scholarly journals A New Challenge: Path Planning for Autonomous Truck of Open-Pit Mines in The Last Transport Section

2020 ◽  
Vol 10 (18) ◽  
pp. 6622
Author(s):  
Ziyu Zhao ◽  
Lin Bi
Keyword(s):  
Path Planning ◽  
High Precision ◽  
Optimal Path ◽  
Transportation Cost ◽  
Open Pit ◽  
Open Pit Mining ◽  
A Algorithm ◽  
Digital Map ◽  
Elevation Data ◽  
Cutting Zone

During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.

scholarly journals A Heuristic Elastic Particle Swarm Optimization Algorithm for Robot Path Planning

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 99 ◽  
Author(s):  
Haiyan Wang ◽  
Zhiyu Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robots ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Path ◽  
Particle Swarm ◽  
A Algorithm ◽  
Swarm Optimization ◽  
Elastic Particle

Path planning, as the core of navigation control for mobile robots, has become the focus of research in the field of mobile robots. Various path planning algorithms have been recently proposed. In this paper, in view of the advantages and disadvantages of different path planning algorithms, a heuristic elastic particle swarm algorithm is proposed. Using the path planned by the A* algorithm in a large-scale grid for global guidance, the elastic particle swarm optimization algorithm uses a shrinking operation to determine the globally optimal path formed by locally optimal nodes so that the particles can converge to it rapidly. Furthermore, in the iterative process, the diversity of the particles is ensured by a rebound operation. Computer simulation and real experimental results show that the proposed algorithm not only overcomes the shortcomings of the A* algorithm, which cannot yield the shortest path, but also avoids the problem of failure to converge to the globally optimal path, owing to a lack of heuristic information. Additionally, the proposed algorithm maintains the simplicity and high efficiency of both the algorithms.

scholarly journals Stochastic C-GNet Environment Modeling and Path Planning Optimization in a Narrow and Long Space

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jianjian Yang ◽  
Zhiwei Tang ◽  
Xiaolin Wang ◽  
Zirui Wang ◽  
Biaojun Yin ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Three Dimensional ◽  
Test Model ◽  
Environment Modeling ◽  
Grid Network ◽  
Planning Optimization ◽  
Elevation Data ◽  
Dynamic Stochastic ◽  
Terrain Elevation

This study proposes a novel method of optimal path planning in stochastic constraint network scenarios. We present a dynamic stochastic grid network model containing semienclosed narrow and long constraint information according to the unstructured environment of an underground or mine tunnel. This novel environment modeling (stochastic constraint grid network) computes the most likely global path in terms of a defined minimum traffic cost for a roadheader in such unstructured environments. Designing high-dimensional constraint vector and traffic cost in nodes and arcs based on two- and three-dimensional terrain elevation data in a grid network, this study considers the walking and space constraints of a roadheader to construct the network topology for the traffic cost value weights. The improved algorithm of variation self-adapting particle swarm optimization is proposed to optimize the regional path. The experimental results both in the simulation and in the actual test model settings illustrate the performance of the described approach, where a hybrid, centralized-distributed modeling method with path planning capabilities is used.

scholarly journals An Improved A-Star Algorithm Considering Water Current, Traffic Separation and Berthing for Vessel Path Planning

2019 ◽  
Vol 9 (6) ◽  
pp. 1057 ◽  
Author(s):  
Chenguang Liu ◽  
Qingzhou Mao ◽  
Xiumin Chu ◽  
Shuo Xie
Keyword(s):  
Path Planning ◽  
Path Length ◽  
Optimal Path ◽  
Water Current ◽  
Path Generation ◽  
Collision Risk ◽  
A Algorithm ◽  
Risk Models ◽  
Planning Methods ◽  
Path Cost

A traditional A-Star (A*) algorithm generates an optimal path by minimizing the path cost. For a vessel, factors of path length, obstacle collision risk, traffic separation rule and manoeuvrability restriction should be all taken into account for path planning. Meanwhile, the water current also plays an important role in voyaging and berthing for vessels. In consideration of these defects of the traditional A-Star algorithm when it is used for vessel path planning, an improved A-Star algorithm has been proposed. To be specific, the risk models of obstacles (bridge pier, moored or anchored ship, port, shore, etc.) considering currents, traffic separation, berthing, manoeuvrability restriction have been built firstly. Then, the normal path generation and the berthing path generation with the proposed improved A-Star algorithm have been represented, respectively. Moreover, the problem of combining the normal path and the berthing path has been also solved. To verify the effectiveness of the proposed A-Star path planning methods, four cases have been studied in simulation and real scenarios. The results of experiments show that the proposed A-Star path planning methods can deal with the problems denoted in this article well, and realize the trade-off between the path length and the navigation safety.

Optimal Path Planning Satisfying Complex Task Requirement in Uncertain Environment

Robotica ◽  
2019 ◽  
Vol 37 (11) ◽  
pp. 1956-1970 ◽  
Author(s):  
Xin-Yi Yu ◽  
Zhen-Yong Fan ◽  
Lin-Lin Ou ◽  
Feng Zhu ◽  
Yong-Kui Guo
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Transition System ◽  
Complex Task ◽  
A Algorithm ◽  
Environment Change ◽  
Uncertain Environment ◽  
Task Requirement ◽  
Global Optimal ◽  
Local Path

SummaryRobots often need to accomplish some complex tasks such as surveillance, response and obstacle avoidance. In this paper, a dynamic search method is proposed to generate optimal robot trajectories satisfying complex task requirement in uncertain environment. The LTL-A* algorithm is presented to generate a global optimal path and the A* algorithm is provided to modify the global optimal path. The task is specified by a linear temporal logic (LTL) formula, and a weighted transition system according to the known information in uncertain environment is modeled to describe the robot motion. Subsequently, a product automaton is constructed by combining the transition system with the task requirement. Based on the product automaton, the LTL-A* algorithm is proposed to generate a global optimal path. The local path planning based on the A* algorithm is employed to deal with the environment change during the process of tracking the global optimal path for the robot. The results of the simulation and experiments show that the proposed method can not only meet the complex task requirement in uncertain environment but also improve the search efficiency.

Multi-Robot Fish Path Planning Based on the Modified A* Algorithm

2014 ◽  
Vol 568-570 ◽  
pp. 1054-1058 ◽  
Author(s):  
Qiang Hong ◽  
Mei Xiao Chen ◽  
Yan Song Deng
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Experimental Results ◽  
Robot Path Planning ◽  
A Algorithm ◽  
Optimal Path Planning ◽  
2D Simulation ◽  
Robot Fish ◽  
Robot Path ◽  
Multi Robot

Based on improved A* algorithm, this paper proposes the optimal path planning of robot fish in globally known environment, so as to achieve better coordination between the robot fish by means of improving their path planning. In the known obstacle environment which is rasterized, target nodes are generated via smoothing A* algorithm. The unnecessary connection points are removed then and the path is smoothed at the turning points. That improved algorithm, in combination with distributed scroll algorithms, is applied to multi-robot path planning in an effort to optimize the path with the avoidance of collision. The experimental results on the 2D simulation platform have verified the feasibility of that method.

Path Planning for Ground Simulation Objiect Based on A* Algorithm

2012 ◽  
Vol 229-231 ◽  
pp. 2019-2024 ◽  
Author(s):  
Zhi Qiang Zhao ◽  
Zhi Hua Liu ◽  
Jia Xin Hao
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Optimal Path ◽  
Planning Method ◽  
Heuristic Function ◽  
A Algorithm

In the process of ground simulation object maneuver simulation in large-scale operation simulation, an efficient path planning method based on A*algorithm is proposed. By means of introducing all kind of geography factors and security factors into heuristic function, the plan reaching method solves the problem of finding an optimal path under acquiring enemy's situation and terrain data. Experiment results show that it has effectively raised path planning speed of A* algorithm and the scheme is practical and feasible.

scholarly journals Evaluation Principles of the Dust Influence of Mining Enterprises on the Environment

2019 ◽  
Vol 56 (3) ◽  
pp. 62-69 ◽  
Author(s):  
Semen G. Gendler ◽  
Marat L. Rudakov ◽  
Vladimir S. Kuznetsov
Keyword(s):  
Mineral Resources ◽  
Negative Influence ◽  
Open Pit ◽  
Open Pit Mining ◽  
Digital Map ◽  
Protection Measures ◽  
Production Processes ◽  
Maximum Value ◽  
Refuse Dumps ◽  
Probabilistic Nature

Abstract It has been noted that the areas disturbed by open-pit mining together with the production processes in the extraction of mineral resources (drilling, blasting, transportation, etc.) have a negative influence on the environment in general and the atmosphere in particular. It has been indicated that, in percentage terms, dusting of refuse dumps and tailing dumps plays a prevailing role in the total amount of dust generated. It has been stated that the processes of formation and subsequent transfer of dust in the atmosphere depend on the combination of meteorological and mining factors that have a probabilistic nature in time and space. It has been shown that the maximum value of environmental risk characterises the level of dust influence, at which reduction environmental protection measures should be directed. The present paper proposes a procedure for evaluation of the dusty influence of mining enterprises on the environment. Under the conditions of Olenegorsk GOK, a GIS has been compiled – a project of the study area and, based on geo-information modelling, the results of calculating dust concentrations in the air have been imposed on a digital map of the area.

scholarly journals Development of the software and technical complex of the high-precision satellite positioning system in the conditions of open pit mining processes

2020 ◽  
Vol 4 (315) ◽  
pp. 42-48
Author(s):  
A. A. Baltiyeva ◽  
◽  
A. S. Raskaliyaev ◽  
A. I. Samsonenko ◽  
L. S. Shamganova ◽  
...  
Keyword(s):  
High Precision ◽  
Base Station ◽  
Base Stations ◽  
Open Pit ◽  
Open Pit Mining ◽  
Main Task ◽  
Positioning System ◽  
Differential Correction ◽  
Satellite Positioning ◽  
Mine Surveying

The article presents technical solutions for the implementation of a high-precision satellite positioning system when performing mine surveying in an open pit. A system was put into commercial operation at one of the fields of JSC "Sokolovsko-Sarbayskoe mining and processing production association" (JSC "SSGPO") this year. The project was funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan through grants for scientific and technical projects 2018–2020 and was co-financed by a private partner, JSC "SSGPO". All work was carried out jointly with the Subsidiary Limited Liability Company "Institute of Space Engineering and Technology". The technology of differential correction of GNSS signals in the form of base stations of differential correction (BSDC) allows solving the problems of high-precision satellite positioning. The main task assigned to the continuously operating base station is the collection of code and phase data from GPS/GLONASS satellites and the distribution of this data to users (services of JSC "SSGPO" and specialists of contracting organizations performing mine surveying and geodetic work at the field). Development of a mobile module and the rationale for its inclusion in the BSDC is provided in this work.

Development of a Non-hazardous Path Planning Algorithm for Mars Rover in Real Terrain Enviroment

2018 ◽  
Vol 19 (11) ◽  
pp. 734-744 ◽  
Author(s):  
G. Wang ◽  
A. V. Fomichev
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Mobile Object ◽  
Risk Indicator ◽  
A Algorithm ◽  
Mars Rover ◽  
Planning Algorithm ◽  
Comprehensive Comparison ◽  
Path Planning Algorithm ◽  
Wheeled Vehicles

In order to fulfill the corresponding task successfully, a crucial issue should be addressed is the path planning for the exploration of the Mars surface owing to the environmental features of the tough terrain. Traditional path planning algorithms, such as the A* algorithm and the improved A* algorithm — the algorithm D* and the Field D*, which have been successfully implemented on the planetary rover during the expeditions of the Moon and Mars, have the problem of finding the shortest optimal path. One of the more effective algorithms derived from the modified A* refers to the Basic Theta* or the Lazy Theta* algorithms, which are faster any-angle path planning. Additionally, the algorithms can find shorter routes. In this paper, derived from a comprehensive comparison of the existing algorithms (A*, Basic Theta* and Lazy Theta*), a novel modification of the Lazy AT methodology is proposed to reduce the calculation time and obtain a shorter path. Based on the analysis of the surface feature of the Mars topography, the corresponding safety indicator is discussed. The principal hazards of the wheeled vehicles during the exploration on the surface of the Mars are the slopes and the obstacles. According to the requirements for avoiding obstacles as well as the exploration stability of the Mars rover in the period of the exploration, the following topographic coefficients have been chosen to develop the hazard indicator, i.e., the inclination angle of the terrain, the surface roughness and the height difference of the terrain. In addition, to obtain a safe trajectory in algorithm Lazy AT on the Mars surface, the terrain hazard indicator (risk indicator) for the modification of the Risk Lazy AT algorithm is also proposed in this paper. The comparing analysis modeling results of the Risk Lazy AT and Lazy Theta* has shown that our proposed algorithm Risk Lazy AT can guarantee the safety movement of a mobile object during the exploration on the surface of the planet. In light of the real-world surface features of the Mars terrain, the digital map of the planet’s surface has been developed and the spatial routing of the rover has been tested with our novel proposed algorithm, so-called Risk Lazy AT.

scholarly journals Human experience–inspired path planning for robots

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875704 ◽  
Author(s):  
Wenyong Gong ◽  
Xiaohua Xie ◽  
Yong-Jin Liu
Keyword(s):  
Path Planning ◽  
Energy Function ◽  
Optimal Path ◽  
Human Experience ◽  
Service Robots ◽  
A Algorithm ◽  
Slowing Down ◽  
Path Distance ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In this article, we present a human experience–inspired path planning algorithm for service robots. In addition to considering the path distance and smoothness, we emphasize the safety of robot navigation. Specifically, we build a speed field in accordance with several human driving experiences, like slowing down or detouring at a narrow aisle, and keeping a safe distance to the obstacles. Based on this speed field, the path curvatures, path distance, and steering speed are all integrated to form an energy function, which can be efficiently solved by the A* algorithm to seek the optimal path by resorting to an admissible heuristic function estimated from the energy function. Moreover, a simple yet effective fast path smoothing algorithm is proposed so as to ease the robots steering. Several examples are presented, demonstrating the effectiveness of our human experience–inspired path planning method.

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