tujuan. Weighted A* adalah algoritma yang digunakan untuk memecahkan masalah pencarian jalur dengan mengubah nilai bobot pada fungsi heuristiknya. Tujuan dari penelitian ini yaitu menganalisa perbandingan algoritma Weighted A* dengan algoritma A*, serta menganalisa pengaruh nilai bobot heuristik pada algoritma Weighted A*. Pengujian yang dilakukan yaitu menggunakan lingkungan maze, narrow, trap, clutter. Hasil yang didapat pada perbandingan algoritma Weighted A* dan A*, dari hasil pengujian diperoleh algoritma Weighted A* menghasilkan waktu pencarian yang lebih baik yaitu sebesar 0,33 detik, sedangkan algoritma A* menghasilkan waktu 1,40 detik. Tetapi algoritma A* dapat menghasilkan jalur yang lebih optimal yaitu 163,69 dibandingkan algoritma Weighted A* yang menghasilkan jalur sebesar 164,52. Dengan strategi yang lebih menekankan pemilihan node yang lebih dekat dengan node goal, maka Weighted A* dapat menghasilkan jalur dengan waktu komputasi yang lebih cepat. Sedangkan algoritma A* karena memilih node dengan nilai heuristik terkecil, maka dapat menghasilkan jalur yang lebih optimal. Weighted A* cocok di implementasikan pada sistem yang membutuhkan waktu pencarian jalur yang lebih singkat tapi tidak harus optimal. Algoritma A* cocok di implementasikan pada sistem yang membutuhkan jalur optimal walaupun waktu pencariannya tidak terlalu cepat

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Obstacles Avoidance for an Articulated Robot Using Modified Smooth Path Planning

Cited by 10 publications

References 5 publications

An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

An Improved A-Star Ship Path-Planning Algorithm Considering Current, Water Depth, and Traffic Separation Rules

A Comparative Study of Graph Search Algorithms for Planar Manipulator to Avoid Obstacle Collision

Analisa Penggunaan Nilai Bobot Heuristik yang Berbeda pada Algoritma Weighted A*

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