scholarly journals Path Planning with CPD Heuristics

2019 ◽  
Author(s):  
Massimo Bono ◽  
Alfonso E. Gerevini ◽  
Daniel D. Harabor ◽  
Peter J. Stuckey
Keyword(s):  
Path Planning ◽  
Optimal Solution ◽  
State Spaces ◽  
Current Node ◽  
Feasible Path

Compressed Path Databases (CPDs) are a leading technique for optimal pathfinding in graphs with static edge costs. In this work we investigate CPDs as admissible heuristic functions and we apply them in two distinct settings: problems where the graph is subject to dynamically changing costs, and anytime settings where deliberation time is limited. Conventional heuristics derive cost-to-go estimates by reasoning about a tentative and usually infeasible path, from the current node to the target. CPD-based heuristics derive cost-to-go estimates by computing a concrete and usually feasible path. We exploit such paths to bound the optimal solution, not just from below but also from above. We demonstrate the benefit of this approach in a range of experiments on standard gridmaps and in comparison to Landmarks, a popular alternative also developed for searching in explicit state-spaces.

scholarly journals Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM

2021 ◽  
Vol 11 (16) ◽  
pp. 7599
Author(s):  
Qiang Cheng ◽  
Wei Zhang ◽  
Hongshuai Liu ◽  
Ying Zhang ◽  
Lina Hao
Keyword(s):  
Path Planning ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Gaussian Mixture ◽  
Target Point ◽  
Dijkstra Algorithm ◽  
Probabilistic Roadmap ◽  
Smooth Path ◽  
Starting Point ◽  
Feasible Path

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.

scholarly journals Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

2021 ◽  
Vol 17 (4) ◽  
pp. 491-505
Author(s):  
G. Kulathunga ◽  
◽  
D. Devitt ◽  
R. Fedorenko ◽  
A. Klimchik ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Free Path ◽  
Optimal Path ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Straight Lines ◽  
Path Planning Algorithm

Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.

Planning the Shortest Path in Cluttered Environments: A Review and a Planar Convex Hull-Based Approach

2019 ◽  
Vol 19 (4) ◽  
Author(s):  
Nafiseh Masoudi ◽  
Georges M. Fadel ◽  
Margaret M. Wiecek
Keyword(s):  
Path Planning ◽  
Shortest Path ◽  
Time Complexity ◽  
Optimal Path ◽  
Optimal Solution ◽  
Free Graph ◽  
Worst Case ◽  
Cluttered Environments ◽  
Planar Convex ◽  
Polyhedral Obstacles

Abstract Routing or path-planning is the problem of finding a collision-free and preferably shortest path in an environment usually scattered with polygonal or polyhedral obstacles. The geometric algorithms oftentimes tackle the problem by modeling the environment as a collision-free graph. Search algorithms such as Dijkstra’s can then be applied to find an optimal path on the created graph. Previously developed methods to construct the collision-free graph, without loss of generality, explore the entire workspace of the problem. For the single-source single-destination planning problems, this results in generating some unnecessary information that has little value and could increase the time complexity of the algorithm. In this paper, first a comprehensive review of the previous studies on the path-planning subject is presented. Next, an approach to address the planar problem based on the notion of convex hulls is introduced and its efficiency is tested on sample planar problems. The proposed algorithm focuses only on a portion of the workspace interacting with the straight line connecting the start and goal points. Hence, we are able to reduce the size of the roadmap while generating the exact globally optimal solution. Considering the worst case that all the obstacles in a planar workspace are intersecting, the algorithm yields a time complexity of O(n log(n/f)), with n being the total number of vertices and f being the number of obstacles. The computational complexity of the algorithm outperforms the previous attempts in reducing the size of the graph yet generates the exact solution.

Trajectory planning for lab robots based on global vision and Voronoi roadmaps

Robotica ◽  
2005 ◽  
Vol 23 (4) ◽  
pp. 467-477 ◽  
Author(s):  
Waldir L. Roque ◽  
Dionísio Doering
Keyword(s):  
Path Planning ◽  
Trajectory Planning ◽  
Planning System ◽  
Global Vision ◽  
Navigation Control ◽  
Feasible Path ◽  
Robot Configurations

This paper discusses the techniques and their applications in the development of a path planning system composed of three modules, namely: global vision (GVM), trajectory planning (TPM) and navigation control (NCM). The GVM captures and processes the workspace image to identify the obstacle and the robot configurations. These configurations are used by the TPM to generate the Voronoi roadmap, to compute the maximal clearance shortest feasible path and the visibility pathway between two configurations. The NCM controls the robot functionalities and navigation. To validate the path planning system, three sets of experiments have been conducted using the Lab robot Khepera, which have shown very good results.

Path Planning for Mobile Robot with Clonal Selection Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2943-2946
Author(s):  
Yi Li ◽  
Zhen Hui Song ◽  
Li Zhao
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Clonal Selection ◽  
Optimal Solution ◽  
Clonal Selection Algorithm ◽  
Mutation Operator ◽  
Robot Path Planning ◽  
Selection Algorithm ◽  
Evolutionary Progress ◽  
Robot Path

Aiming at the problem of path planning for a mobile robot, an oriented clonal selection algorithm is proposed. Firstly, the static environment was expressed by a map with nodes and links. Secondly, the locations of target and obstacles were defined. Thirdly, an oriented mutation operator was used to accelerate the evolutionary progress. In this way, we can find an optimal solution with proposed oriented clonal algorithm. Experiment results demonstrate that the algorithm is simple, effective, to solve the problem of robot path planning in a static environment

CELLULAR AUTOMATA AND OPTIMAL PATH PLANNING

1996 ◽  
Vol 06 (03) ◽  
pp. 603-610 ◽  
Author(s):  
M. STÄMPFLE
Keyword(s):  
Cellular Automata ◽  
Path Planning ◽  
Optimal Path ◽  
Shortest Paths ◽  
Optimal Solution ◽  
Starting Position ◽  
Optimal Path Planning ◽  
Time Space ◽  
Neural Network Theory ◽  
Optimal Configurations

Cellular automata are deterministic dynamical systems in which time, space, and state values are discrete. Although they consist of uniform elements, which interact only locally, cellular automata are capable of showing complex behavior. This property is exploited for solving path planning problems in workspaces with obstacles. A new automaton rule is presented which calculates simultaneously all shortest paths between a starting position and a target cell. Based on wave propagation, the algorithm ensures that the dynamics settles down in an equilibrium state which represents an optimal solution. Rule extensions include calculations with multiple starts and targets. The method allows applications on lattices and regular, weighted graphs of any finite dimension. In comparison with algorithms from graph theory or neural network theory, the cellular automaton approach has several advantages: Convergence towards optimal configurations is guaranteed, and the computing costs depend only linearly on the lattice size. Moreover, no floating-point calculations are involved.

Path Planning of Planar Parallel Mechanisms Using Global Workspace Road Maps

2012 ◽  
Author(s):  
Wesley Au ◽  
Chao Chen ◽  
Hoam Chung
Keyword(s):  
Path Planning ◽  
Parallel Mechanisms ◽  
Road Map ◽  
Reachable Workspace ◽  
Global Workspace ◽  
Rotary Disk ◽  
Road Maps ◽  
Local Path ◽  
Feasible Path ◽  
Complex Manner

The aim of this paper is to define a process to expand the overall reachable workspace of the 5R mechanism and to conduct feasible path planning between configurations within that space, even in cases where the initial and final configurations exist in different modes. It is known that if a mechanism has multiple working modes, there is a unique workspace for each configuration, and they are often only partially connected to each other in a complex manner. Serial singularities allow the mechanism to remain stiff and controllable. These regions are utilised to link disconnected workspaces to maximise the overall reachable workspace to form the global workspace road map. This paper focuses on the 2-DOF path planning of the 5R planar mechanism. A generalised method for analysing complex workspaces of parallel mechanisms is proposed, the rotary disk search for finding the connected reachable workspace, a strategy for global path finding by analysing the global workspace road map and local path generation are discussed.

scholarly journals D* lite algorithm based path planning of mobile robot in static Environment

2012 ◽  
pp. 286-290
Author(s):  
Pradipta kumar Das ◽  
S .N. Patro ◽  
C. N. Panda ◽  
Bunil Balabantaray
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Shortest Path ◽  
Target Position ◽  
Initial Position ◽  
Distance Metric ◽  
Unknown Environment ◽  
Feasible Path ◽  
The Cost

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost function.

scholarly journals Trajectory tracking method based on the circulation of feasible path planning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252542
Author(s):  
Yi Yu ◽  
Peng Han
Keyword(s):  
Path Planning ◽  
Trajectory Tracking ◽  
Control Method ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Path Generation ◽  
Trajectory Tracking Control ◽  
Core System ◽  
Tracking Method ◽  
Feasible Path

The control method is the central point of the unmanned vehicles. As the core system to guarantee the properties of self-decision and trajectory tracking of the unmanned vehicles, a new kind of trajectory tracking method based on the circulation of feasible path planning for the unmanned vehicles are proposed in this article which considered the dynamics and kinematics characteristics of vehicles. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform of the trajectory tracking simulation of unmanned vehicles is built considering the mechanical properties of system elements and the mechanical characteristics. Finally, the proposed trajectory tracking method is verified. The tracking error would be reduced to make sure the vehicles move along the pre-set virtual track.

scholarly journals A Learning Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Chengtian Ouyang ◽  
Donglin Zhu ◽  
Fengqi Wang
Keyword(s):  
Path Planning ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Method ◽  
Search Process ◽  
Local Optimum ◽  
Test Function ◽  
Intelligent Optimization Algorithms ◽  
Robot Path

This paper solves the drawbacks of traditional intelligent optimization algorithms relying on 0 and has good results on CEC 2017 and benchmark functions, which effectively improve the problem of algorithms falling into local optimality. The sparrow search algorithm (SSA) has significant optimization performance, but still has the problem of large randomness and is easy to fall into the local optimum. For this reason, this paper proposes a learning sparrow search algorithm, which introduces the lens reverse learning strategy in the discoverer stage. The random reverse learning strategy increases the diversity of the population and makes the search method more flexible. In the follower stage, an improved sine and cosine guidance mechanism is introduced to make the search method of the discoverer more detailed. Finally, a differential-based local search is proposed. The strategy is used to update the optimal solution obtained each time to prevent the omission of high-quality solutions in the search process. LSSA is compared with CSSA, ISSA, SSA, BSO, GWO, and PSO in 12 benchmark functions to verify the feasibility of the algorithm. Furthermore, to further verify the effectiveness and practicability of the algorithm, LSSA is compared with MSSCS, CSsin, and FA-CL in CEC 2017 test function. The simulation results show that LSSA has good universality. Finally, the practicability of LSSA is verified by robot path planning, and LSSA has good stability and safety in path planning.

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