Particle Swarm Optimization for Autonomous Source Seeking
We explored the novel idea of implementing Particle Swarm Optimization (PSO) on a swarm of physical mobile robots to conduct a source seeking task. Our focus is on electromagnetic sources which can be cell phone signals or signals from remote control devices. We consider each robot as a particle, and they evolve in the search space looking for the source. To adapt PSO to real robots, we incorporated constraints posed by robot physical limitations into this computational optimization technique. We also explored several PSO variations and how they could be applied to this problem effectively.
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.
Rui Zou et al.,
Particle swarm optimization-based source seeking. In
IEEE Transactions on Automation Science and Engineering, vol. PP, no. 99, pp. 1-11, 2015.
In this paper, we address the problem of seeking a source that emits signal described by a function that is radially symmetric, and decays with increasing distance. Electromagnetic signals, acoustic signals, vapor emission, etc, are examples of such signals. We analyze a scenario in which a team of mobile agents, called seekers, try to locate the source without any prior knowledge about the decay profile. In contradistinction to existing techniques, we use a non-gradient based technique known as Particle Swarm Optimization (PSO) to overcome the difficulties posed due to lack of a mathematical model for the decay profile in real scenarios. We study two variations of PSO and implement on a real noisy source. We compare their mechanism and performance. Finally, we validate our conclusions through experiments.
Rui Zou et al.,
Swarm optimization techniques for multi-agent source localization. In
IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Pages 402-407, 2014.
In this paper, we address the problem of seeking a source in an environment with obstacles. The source emits signal described by a function that is radially symmetric, and decays with increasing distance. Due to the noisy nature of the signal, PSO algorithm is applied to search for the global maximum of signal strength in an obstacle-free environment. We propose two static obstacle avoidance strategies and a dynamic one to deal with more complex environments with obstacles. We validate the effectiveness of our strategies with simulation and experiments. The obstacle avoidance strategies proposed in this paper expand the application of PSO on robot source seeking.
Rui Zou et al.,
Particle Swarm Optimization for Source Localization in Environment with Obstacles. In
IEEE International Symposium on Intelligent Control (ISIC), Pages 1602-1607, 2014.
