Path planning algorithm to minimize an overlapped path and turning number for an underwater mining robot

Pratama, Pandu Sandi; Kim, Jin-Wook; Kim, Hak-Kyeong; Yoon, Suk Min; Yeu, Tae–Kyeong; Hong, Sup; Oh, Sea-June; Kim, Sang-Bong

doi:10.1109/iccas.2015.7364969

Cited by 10 publications

(7 citation statements)

References 10 publications

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“…work e f f iciency = overall travel time f or the normal scenario overall travel time f or the breakdown scenario (11) Another method of quantifying real-time scheduling can be defined. This is called recovery efficiency, and it can be formulated in Equation (12). The recovery efficiency quantifies how optimal the breakdown paths are in comparison to the paths planned for the normal scenario.…”

Section: Computation Time Area Coverage and Work Efficiencymentioning

confidence: 99%

“…Having such a high recovery efficiency means that the proposed methods can create paths that are comparable to the ones originally planned. recovery e f f iciency = total travel time f or the normal scenario total travel time f or the breakdown scenario (12) Since the lower-level path generation used to pre-process the service paths for each sub-area, the area coverage remains the same for any scenario and is independent of the performance of the higher-level path generation. The area coverage for each sub-area can be seen in Table 6, where all sub-area coverages are over 94%, and the average area coverage is 99.23%.…”

Section: Computation Time Area Coverage and Work Efficiencymentioning

confidence: 99%

“…Then, a local path planner can be used to find the coverage path in each cell (typically an exhaustive sweep of the cell), and a global path planner can be used to find the optimal traversal sequence of the cells. Several methods have been explored in the literature to perform such decomposition, such as boustrophedon cell decomposition [11], morse cell decomposition [12], and trapezoidal decomposition [13]. Each method has its own application, which is dependent on the problem requirements and environmental complexity.…”

Section: Introductionmentioning

confidence: 99%

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Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Parsons,

Baghyari,

Seo

et al. 2024

Robotics

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In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.

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Section: Computation Time Area Coverage and Work Efficiencymentioning

confidence: 99%

Section: Computation Time Area Coverage and Work Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Parsons,

Baghyari,

Seo

et al. 2024

Robotics

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show abstract

“…The DFS fails in infinite depth spaces and does not guarantee to find an optimal solution (shortest coverage path), whereas the BFS consumes large memory space due to the high branching factor in the search space. The DFS optimizes the sequence path with the benefit of minimum overlapped and several turns for CPP [156,157,158]. Kabir et al [159] utilized the DFS technique to create a cleaning trajectory by generating a sequence of setups.…”

Section: ) Depth-first Search and Breadth-first Search Algorithmsmentioning

confidence: 99%

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Mohd‐Mokhtar²,

2021

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The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights. INDEX TERMSCoverage path planning, exploration, heuristic algorithm, deep reinforcement learning.

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“…Dr. Jing-ni Yuan from Shanghai Jiao Tong University proposed an improved vehicle motion planning method combining RRT* and Bessel curve control point optimization, but it is not suitable for deep-sea environment [3] . The Korean scholar Sup-hong et al deduced that the angular velocity during turning should be limited to 0.043rad/s [4,5,6,7] .Changsha Institute of Mining Research co. LTD., after the mining truck maritime test, concluded that the speed of the seabed mining truck should be controlled at 0.5m/s-1m/s. Considering that there is no sharp turn in obstacle avoidance, the actual turning distance is slightly larger than that of the idealized model.…”

Section: Introductionmentioning

confidence: 99%

Optimal path planning based on A* algorithm for submarine mining vehicle

Chen

Sui

et al. 2020

MATEC Web Conf.

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Deep sea mining, as a frontier area in China, urgently needs to make progress in automatic navigation technology. In order to improve the operation efficiency of the seabed mining machine on the soft seabed, the submarine mining vehicle which complete the mining work in a certain mining area need to enter the next mining area quickly and economically. As a classical algorithm, the majority of scholars consider that A * algorithm is the most practical path planning search algorithm. Considering the limitation of operation conditions, the three-dimensional diagram is transformed into two-dimensional diagram by interpolation method when the seabed terrain conditions allow, and then the obstacles can be marked in two-dimensional diagram. A* algorithm was applied into the path planning of mining truck. The simulation results of the paper show that path cost, turning time and turning mode should be considered in the process of avoiding obstacles.

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Path planning algorithm to minimize an overlapped path and turning number for an underwater mining robot

Cited by 10 publications

References 10 publications

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Optimal path planning based on A* algorithm for submarine mining vehicle

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