Human Swarm Interaction for Radiation Source Search and Localization

This study shows that appropriate human interaction can benefit a swarm of robots to achieve goals more efficiently. A set of desirable features for human swarm interaction is identified based on the principles of swarm robotics. Human swarm interaction architecture is then proposed that has all of the desirable features. A swarm simulation environment is created that allows simulating a swarm behavior in an indoor environment. The swarm behavior and the results of user interaction are studied by considering radiation source search and localization application of the swarm. Particle swarm optimization algorithm is slightly modified to enable the swarm to autonomously explore the indoor environment for radiation source search and localization. The emergence of intelligence is observed that enables the swarm to locate the radiation source completely on its own. Proposed human swarm interaction is then integrated in a simulation environment and user evaluation experiments are conducted. Participants are introduced to the interaction tool and asked to deploy the swarm to complete the missions. The performance comparison of the user guided swarm to that of the autonomous swarm shows that the interaction interface is fairly easy to learn and that user guided swarm is more efficient in achieving the goals. The results clearly indicate that the proposed interaction helped the swarm achieve emergence

Optimisasi Mobile Robot Pendeteksi Sumber Gas Menggunakan Metode Hybrid

Mendeteksi kebocoran gas sejak awal dengan menggunakan mobile robot berguna agar kebocoran gas tersebut dapat segera ditanggulangi. Sebuah navigasi yang baik diperlukan dari mobile robot untuk membentuk sebuah sistem yang handal. Pada penelitian ini akan dibahas mengenai ulasan dari beberapa metode yang pernah digunakan pada navigasi penciuman mobile robot dalam melokalisasi sumber kebocoran gas. Seperti dengan melakukan pemetaan pada jalur robot, atau menggunakan metode logika fuzzy yang berfungsi sebagai pengendali yang kemudian dikombinasikan dengan metode PSO (Particle Swarm Optimization) sebagai optimisasi mobile robot dalam menemukan target, sehingga pada hasil akhir dari penelitian ini menunjukkan bahwa metode – metode yang akan digunakan pada mobile robot dalam melokalisasi mampu mendeteksi sumber kebocoran dan mendekati sumber gas dengan baik, serta mobile robot juga dapat menghindari halangan – halangan yang berkemungkinan muncul di sepanjang jalur lintasan

Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET)

This paper describes the swarm robots communication and control base Mobile ad-hoc network (MANET). MANET is a source of codes which migrate the network, collects and exchanges information of network nodes. In this work, the communication networks, which do not rely on fixed, preinstalled communication devices like base stations or predefine communication cells. Communications standards are considered in this work use the ad-hoc network such as Wireless LAN, X-Bee/Zig-Bee and Internet platform. All standards are integrated on swarm robots for real experiments. For finding the target, Particle swarm optimization (PSO) algorithm is proposed to control the real swarm robots communication in unknown experiment. As a results swarm robots-base MANET use PSO algorithm produce past response to find the target and swarm robots can move in the group without collision

Swarm Robots Communication-base Mobile Ad-Hoc Network (MANET)

This paper describes the swarm robots communication and control base Mobile ad-hoc network (MANET). MANET is a source of codes which migrate the network, collects and exchanges information of network nodes. In this work, the communication networks, which do not rely on fixed, preinstalled communication devices like base stations or predefine communication cells. Communications standards are considered in this work use the ad-hoc network such as Wireless LAN, X-Bee/Zig-Bee and Internet platform. All standards are integrated on swarm robots for real experiments. For finding the target, Particle swarm optimization (PSO) algorithm is proposed to control the real swarm robots communication in unknown experiment. As a results swarm robots-base MANET use PSO algorithm produce past response to find the target and swarm robots can move in the group without collision

Scalar source tracking in turbulent environments using deep reinforcement learning

Preprin

Context-dependent interaction leads to emergent search behavior in social aggregates

Locating the source of an advected chemical signal is a common challenge facing many living organisms. When the advecting medium is characterized by either high Reynolds number or high Peclet number the task becomes highly non-trivial due to the generation of heterogenous, dynamically changing filamental concentrations which do not decrease monotonically with distance to the source. Defining search strategies which are effective in these environments has important implications for the understanding of animal behavior and for the design of biologically inspired technology. Here we present a strategy which is able to solve this task without the higher intelligence required to assess spatial gradient direction, measure the diffusive properties of the flow field or perform complex calculations. Instead our method is based on the collective behavior of autonomous individuals following simple social interaction rules which are modified according to the local conditions they are experiencing. Through these context-dependent interactions the group is able to locate the source of a chemical signal and in doing so displays an awareness of the environment not present at the individual level. Our model demonstrates the ability of decentralized information processing systems to solve real world problems and also illustrates an alternative pathway to the evolution of higher cognitive capacity via the emergent, group level intelligence which can result from local interactions.Comment: 3 figure

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

Optimisasi Mobile Robot Pendeteksi Sumber Gas Menggunakan Metode Hybrid

Mendeteksi kebocoran gas sejak awal dengan menggunakan mobile robot berguna agar kebocoran gas tersebut dapat segera ditanggulangi. Sebuah navigasi yang baik diperlukan dari mobile robot untuk membentuk sebuah sistem yang handal. Pada penelitian ini akan dibahas mengenai ulasan dari beberapa metode yang pernah digunakan pada navigasi penciuman mobile robot dalam melokalisasi sumber kebocoran gas. Seperti dengan melakukan pemetaan pada jalur robot, atau menggunakan metode logika fuzzy yang berfungsi sebagai pengendali yang kemudian dikombinasikan dengan metode PSO (Particle Swarm Optimization) sebagai optimisasi mobile robot dalam menemukan target, sehingga pada hasil akhir dari penelitian ini menunjukkan bahwa metode – metode yang akan digunakan pada mobile robot dalam melokalisasi mampu mendeteksi sumber kebocoran dan mendekati sumber gas dengan baik, serta mobile robot juga dapat menghindari halangan – halangan yang berkemungkinan muncul di sepanjang jalur lintasan

Chemical Plume Tracing by Discrete Fourier Analysis and Particle Swarm Optimization

A novel methodology for solving the chemical plume tracing problem that utilizes data from a network of stationary sensors has been developed in this study. During a toxic chemical release and dispersion incident, the imperative need of first responders is to determine the physical location of the source of chemical release in the shortest possible time. However, the chemical plume that develops from the source of release may evolve into a highly complex distribution over the entire contaminated region, making chemical plume tracing one of the most challenging problems known to date. In this study, the discrete Fourier series method was applied for re-construction of the contour map representing the concentration distribution of chemical over the contaminated region based on point measurements by sensors in a pre-installed network. Particle Swarm Optimization was then applied to the re-constructed contour map to locate the position of maximal concentration. Such a methodology was found to be highly successful in solving the chemical plume tracing problem via the sensor network approach and thus closes a long-standing gap in the literature. Furthermore, the nature of the methodology is such that a visual of the entire chemical dispersion process is made available during the solution process and this can be beneficial for warning purposes and evacuation planning. In the context of such chemical release scenarios, the algorithm developed in this study is believed to be able to play an instrumental role towards national defense for any country in the world that is subjected to such threats