scholarly journals Heuristics for Two Depot Heterogeneous Unmanned Vehicle Path Planning to Minimize Maximum Travel Cost

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2461 ◽  
Author(s):  
Jungyun Bae ◽  
Woojin Chung
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Target Location ◽  
Travel Cost ◽  
Unmanned Vehicles ◽  
Traveling Salesman ◽  
Planning Problem ◽  
Travel Costs ◽  
Primal Dual ◽  
Multiple Depot

A solution to the multiple depot heterogeneous traveling salesman problem with a min-max objective is in great demand with many potential applications of unmanned vehicles, as it is highly related to a reduction in the job completion time. As an initial idea for solving the min-max multiple depot heterogeneous traveling salesman problem, new heuristics for path planning problem of two heterogeneous unmanned vehicles are proposed in this article. Specifically, a task allocation and routing problem of two (structurally) heterogeneous unmanned vehicles that are located in distinctive depots and a set of targets to visit is considered. The unmanned vehicles, being heterogeneous, have different travel costs that are determined by their motion constraints. The objective is to find a tour for each vehicle such that each target location is visited at least once by one of the vehicles while the maximum travel cost is minimized. Two heuristics based on a primal-dual technique are proposed to solve the cases where the travel costs are symmetric and asymmetric. The computational results of the implementation have shown that the proposed algorithms produce feasible solutions of good quality within relatively short computation times.

Robot Path Planning for Dimensional Measurement in Automotive Manufacturing

2005 ◽  
Vol 127 (2) ◽  
pp. 420-428 ◽  
Author(s):  
Weihua Sheng ◽  
Ning Xi ◽  
Mumin Song ◽  
Yifan Chen
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Performance Ratio ◽  
Planning System ◽  
Planning Problem ◽  
Robot Path Planning ◽  
Automotive Manufacturing ◽  
Dimensional Measurement ◽  
Robot Path

This paper addresses the robot path planning problem in our effort to develop a fully automated dimensional measurement system using an eye-in-hand robotic manipulator. First, the CAD-based vision sensor planning system developed in our lab is briefly introduced; it uses both the CAD model and the camera model to plan camera viewpoints. The planning system employs a decomposition-based approach to generate camera viewpoints that satisfy given task constraints. Second, to improve the efficiency of the eye-in-hand robot inspection system, robot path planning is studied, which is the focus of this paper. This problem is rendered as a Traveling Salesman Problem (TSP). A new hierarchical approach is developed to solve the TSP into its suboptimality. Instead of solving a large size TSP, this approach utilizes the clustering nature of the viewpoints and converts the TSP into a clustered Traveling Salesman Problem (CTSP). A new algorithm, which favors the intergroup paths, is proposed to solve the CTSP quickly. Performance of the new algorithm is analyzed. It is shown that instead of a fixed performance ratio as reported in some existing work, a constant bound can be achieved which is related to the diameter of the clusters. Experimental results demonstrate the effectiveness of the robot motion planning system. The proposed path planning approach can obtain sub-optimal solutions quickly for many large scale TSPs, which are common problems in many robotic applications.

scholarly journals ON SAMPLING BASED METHODS FOR THE DUBINS TRAVELING SALESMAN PROBLEM WITH NEIGHBORHOODS

2015 ◽  
Vol 2 (2) ◽  
pp. 57-61
Author(s):  
Petr Váňa ◽  
Jan Faigl
Keyword(s):  
Path Planning ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Computational Time ◽  
Solution Quality ◽  
Goal Region ◽  
Dubins Vehicle ◽  
Proposed Modification ◽  
Dubins Traveling Salesman Problem

In this paper, we address the problem of path planning to visit a set of regions by Dubins vehicle, which is also known as the Dubins Traveling Salesman Problem Neighborhoods (DTSPN). We propose a modification of the existing sampling-based approach to determine increasing number of samples per goal region and thus improve the solution quality if a more computational time is available. The proposed modification of the sampling-based algorithm has been compared with performance of existing approaches for the DTSPN and results of the quality of the found solutions and the required computational time are presented in the paper.

scholarly journals The multi-path Traveling Salesman Problem with stochastic travel costs

2014 ◽  
Vol 6 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Roberto Tadei ◽  
Guido Perboli ◽  
Francesca Perfetti
Keyword(s):  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Travel Costs

scholarly journals Genetic Algorithm for Combinatorial Path Planning: The Subtour Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-31 ◽  
Author(s):  
Giovanni Giardini ◽  
Tamás Kalmár-Nagy
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Optimal Strategy ◽  
Numerical Experiments ◽  
Solution Method ◽  
Autonomous Systems ◽  
Traveling Salesman ◽  
Optimal Solutions

The purpose of this paper is to present a combinatorial planner for autonomous systems. The approach is demonstrated on the so-called subtour problem, a variant of the classical traveling salesman problem (TSP): given a set of possible goals/targets, the optimal strategy is sought that connects goals. The proposed solution method is a Genetic Algorithm coupled with a heuristic local search. To validate the approach, the method has been benchmarked against TSPs and subtour problems with known optimal solutions. Numerical experiments demonstrate the success of the approach.

scholarly journals Optimization-based approach to path planning for closed chain robot systems

2011 ◽  
Vol 21 (4) ◽  
pp. 659-670 ◽  
Author(s):  
Wojciech Szynkiewicz ◽  
Jacek Błaszczyk
Keyword(s):  
Path Planning ◽  
Large Scale ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Closed Chain ◽  
Robot Systems ◽  
Equality And Inequality Constraints ◽  
Primal Dual ◽  
The Given

Optimization-based approach to path planning for closed chain robot systems An application of advanced optimization techniques to solve the path planning problem for closed chain robot systems is proposed. The approach to path planning is formulated as a "quasi-dynamic" NonLinear Programming (NLP) problem with equality and inequality constraints in terms of the joint variables. The essence of the method is to find joint paths which satisfy the given constraints and minimize the proposed performance index. For numerical solution of the NLP problem, the IPOPT solver is used, which implements a nonlinear primal-dual interior-point method, one of the leading techniques for large-scale nonlinear optimization.

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