Standard Particle Swarm Optimization on Source Seeking Using Mobile Robots

Zou, R.; Kalivarapu, Vijay; Bhattacharya, Sourabh; Winer, Eliot H.; Oliver, James H.

doi:10.2514/6.2015-0897

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…This technique assumes prior knowledge about the wave length and frequency of the waves which are often unknown. Our previous work [19,20,21] attempted to apply particle swarm optimization (PSO) to solve this problem. As a population based heuristic method, the implementation of PSO on source seeking agents has shown promising performance with high success rate.…”

Section: Introductionmentioning

confidence: 99%

Path planning algorithms for source seeking agents

Zou

Tian

Bhattacharya

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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In this paper, we address the problem of source-seeking for sources that transmit signals which decay according to a specific path loss model. This is typical for sources emitting electromagnetic signals. Based on this decay profile, we analyze the scenario where the seeker has partial knowledge about the equation that governs the decay profile. We present an approximate gradient descent based iterative algorithm for this case. We prove that the seeker converges to the source after traveling a finite distance. We also present a quantitative measure of the performance of our algorithm in terms of the number of measurements required by the seeker. Next, we extend the aforementioned algorithm to provide cooperative path planning algorithms for a team of seekers. Finally, we provide simulation results that show the effectiveness of the algorithms.

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Section: Introductionmentioning

confidence: 99%

Path planning algorithms for source seeking agents

Zou

Tian

Bhattacharya

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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“…Applications for this appear to be favorable with robotics where ground swarm-agents can easily adjust their current heading to match the algorithm [39]. In the case of space systems applications, PSO can be utilized for the design phase of a spacecraft.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Modeling and Simulation of Vehicle Performance in a UAV Swarm Using Horizon Simulation Framework

Frye

Mehiel

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A UAV swarm is simulated using Horizon Simulation Framework. The asset utilized for the swarm agent is a simplified model of the MQ-1 Predator, a large fixed-wing aircraft. The simulated swarm utilizes a decentralized cooperative control approach to command the assets through the use of digital pheromones and a pheromone map. Each vehicle operates at steady-state flight conditions of 36 m/s with an altitude of 1,800 m, and utilize an LQR set-point controller to maneuver through the pheromone map. All pheromone and aircraft related models are written in Python to expand the HSF scripting capability and include airborne scenarios. The simulation study focuses in the variation of three parameters in the repelling pheromone model. The first two are the update and deposit parameters with values of 2, 10, and 18. The third is the threshold parameter with values of 1e-02, 1e-10, and 1e-18. The lower parameter values provide more time-on-target while the higher parameters allow the swarm to search the surrounding area by only visiting the grid-space once. iv ACKNOWLEDGMENTS This thesis is dedicated to all "Magnificent Bastards" from 2nd Battalion 4th Marines, and to all of my other 5th Marine Regiment brothers whom I served with. Thanks to Cpl Dustin Libby and SgtMaj Joseph Ellis for motivating and influencing me to never give up on my goals. Semper Fi brothers, you will never be forgotten. Thank you to Dr Paulo Iscold and Dr Colleen Kirk for taking time out of their busy schedule to be part of my committee. Thank you to Dr Kira Abercromby for making aerospace courses fun and more interesting; thanks for giving me the inspiration to pursue this topic. Thanks to all the faculty and staff of the Aerospace Department for all your passion and dedication to students. Thanks to my family and friends for their support. Thanks to my Hufflepuff friend Karen Kauffman for introducing me to some great reading material, and for all the riveting programming and hackathon conversations. Thanks to my life mentors and friends, Dr Richard Robertson and Craig Snow, for all their valuable advice, and a special thanks to Dr Eric Mehiel for being my advisor. Thank you for being a positive influence in my life and for introducing me to the world of dynamics and control that I have come to enjoy so very much. Thank you for all your guidance and patience with me during the writing of this thesis. v

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Particle Swarm Optimization-Based Source Seeking

Zou

Kalivarapu

Winer

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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The task of locating a source based on the measurements of the signal emitted/emanating from it is called the source-seeking problem. In the past few years, there has been a lot of interest in deploying autonomous platforms for source-seeking. Some of the challenging issues with implementing autonomous source-seeking are the lack of a priori knowledge about the distribution of the emitted signal and presence of noise in both the environment and on-board sensor measurements. This paper proposes a planner for a swarm of robots engaged in seeking an electromagnetic source. The navigation strategy for the planner is based on Particle Swarm Optimization (PSO) which is a population-based stochastic optimization technique. An equivalence is established between particles generated in the traditional PSO technique, and the mobile agents in the swarm. Since the positions of the robots are updated using the PSO algorithm, modifications are required to implement the PSO algorithm on real robots to incorporate collision avoidance strategies. The modifications necessary to implement PSO on mobile robots, and strategies to adapt to real environments are presented in this paper. Our results are also validated on an experimental testbed. Note to Practitioners-This paper is inspired by the source seeking problem in which the signal emitted from the source is assumed to be very noisy, and the spatial distribution is assumed to be non-smooth. We focus our work specifically on electromagnetic sources. However, the strategies proposed in this paper are also applicable to other kinds of sources, for example, nuclear, radiological, chemical or biological. We develop a planner for a swarm of mobile agents that try to locate an unknown electromagnetic source. The mobile agents know their own positions and can measure the signal strength at their current location. They can share information among themselves, and plan for the next step. We propose a complete solution to ensure the effectiveness of PSO in complex environments where collisions may occur. We incorporate static and dynamic obstacle avoidance strategies in PSO to make it fully applicable to real-world scenario. We validate the proposed technique on an experimental testbed. As a part of our future work, we will extend the technique to locate multiple sources of different kinds. Abstract-Signal source seeking using autonomous vehicles is a complex problem. The complexity increases manifold when signal intensities captured by physical sensors onboard are noisy and unreliable. Added to the fact that signal strength decays with distance, noisy environments make it extremely difficult to describe and model a decay function. This paper addresses our work with seeking maximum signal strength in a continuous electromagnetic signal source with mobile robots, using Particle Swarm Optimization (PSO). A one to one correspondence with swarm members in a PSO and physical mobile robots is established and the positions of the robots are iteratively updated as the PSO algorithm proceeds...

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Standard Particle Swarm Optimization on Source Seeking Using Mobile Robots

Cited by 3 publications

References 30 publications

Path planning algorithms for source seeking agents

Path planning algorithms for source seeking agents

Modeling and Simulation of Vehicle Performance in a UAV Swarm Using Horizon Simulation Framework

Particle Swarm Optimization-Based Source Seeking

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