Algorithms for Localization and Routing of Unmanned Vehicles in GPS-Denied Environments

Author(s):  
Bingyu Wang ◽  
Sivakumar Rathinam ◽  
Rajnikant Sharma ◽  
Kaarthik Sundar

A majority of the routing algorithms for unmanned aerial or ground vehicles rely on Global Positioning System (GPS) information for localization. However, disruption of GPS signals, by intention or otherwise, can render these algorithms ineffective. This article provides a way to address this issue by utilizing landmarks to aid localization in GPS-denied environments. Specifically, given a number of vehicles and a set of targets, we formulate a joint routing and landmark placement problem as a combinatorial optimization problem: to compute paths for the vehicles that traverse every target at least once, and to place landmarks to aid the vehicles in localization while each of them traverses its route, such that the sum of the traveling cost and the landmark placement cost is minimized. A mixed-integer linear program is presented, and a set of algorithms and heuristics are proposed for different approaches to address certain issues not covered by the linear program. The performance of each proposed algorithm is evaluated and compared through extensive computational and simulation results.

2021 ◽  
Author(s):  
Linh Nguyen

<pre>The paper addresses the problem of efficiently planning routes for multiple ground vehicles used in goods delivery services. Given popularity of today's e-commerce, particularly under the COVID-19 pandemic conditions, goods delivery services have been booming than ever, dominated by small-scaled (electric) bikes and promised by autonomous vehicles. However, finding optimal routing paths for multiple delivery vehicles operating simultaneously in order to minimize transportation cost is a fundamental but challenging problem. In this paper, it is first proposed to exploit the mixed integer programming paradigm to model the delivery routing optimization problem (DROP) for multiple simultaneously-operating vehicles given their energy constraints. The routing optimization problem is then solved by the multi-chromosome genetic algorithm, where the number of delivery vehicles can be optimized. The proposed approach was evaluated in a real-world experiment in which goods were expected to be delivered from a depot to 26 suburb locations in Canberra, Australia. The obtained results demonstrate effectiveness of the proposed algorithm.</pre>


2018 ◽  
Vol 200 ◽  
pp. 00005
Author(s):  
Halima Lakhbab

Wireless sensor networks are used for monitoring the environment and controlling the physical environment. Information gathered by the sensors is only useful if the positions of the sensors are known. One of the solutions for this problem is Global Positioning System (GPS). However, this approach is prohibitively costly; both in terms of hardware and power requirements. Localization is defined as finding the physical coordinates of a group of nodes. Localization is classified as an unconstrained optimization problem. In this work, we propose a new algorithm to tackle the problem of localization; the algorithm is based on a hybridization of Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Simulation results are given to illustrate the robustness and efficiency of the presented algorithm.


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