9 research outputs found
Particle Swarm Optimization Based Source Seeking
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 forward. Since physical robots are responsive to swarm
position updates, modifications were required to implement the interaction
between real robots and the PSO algorithm. The development of modifications
necessary to implement PSO on mobile robots, and strategies to adapt to real
life environments such as obstacles and collision objects are presented in this
paper. Our findings are also validated using experimental testbeds.Comment: 13 pages, 12 figure
RF-Based Simultaneous Localization and Source Seeking for Multi-Robot Systems
This paper considers a radio-frequency (RF)-based simultaneous localization
and source-seeking (SLASS) problem in multi-robot systems, where multiple
robots jointly localize themselves and an RF source using distance-only
measurements extracted from RF signals and then control themselves to approach
the source. We design a Rao-Blackwellized particle filter-based algorithm to
realize the joint localization of the robots and the source. We also devise an
information-theoretic control policy for the robots to approach the source. In
our control policy, we maximize the predicted mutual information between the
source position and the distance measurements, conditioned on the robot
positions, to incorporate the robot localization uncertainties. A projected
gradient ascent method is adopted to solve the mutual information maximization
problem. Simulation results show that the proposed SLASS framework outperforms
two benchmarks in terms of the root mean square error (RMSE) of the estimated
source position and the decline of the distances between the robots and the
source, indicating more effective approaching of the robots to the source
Impact of initialization of a modified particle swarm optimization on cooperative source searching
Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization
Metacognitive Decision Making Framework for Multi-UAV Target Search Without Communication
This paper presents a new Metacognitive Decision Making (MDM) framework
inspired by human-like metacognitive principles. The MDM framework is
incorporated in unmanned aerial vehicles (UAVs) deployed for decentralized
stochastic search without communication for detecting stationary targets
(fixed/sudden pop-up) and dynamic targets. The UAVs are equipped with multiple
sensors (varying sensing capability) and search for targets in a largely
unknown area. The MDM framework consists of a metacognitive component and a
self-cognitive component. The metacognitive component helps to self-regulate
the search with multiple sensors addressing the issues of
"which-sensor-to-use", "when-to-switch-sensor", and "how-to-search". Each
sensor possesses inverse characteristics for the sensing attributes like
sensing range and accuracy. Based on the information gathered by multiple
sensors carried by each UAV, the self-cognitive component regulates different
levels of stochastic search and switching levels for effective searching. The
lower levels of search aim to localize the search space for the possible
presence of a target (detection) with different sensors. The highest level of a
search exploits the search space for target confirmation using the sensor with
the highest accuracy among all sensors. The performance of the MDM framework
with two sensors having low accuracy with wide range sensor for detection and
increased accuracy with low range sensor for confirmation is evaluated through
Monte-Carlo simulations and compared with six multi-UAV stochastic search
algorithms (three self-cognitive searches and three self and social-cognitive
based search). The results indicate that the MDM framework is efficient in
detecting and confirming targets in an unknown environment.Comment: 12 pages, 9 figures, 9 table
Intelligent Robotics Navigation System: Problems, Methods, and Algorithm
This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments
Motion Control of Automated Mobile Robots in Dynamic Environment
Autonomous mobile robot navigation had been a challenge for the researches from decades. Control and navigation of mobile robot in unknown environment is burning topic in the field of robotics. Several researchers have done lot of contribution in robot navigation problem. A general robot navigation problem includes features like obstacle detection and avoidance, smooth travel and reaching of a particular goal position. Among these aspects the obstacle avoidance part is of paramount importance in robot navigation problem. The robot will avoid the collision with objects if it has the ability to sense obstacle, take decision and move away from obstacle that means the robot should be intelligent and Intelligence can be achieved through programming. Here the main goal is to design and develop multiple intelligent mobile robots for autonomous navigation in unknown dynamic environment. Deployment of multiple mobile robots in unknown environment is worthwhile compared to single mobile robot. At the same time it will add more complexity and difficulty in controlling all the mobile robots. Multi-robot cooperation has lot of implications like target seeking, search and rescue, and disaster control. The obstacle avoidance issue of multiple mobile robots in unknown dynamic environment is addressed in this paper. For better motion control and obstacle avoidance PSO algorithm is used. In future goal seeking task of the mobile robots will be performed
Swarm Robotics
Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties
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.This is a manuscript of an article published as Zou, Rui, Vijay Kalivarapu, Eliot Winer, James Oliver, and Sourabh Bhattacharya. "Particle swarm optimization-based source seeking." IEEE Transactions on Automation Science and Engineering 12, no. 3 (2015): 865-875. doi: 10.1109/TASE.2015.2441746. Posted with permission.</p