Rui Zou

Particle Swarm Optimization-Based Source Seeking

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.

Swarm optimization techniques for multi-agent source localization

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.

Particle Swarm Optimization for Source Localization in Environment with Obstacles

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 in a complex environment with obstacles. Electromagnetic signals, acoustic signals, vapor emission, etc, are examples of such signals. 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 propose static and dynamic obstacle avoidance strategies, and integrate them with PSO in the source seeking problem. Finally, we validate the effectiveness of our strategies with simulation and experiments.

Target tracking on triangulation graphs

We investigate a variation of the art gallery problem in which a team of mobile guards tries to track an unpredictable intruder in a simply-connected polygonal environment. The guards are confined to move along the diagonals of a polygon, and are deployed according to the strategy proposed in [1] that provides an upper bound of ⌊ n/4 ⌋ mobile guards to cover a simply-connected polygonal environment. We introduce the concept of critical regions to generate event-triggered strategies for the guards. Based on these strategies, we present sufficient conditions for ⌊ n/4 ⌋ guards to track an unpredictable mobile intruder forever.

Standard Particle Swarm Optimization on Source Seeking Using Mobile Robots

In this paper, we explore the implementation of standard particle swarm optimization (SPSO) on a swarm of physical mobile robots conducting a source seeking task. The signal source is electromagnetic, whose strength is non-differentiable at many points making most gradient based source seeking strategies ineffective in this scenario. We analyze the physical limitations of the robots and modify SPSO accordingly to make them compatible with each other. We also compare different SPSO topology models to determine the one best suited for our problem. Finally, we incorporate obstacle avoidance strategies into PSO, and compare the performance of original PSO, SPSO 2006 and SPSO 2011 in a complex environment with obstacles. Simulation results demonstrate the efficacy of implementing SPSO to robot source seeking problem. Moreover, it is shown that SPSO 2011 is not only superior as an optimization method, but also provides better performance in robotic implementation compared to SPSO 2006 and original PSO.

Visibility based multi-agent surveillance strategies in decentralized network

In this paper, we address the problem of decentralized visibility-based target tracking for a team of mobile observers trying to track a team of mobile targets. Based on the results of previous work, we address the problem when the graph that models the communication network within the team of the obsrevers is not complete. We propose a hierarchical approach. At the upper level, each observer is allocated to a target through a local minimum cost matching. At the lower level, each observer computes its navigation strategy based on the results of the single observer-single target problem, thereby, decomposing a large multi-agent problem into several 2- agent problems. Finally, we evaluate the performance of the proposed strategy in simulations and experiments.

Visibility-Based Finite-Horizon Target Tracking Game

In this letter, we address a visibility-based target-tracking problem in which a mobile observer tries to track a mobile target for a finite time. In order to ensure tracking guarantees for the observer, we formulate the problem as a finite-horizon zero-sum game between the observer and the target. First, we use optimal control theory in conjunction with geometric techniques to solve the problem around a corner. We show that the solution to the optimal control problem for both players is in Nash equilibrium. Next, we partition the visibility polygon of the pursuer and evader based on the winner of the game. These are the projections of the escape set and capture set on the workspace. Finally, we use the partition to construct a region that bounds the set of initial positions of the observer from which it can track the target in an environment containing multiple obstacles for the prespecified time horizon.

Path planning algorithms for source seeking agents

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.