scholarly journals Approximation algorithms for tours of height-varying view cones

2018 ◽  
Vol 38 (2-3) ◽  
pp. 224-235 ◽  
Author(s):  
Patrick A Plonski ◽  
Volkan Isler
Keyword(s):  
Precision Agriculture ◽  
Ground Plane ◽  
Optimal Solution ◽  
Field Of View ◽  
Coverage Problem ◽  
Polynomial Time Approximation Algorithm ◽  
Novel Variant ◽  
The Common ◽  
The Traveling Salesman Problem ◽  
Theoretical Performance

We introduce a novel coverage problem that arises in aerial surveying applications. The goal is to compute a shortest path that visits a given set of cones. The apex of each cone is restricted to lie on the ground plane. The common angle [Formula: see text] of the cones represent the field of view of the onboard camera. The cone heights, which can be varying, correspond with the desired observation quality (e.g. resolution). This problem is a novel variant of the traveling salesman problem with neighborhoods (TSPN). We name it Cone-TSPN. Our main contribution is a polynomial time approximation algorithm for Cone-TPSN. We analyze its theoretical performance and show that it returns a solution whose length is at most [Formula: see text] times the length of the optimal solution where [Formula: see text] and [Formula: see text] are the heights of the tallest and shortest input cones, respectively.We demonstrate the use of our algorithm in a representative precision agriculture application. We further study its performance in simulation using randomly generated cone sets. Our results indicate that the performance of our algorithm is superior to standard solutions.

scholarly journals Approximation algorithms for tours of orientation-varying view cones

2020 ◽  
Vol 39 (4) ◽  
pp. 389-401
Author(s):  
Nikolaos Stefas ◽  
Patrick A Plonski ◽  
Volkan Isler
Keyword(s):  
Approximation Algorithm ◽  
Ground Plane ◽  
Orientation Angle ◽  
Apex Angle ◽  
Field Of View ◽  
Plane Normal ◽  
Polynomial Time Approximation Algorithm ◽  
New Variant ◽  
Angle Difference ◽  
Novel Variant

This article considers the problem of finding a shortest tour to visit viewing sets of points on a plane. Each viewing set is represented as an inverted view cone with apex angle [Formula: see text] and height [Formula: see text]. The apex of each cone is restricted to lie on the ground plane. Its orientation angle (tilt) [Formula: see text] is the angle difference between the cone bisector and the ground plane normal. This is a novel variant of the 3D Traveling Salesman Problem with Neighborhoods (TSPN) called Cone-TSPN. One application of Cone-TSPN is to compute a trajectory to observe a given set of locations with a camera: for each location, we can generate a set of cones whose apex and orientation angles [Formula: see text] and [Formula: see text] correspond to the camera’s field of view and tilt. The height of each cone [Formula: see text] corresponds to the desired resolution. Recently, Plonski and Isler presented an approximation algorithm for Cone-TSPN for the case where all cones have a uniform orientation angle of [Formula: see text]. We study a new variant of Cone-TSPN where we relax this constraint and allow the cones to have non-uniform orientations. We call this problem Tilted Cone-TSPN and present a polynomial-time approximation algorithm with ratio [Formula: see text], where [Formula: see text] is the set of all cone heights. We demonstrate through simulations that our algorithm can be implemented in a practical way and that by exploiting the structure of the cones we can achieve shorter tours. Finally, we present experimental results from various agriculture applications that show the benefit of considering view angles for path planning.

scholarly journals Efficient Algorithms for Max-Weighted Point Sweep Coverage on Lines

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1457
Author(s):  
Dieyan Liang ◽  
Hong Shen
Keyword(s):  
Optimal Algorithm ◽  
Approximate Solutions ◽  
Approximation Ratio ◽  
Mobile Sensors ◽  
Np Hard ◽  
Coverage Problem ◽  
Polynomial Time Approximation Algorithm ◽  
Weighted Point ◽  
Eulerian Graph ◽  
Set Of Points

As an important application of wireless sensor networks (WSNs), deployment of mobile sensors to periodically monitor (sweep cover) a set of points of interest (PoIs) arises in various applications, such as environmental monitoring and data collection. For a set of PoIs in an Eulerian graph, the point sweep coverage problem of deploying the fewest sensors to periodically cover a set of PoIs is known to be Non-deterministic Polynomial Hard (NP-hard), even if all sensors have the same velocity. In this paper, we consider the problem of finding the set of PoIs on a line periodically covered by a given set of mobile sensors that has the maximum sum of weight. The problem is first proven NP-hard when sensors are with different velocities in this paper. Optimal and approximate solutions are also presented for sensors with the same and different velocities, respectively. For M sensors and N PoIs, the optimal algorithm for the case when sensors are with the same velocity runs in O(MN) time; our polynomial-time approximation algorithm for the case when sensors have a constant number of velocities achieves approximation ratio 12; for the general case of arbitrary velocities, 12α and 12(1−1/e) approximation algorithms are presented, respectively, where integer α≥2 is the tradeoff factor between time complexity and approximation ratio.

scholarly journals Battery-Aware Electric Truck Delivery Route Exploration

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2096
Author(s):  
Donkyu Baek ◽  
Yukai Chen ◽  
Naehyuck Chang ◽  
Enrico Macii ◽  
Massimo Poncino
Keyword(s):  
Heuristic Algorithms ◽  
Combustion Engine ◽  
Optimal Solution ◽  
Optimal Routing ◽  
Total Energy Consumption ◽  
Delivery Problem ◽  
Package Delivery ◽  
Delivery Route ◽  
The Traveling Salesman Problem ◽  
Critical Metric

The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which use a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study.

Improved Ant Colony Algorithm for Optimal Solution TSP

2013 ◽  
Vol 765-767 ◽  
pp. 699-702
Author(s):  
Tian Yuan Zhou
Keyword(s):  
Traveling Salesman Problem ◽  
Ant Colony Algorithm ◽  
Search Strategy ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Algorithm Analysis ◽  
Simulation Testing ◽  
The Traveling Salesman Problem ◽  
Improved Ant Colony Algorithm

Based on the ant colony algorithm analysis and research, this paper proposed an improved ant colony algorithm. Through updating pheromone and optimal search strategy, then applied to the Traveling Salesman Problem (TSP), effectively improved the searching capability of the algorithm. Finally through the simulation testing and analysis, verified that the improved ant colony algorithm is effective, and has good performance.

scholarly journals Traveling-Salesman-Problem Algorithm Based on Simulated Annealing and Gene-Expression Programming

Information ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 7 ◽  
Author(s):  
Ai-Hua Zhou ◽  
Li-Peng Zhu ◽  
Bin Hu ◽  
Song Deng ◽  
Yan Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Heuristic Algorithms ◽  
Gene Expression Programming ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Np Hard Problem ◽  
Convergence Performance ◽  
The Traveling Salesman Problem

The traveling-salesman problem can be regarded as an NP-hard problem. To better solve the best solution, many heuristic algorithms, such as simulated annealing, ant-colony optimization, tabu search, and genetic algorithm, were used. However, these algorithms either are easy to fall into local optimization or have low or poor convergence performance. This paper proposes a new algorithm based on simulated annealing and gene-expression programming to better solve the problem. In the algorithm, we use simulated annealing to increase the diversity of the Gene Expression Programming (GEP) population and improve the ability of global search. The comparative experiments results, using six benchmark instances, show that the proposed algorithm outperforms other well-known heuristic algorithms in terms of the best solution, the worst solution, the running time of the algorithm, the rate of difference between the best solution and the known optimal solution, and the convergent speed of algorithms.

Comparison of Heuristics for Resolving the Traveling Salesman Problem with Information Technology

2014 ◽  
Vol 886 ◽  
pp. 593-597 ◽  
Author(s):  
Wei Gong ◽  
Mei Li
Keyword(s):  
Information Technology ◽  
Traveling Salesman Problem ◽  
Spanning Tree ◽  
Nearest Neighbor ◽  
Minimum Spanning Tree ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Problem Class ◽  
The Traveling Salesman Problem ◽  
Random Insertion

Traveling Salesman Problem (Min TSP) is contained in the problem class NPO. It is NP-hard, means there is no efficient way to solve it. People have tried many kinds of algorithms with information technology. Thus in this paper we compare four heuristics, they are nearest neighbor, random insertion, minimum spanning tree and heuristics of Christofides. We dont try to find an optimal solution. We try to find approximated short trips via these heuristics and compare them.

scholarly journals A panoramic synthetic vision model for improving collision avoidance in crowd simulation

2015 ◽  
Vol 6 (2) ◽  
pp. 1
Author(s):  
Ricardo Bustamante de Queiroz ◽  
Teófilo Dutra ◽  
Creto Vidal ◽  
Joaquim Cavalcante-Neto
Keyword(s):  
Virtual Reality ◽  
Visual Perception ◽  
Collision Avoidance ◽  
Vision System ◽  
Crowd Simulation ◽  
Field Of View ◽  
Panoramic View ◽  
Natural Manner ◽  
Synthetic Vision ◽  
The Common

Crowd Simulation is very important in many virtual reality applications, because it improves the sense of immersion of the users by making the population of agents in the environment to move as real crowds do. Recently, models for simulating crowds, in which each agent is equipped with a synthetic vision system, have shown interesting results regarding the natural manner in which the agents navigate inside the environment thanks to their visual perception. In this article, we propose an upgrade to the agent’s visual system with a panoramic view in order to allow an agent to expand its vision beyond the limit of 180o imposed by the common projection provided by rendering APIs. Also, we analyze different parameters, which are used to define the field of view, to investigate the influence they have on the agent’s behavior. The impacts that those changes may cause on the efficiency of the algorithms are also analysed. A visible change on the agent’s behavior is achieved by using the technique, with a slight loss of performance.

scholarly journals Approximation Algorithms for Barrier Sweep Coverage

2019 ◽  
Vol 30 (03) ◽  
pp. 425-448 ◽  
Author(s):  
Barun Gorain ◽  
Partha Sarathi Mandal
Keyword(s):  
Energy Source ◽  
Approximation Algorithm ◽  
Finite Length ◽  
Optimal Solution ◽  
Mobile Sensors ◽  
Continuous Curve ◽  
Mobile Sensor ◽  
Coverage Problem ◽  
Line Segments ◽  
Approximation Factor

Time-varying coverage, namely sweep coverage is a recent development in the area of wireless sensor networks, where a few mobile sensors sweep or monitor a comparatively large number of locations periodically. In this article, we study barrier sweep coverage with mobile sensors where the barrier is considered as a finite length continuous curve on a plane. The coverage at every point on the curve is time-variant. We propose an optimal solution for sweep coverage of a finite length continuous curve. Usually, energy source of a mobile sensor is a battery with limited power, so energy restricted sweep coverage is a challenging problem for long running applications. We propose an energy-restricted sweep coverage problem where every mobile sensor must visit an energy source frequently to recharge or replace its battery. We propose a [Formula: see text]-approximation algorithm for this problem. The proposed algorithm for multiple curves achieves the best possible approximation factor 2 for a special case. We propose a 5-approximation algorithm for the general problem. As an application of the barrier sweep coverage problem for a set of line segments, we formulate a data gathering problem. In this problem a set of mobile sensors is arbitrarily monitoring the line segments one for each. A set of data mules periodically collects the monitoring data from the set of mobile sensors. We prove that finding the minimum number of data mules to collect data periodically from every mobile sensor is NP-hard and propose a 3-approximation algorithm to solve it.

Geometry-Based Optimal Rate Coordination for Robot Manipulators With Redundancy

1992 ◽  
Author(s):  
K. R. Hareendra Varma ◽  
Ming Z. Huang
Keyword(s):  
Objective Function ◽  
Closed Form ◽  
Optimal Solution ◽  
Minimum Norm ◽  
Redundancy Resolution ◽  
Minimum Norm Solution ◽  
Serial Chain ◽  
Gradient Solution ◽  
Resolution Problem ◽  
The Common

Abstract The redundancy resolution problem for kinematically redundant serial chain manipulators is addressed. In this paper, we present a generalization of the geometry based rate allocation algorithm, developed initially in [12] for only minimum norm solution, to obtain the optimal joint rate solution for any specified objective function, with or without weightage. This generalization is made possible through a geometrical interpretation of the common pseudoinverse-based gradient solution scheme, and by developing a modified formulation for the objective function as a minimum criterion not with respect to the origin of the joint rate space, but with respect to another point in the joint rate space represented by the gradient of the specified objective. Application examples of the algorithm including procedures of solution are demonstrated using 7R manipulators with two generic types of geometry. A closed form optimal solution for the 7R anthropomorphic arm considered is also presented.

Weight and Cost Optimization of Midship Section Using Common Structural Rules

2020 ◽  
Vol 36 (03) ◽  
pp. 171-180
Author(s):  
Mesbah Sayebani ◽  
Abdolhossein Mohammadrahimi ◽  
Hossein Khoshdel Looyeh
Keyword(s):  
Limit State ◽  
Optimization Technique ◽  
Cost Optimization ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Computational Tool ◽  
Structural Rules ◽  
Ship Construction ◽  
The Common ◽  
Common Structural Rules

Cost and weight optimization in ship construction are usually investigated in the form of a multiobjective optimization problem. So far, many studies have been carried out to achieve various types of existing optimization objectives and different tools have been developed. Most of the studies in the field of structural optimization have focused on comparing the available optimization algorithms. In this study, a rule-based tool is developed based on the Common Structural Rules (CSRs), which despite its simplicity in application, provides high capabilities in producing an optimal solution. In the developed tool, structural analysis of serviceability limit state is performed by using the relationships of CSRs. The computational tool is created by MATLAB software (Mathworks, Natick, Massachusetts), and the optimization technique is a genetic algorithm. The performance of the computational tool is evaluated by analyzing the midship section of a chemical tanker. In the optimization procedure, weight and cost are assumed to have the same importance. From the results of the developed tool, all components of the weight and cost of ship construction decreased in the optimal solution relative to the initial design.

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