Abstract:In this paper, a probabilistic roadmap planner algorithm with the multi robot path planning problem have been proposed by using the A* search algorithm in a dynamic environment. The whole process consists of two phases. In the first phase: Preprocessing phase, the work space is converted into the configuration space, constructing a probabilistic roadmap graph in the free space, and finding the optimal path for each robot using a global planner that avoids the collision with the static obstacles. The second pha… Show more
“…It can be observed that the main drawback of the above approach is the reduction of autonomy in mobile robots, i.e., mobile robots cannot decide the number of path elements by themselves and the human help is needed. Additionally, if the number of path elements is not well established, a large value can over-define the path (this can complicate the post-processing task aspects [38] such as path smoothing [39], control [40]) and energetic efficiency [41], while a lower one can reduce the chances of finding the optimal path [42]. A way to deal with the above difficulty is to treat the path-planning as a Variable-Length-Vector Optimization Problem (VLV-OP) to determine the number of the variables (points) and their values (Cartesian coordinates) enhancing the application performance.…”
Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.
“…It can be observed that the main drawback of the above approach is the reduction of autonomy in mobile robots, i.e., mobile robots cannot decide the number of path elements by themselves and the human help is needed. Additionally, if the number of path elements is not well established, a large value can over-define the path (this can complicate the post-processing task aspects [38] such as path smoothing [39], control [40]) and energetic efficiency [41], while a lower one can reduce the chances of finding the optimal path [42]. A way to deal with the above difficulty is to treat the path-planning as a Variable-Length-Vector Optimization Problem (VLV-OP) to determine the number of the variables (points) and their values (Cartesian coordinates) enhancing the application performance.…”
Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.
“…There are many techniques that has been adopted to solve the path planning problem. The blind search technique, i.e., Breath First Search (BFS) [7] and Depth First Search (DFS) [8], traverses every single state available until the feasible solution is found. They are typically used to solve maze problems.…”
This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result.
“…Some methods meet only a few of these requirements, and some meet all requirements. Traditional path planning methods include depth first search(DFS) [3] , breadth first search(BFS) and swarm intelligence algorithms like genetic algorithms, ant colony algorithms, etc. However, these algorithms are often used to solve the problem that the solution to the problem with a constant risk range from the start point to the end point.…”
Path planning is an important research field in robotics, and it is also a key technology for realizing automatic navigation of aircraft. It is of great significance both in theory and in application. The task of planning trajectories for an aircraft has received considerable attention in the research literature. Most of the work assumes that the threat to the aircraft is static; less attention has been paid to the problem of the changing environment. However, the threat from the weather when the UAV is flying in the air is always changing. This paper introduces an improved BFS algorithm, which can find the best safe path when the threat is always changing.
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