Abstract:The determination of the route of movement is a key factor which enables navigation. In this article, the authors present the methodology of using different resolution terrain passability maps to generate graphs, which allow for the determination of the optimal route between two points. The routes are generated with the use of two commonly used pathfinding algorithms: Dijkstra’s and A-star. The proposed methodology allows for the determination of routes in various variants—a more secure route that avoids all t… Show more
“…Since the 1970s, many studies on the path planning problem have been conducted. The path planning methods can be roughly divided into several groups: the grid search methods, such as A* algorithm [ 2 ], Depth-First Search (DFS) [ 3 ], Breadth-first Search (BFS) [ 4 ], and Dijkstra algorithm [ 5 ]; the sampling-based methods, such as Probabilistic Roadmap (PRM) [ 6 ] and Rapidly Exploring Random Tree (RRT) [ 7 ]; heuristic or swarm intelligence algorithms, such as Genetic Algorithm (GA) [ 8 ], Ant Colony Optimization (ACO) [ 9 ], Particle Swarm Optimization (PSO) [ 10 ], and neural network-based algorithms [ 11 ]; the potential field methods, such as Artificial Potential Field (APF) [ 12 ], optimal-control based method [ 13 ], and geometry-based method [ 14 ]. The listed algorithms have certain advantages and disadvantages.…”
To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.
“…Since the 1970s, many studies on the path planning problem have been conducted. The path planning methods can be roughly divided into several groups: the grid search methods, such as A* algorithm [ 2 ], Depth-First Search (DFS) [ 3 ], Breadth-first Search (BFS) [ 4 ], and Dijkstra algorithm [ 5 ]; the sampling-based methods, such as Probabilistic Roadmap (PRM) [ 6 ] and Rapidly Exploring Random Tree (RRT) [ 7 ]; heuristic or swarm intelligence algorithms, such as Genetic Algorithm (GA) [ 8 ], Ant Colony Optimization (ACO) [ 9 ], Particle Swarm Optimization (PSO) [ 10 ], and neural network-based algorithms [ 11 ]; the potential field methods, such as Artificial Potential Field (APF) [ 12 ], optimal-control based method [ 13 ], and geometry-based method [ 14 ]. The listed algorithms have certain advantages and disadvantages.…”
To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.
“…There are many papers on route planning considering terrain information [8][9][10][11][12]. The authors of [8] presented a control architecture for fast quadruped locomotion over rough terrain.…”
Section: Introductionmentioning
confidence: 99%
“…[8] applied a Dynamic Programming (DP) algorithm to plan a minimum-cost path across the terrain. The authors of [9] addressed using different resolution terrain passability maps to construct graphs, which allow for the determination of the optimal route between two points. The routes were generated using two path planners: Dijkstra's and A * .…”
Section: Introductionmentioning
confidence: 99%
“…However, DP [8], A * [9,11,12], or D * [10] require that the entire region is already completely covered by multiple grid cells. As one increases the entire workspace size, the number of grid cells covering the workspace also increases.…”
Section: Introductionmentioning
confidence: 99%
“…As one increases the size of the entire region in ACO [6], DP [8], A * [9,11,12], D * [10], or Theta * [4,5], the number of grid cells covering the entire region also increases. Our paper does not use grid cells to cover the entire region, and the shortest route is built using the node network deployed by two virtual vehicles.…”
Route planning considering terrain information is useful for the navigation of autonomous ground vehicles (AGV) on complicated terrain surfaces, such as mountains with rivers. For instance, an AGV in mountains cannot cross a river or a valley that is too steep. This article addresses a novel route-planning algorithm that is time-efficient in building a sub-optimal route considering terrain information. In order to construct a route from the start to the end point in a time-efficient manner, we simulate two virtual vehicles that deploy virtual nodes iteratively, such that the connected node network can be formed. The generated node network serves as a topological map for a real AGV, and we construct the shortest route from the start to the end point utilizing the network. The route is weighted considering the route length, the steepness of the route, and the traversibility of the route. Through MATLAB simulations, we demonstrate the effectiveness of the proposed route-planning algorithm by comparing it with RRT-star planners.
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