Showing papers on "Ant robotics published in 2011"

Self-organized cooperation between robotic swarms

Abstract: We study self-organized cooperation between heterogeneous robotic swarms. The robots of each swarm play distinct roles based on their different characteristics. We investigate how the use of simple local interactions between the robots of the different swarms can let the swarms cooperate in order to solve complex tasks. We focus on an indoor navigation task, in which we use a swarm of wheeled robots, called foot-bots, and a swarm of flying robots that can attach to the ceiling, called eye-bots. The task of the foot-bots is to move back and forth between a source and a target location. The role of the eye-bots is to guide foot-bots: they choose positions at the ceiling and from there give local directional instructions to foot-bots passing by. To obtain efficient paths for foot-bot navigation, eye-bots need on the one hand to choose good positions and on the other hand learn the right instructions to give. We investigate each of these aspects. Our solution is based on a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt their position and the instructions they give. Our approach is inspired by pheromone mediated navigation of ants, as eye-bots serve as stigmergic markers for foot-bot navigation. Through simulation, we show how this system is able to find efficient paths in complex environments, and to display different kinds of complex and scalable self-organized behaviors, such as shortest path finding and automatic traffic spreading.

Self-organised path formation in a swarm of robots

Abstract: In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.

Task partitioning in swarms of robots: an adaptive method for strategy selection

Abstract: Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals’ skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.

Engineering the evolution of self-organizing behaviors in swarm robotics: A case study

Abstract: Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.

A PSO-based algorithm designed for a swarm of mobile robots

Abstract: This paper presents an algorithm called augmented Lagrangian particle swarm optimization with velocity limits (VL-ALPSO). It uses a particle swarm optimization (PSO) based algorithm to optimize the motion planning for swarm mobile robots. Considering problems with engineering constraints and obstacles in the environment, the algorithm combines the method of augmented Lagrangian multipliers and strategies of velocity limits and virtual detectors so as to ensure enforcement of constraints, obstacle avoidance and mutual avoidance. All the strategies together with basic PSO are corresponding to real situations of swarm mobile robots in coordinated movements. This work also builds a swarm motion model based on Euler forward time integration that involves some mechanical properties such as masses, inertias or external forces to the swarm robotic system. Simulations show that the robots moving in the environment display the desired behavior. Each robot has the ability to do target searching, obstacle avoidance, random wonder, acceleration or deceleration and escape entrapment. So, in summary due to the characteristic features of the VL-ALPSO algorithm, after some engineering adaptation, it can work well for the planning of coordinated movements of swarm robotic systems.

Cooperative Motion of Swarm Mobile Robots Based on Particle Swarm Optimization and Multibody System Dynamics

Abstract: This paper addresses the problem of cooperative motion of a swarm mobile robotic system with the purpose of searching a target in a complicated environment. The solution is inspired from Particle Swarm Optimization (PSO) and combined with multibody system dynamics which also includes the consideration of robots' physical properties like mass, inertia, force, acceleration, etc. The entire robot swarm is mainly guided by this physical PSO and an independent obstacle avoidance module is active when robots encounter any conflicts during missions. This paper considers an artificial swarm mobile robot system to perform searching tasks and each member of the system may interact with its neighbors or the environment by limited local communication ability. Several groups of simulations are set up for the verification of the strategy and the results show that this method creates the desired behavior well. The simulation experiments also investigate the feature of fault tolerance of this strategy. Finally, a framewo...

A path planning method based on cellular automata for cooperative robots

Abstract: A Cellular Automaton-based technique suitable for solving the path planning problem in a distributed robot team is outlined. Real-time path planning is a challenging task that has many applications in the fields of artificial intelligence, moving robots, virtual reality, and agent behavior simulation. The problem refers to finding a collision-free path for autonomous robots between two specified positions in a configuration area. The complexity of the problem increases in systems of multiple robots. More specifically, some distance should be covered by each robot in an unknown environment, avoiding obstacles found on its route to the destination. On the other hand, all robots must adjust their actions in order to keep their initial team formation immutable. Two different formations were tested in order to study the efficiency and the flexibility of the proposed method. Using different formations, the proposed technique could find applications to image processing tasks, swarm intelligence, etc. Furthermore, the presented Cellular Automaton (CA) method was implemented and tested in a real system using three autonomous mobile minirobots called E-pucks. Experimental results indicate that accurate collision-free paths could be created with low computational cost. Additionally, cooperation tasks could be achieved using minimal hardware resources, even in systems with low-cost robots.

Bee-inspired foraging in an embodied swarm

Abstract: We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation of environmental parameters (e.g. pheromones).

Swarm Robots

Abstract: Inspired by the swarm behaviours of social insects, research into the self-assembly of swarm robots has become an attractive issue in the robotic community. Unfortunately, there are very few platforms for self-assembly and locomotion in the field of swarm robotics. The Sambot is a novel self-assembling modular robot that shares characteristics with swarm robots and self-reconfigurable robots. Each Sambot can move autonomously and connect with the other. This paper discusses the concept of combining self-assembly and locomotion for swarm robots. Distributed control algorithms for self-assembly and locomotion are proposed. Using five physical Sambots, experiments were carried out on autonomous docking, self-assembly and locomotion. Our control algorithm for self-assembly can also be used to realize the autonomous construction and self-repair of robotic structures consisting of a large number of Sambots.

A serialization algorithm for mobile robots using mobile agents with distributed ant colony clustering

Abstract: This paper presents effective extensions of our previously proposed algorithm for controlling multiple robots. The robots are connected by communication networks, and the controlling algorithm is based on a specific Ant Colony Clustering (ACC) algorithm. In traditional ACC, imaginary ants convey imaginary objects for classifying them based on some similarities, but in our algorithm, we implemented the ants as actual mobile software agents that control the mobile robots which are corresponding to objects. The ant agent as a software agent guides the mobile robot (object) to which direction it should move. In the previous approach, we implemented not only the ant but also the pheromone as mobile software agents to assemble the mobile robots with as little energy consumption as possible. In our new approach, we take advantage of the pheromone agents not only to assemble the robots but also to serialize them. The serializing property is desirable for particular applications such as gathering carts in airports. We achieve the property by allowing each ant agent to alternatively receive a pheromone agent. We have built a simulator based on our algorithm, and conducted numerical experiments to demonstrate the feasibility of our approach. The experimental results show the effectiveness of our algorithm.

Ant Colony Optimization : A Technique used for Image Processing

Abstract: Ant colony optimization (ACO) is a technique which can be used for various applications. Ant colony Optimization is an optimization technique that is based on the foraging behaviour of real ant colonies. Ant colony optimization is applied for the image processing which are on the basis continuous optimization. This paper proposes an ant colony optimization (ACO) based algorithm for continuous optimization problems on images like image edge detection, image compression, image segmentation, structural damage monitoring etc in image processing .This paper represents that how ACO is applied for various applications in image processing. The algorithm can find the optimal solution for problem. The results show feasibility of the algorithm in terms of accuracy and continuous optimization. Ant Colony Optimization: The ant colony optimization algorithm (ACO) is a probabilistic technique for solving many problems which can be reduced to finding good paths through graphs. Although real ants are blind, they are capable of finding shortest path from food source to their nest by exploiting a liquid substance, called pheromone, which they release on the transit route (1). This algorithm is a member of ant colony algorithms family, in swarm intelligence methods, and it constitutes some met heuristic optimizations. Ant Colony Optimization (ACO) is a population-based, general search technique for the solution of complex continuous problems which is inspired by the pheromone track laying behaviour of real ant colonies. The behaviour of ant is intimidated in artificial ant colonies for the search of estimated solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was aiming to search for an optimal path in a graph, based on the behaviour of ants looking for a path between their colony and a source of food. The ant colony optimization (ACO) meta- heuristic a colony of artificial ants assists in finding good solutions to difficult discrete optimization problems (2). The choice is to allocate the computational resources to a set of relatively simple agents (artificial ants) that communicate indirectly by stigmergy. Good solutions are an emergent property of the agents' cooperative interaction. The original idea has since diversified to solve a wider class of numerical problems, and as a result, several problems have emerged, drawing on various aspects of the behaviour of ants. The main underlying idea, loosely inspired by the behaviour of real ants, is that of a parallel search over several constructive computational threads based on local problem data and on a dynamic memory structure containing information on the quality of previously obtained result. The collective behaviour emerging from the interaction of the different search threads has proved effective in solving combinatorial optimization (CO) problems. The developed AS strategy attempts to simulate behaviour of real ants with the addition of several artificial characteristics: visibility, memory, and discrete time to resolve many complex problems successfully such as the travelling salesman problem (TSP) ,vehicle routing problem (VRP), and best path planning, Even though many changes have been applied to the ACO algorithms during the past years, their fundamental ant behavioural mechanism that is positive feedback process demonstrated by a colony of ants is still the same. Ant's algorithm has also plenty of networking applications such as in communication networks and electrical distribution networks. II. Ant Colony System Algorithm Different steps of a simple ant colony system algorithm are as follows.

Cooperative swarm control for multiple mobile robots using only information from PSD sensors

Abstract: Recently, many studies on swarm robotics have been conducted in which the aim seems to be the realization of an ability to perform complex tasks by cooperating with each other. Future progress and concrete applications are expected. The objective of this study was to construct a cooperative swarm system by using multiple mobile robots. First, multiple mobile robots with six position-sensitive detector (PSD) sensors were designed. A PSD sensor is a type of photo sensor. A control system was considered to realize swarm behavior, such as that shown by Ligia exotica, by using only information from the PSD sensors. Experimental results showed interesting behavior among the multiple mobile robots, such as following, avoidance, and schooling. The controller of the schooling mode was designed based on subsumption architecture. The proposed system was demonstrated to high school students at OPEN CAMPUS 2010, held in Tokyo University of Science, Yamaguchi.

A Robust Aggregation Method for Quasi-blind Robots in an Active Environment

Abstract: The aggregation of a swarm of autonomous agents into com- pact clusters is often a required behavior of multi-agent systems. In the case where no central control or coordination exists, this problem is known as the Decentralized Gathering. This presents a first step in the application of a bio-inspired aggregation scheme to aggregate robots whose abilities are very restricted: the only way they can communicate is through an active environment (stigmergy) and the only information they can receive is the local detection of the waves produced by other robots. The active environment obeys a cellular automaton rule and is simulated with a projection of light on the robots.

Analysis of various swarm-based & ant-based algorithms

Abstract: Ant algorithms and swarm intelligence systems have been offered as a novel computational approach that replaces the traditional emphasis on control, preprogramming and centralization with designs featuring autonomy, emergence and distributed functioning. These designs provide scalable, flexible and robust, able to adapt quickly changes to changing environments and to continue functioning even when individual elements fail. These properties make swarm intelligence very attractive for mobile ad hoc networks. These algorithms also provide potential advantages for conventional routing algorithms. Ant Colony Optimization is popular among other Swarm Intelligence Techniques.In this paper a detailed comparison of different Ant based algorithms is presented. The comparative results will help the researchers to understand the basic differences among various existing Ant colony based routing algorithms.

Self-Organized Cooperation in Swarm Robotics

Abstract: Cooperation is a key concept used in multi-robot systems for performing complex tasks. In swarm robotics, a self-organized cooperation is applied, where robots with limited intelligence cooperate and interact locally to build up the desired global behavior. In this paper, we are studying a mobile object tracking scenario performed by a swarm of robots. The robustness, scalability and flexibility of swarm robots make it an attractive approach for missions like object tracking in complex and dynamic environments. As the individual robot capabilities are limited in swarm systems, the robots may not be able to track the mobile object continuously. This limitation is overcome using the robots communication capability. In order to increase the probability of object detection, we propose a greedy self-deployment strategy, where the robots are spread uniformly in the environment to be monitored. For detecting a moving target, the robots use a biologically inspired algorithm for collecting robots currently located in other regions to track the target. In such cooperative tasks the robots normally need to be time synchronized for simultaneous activation. A new proposal for time synchronization in swarm robots is introduced which exploits the mobility of the robots for handling possible disconnections in the network and synchronize them at the beginning of tracking time slots.

Effect of sensor and actuator quality on robot swarm algorithm performance

Abstract: The performance of a swarm of robots depends on the hardware quality of the robots in the swarm. A swarm of robots with high-quality sensors and actuators is expected to out-perform a swarm of robots with low-quality sensors and actuators. This paper directly investigates the relationship between hardware quality and swarm performance. We take three common components of swarm algorithms (trail following, swarm expansion, and shape formation) and measure how they are affected by two common types of hardware inaccuracy (communication bearing reception error, and movement error) both in simulation and with E-Puck robots. We find that large amounts of both types of hardware error are required before performance appreciably decreases.

Intelligent navigation of multiple mobile robotsusing an ant colony optimization techniquein a highly cluttered environment

Abstract: This article presents the cooperative behaviour of a multi-robot system in which each robot has a very simple interaction method. In this article, some effort has been made to develop a theory of a...

Theory of swarm intelligence

Abstract: Social animals as found in fish schools, bird flocks, bee hives, and ant colonies are able to solve highly complex problems in nature. This includes foraging for food, constructing astonishingly complex nests, and evading or defending against predators. Remarkably, these animals in many cases use very simple, decentralized communication mechanisms that do not require a single leader. This makes the animals perform surprisingly well, even in dynamically changing environments. The collective intelligence of such animals is known as swarm intelligence and it has inspired popular and very powerful optimization paradigms, including ant colony optimization (ACO) and particle swarm optimization (PSO). The reasons behind their success are often elusive. We are just beginning to understand when and why swarm intelligence algorithms perform well, and how to use swarm intelligence most effectively. Understanding the fundamental working principles that determine their efficiency is a major challenge. This tutorial will give a comprehensive overview of recent theoretical results on swarm intelligence algorithms, with an emphasis on their efficiency (runtime/computational complexity). In particular, the tutorial will show how techniques for the analysis of evolutionary algorithms can be used to analyze swarm intelligence algorithms and how the performance of swarm intelligence algorithms compares to that of evolutionary algorithms. The results shed light on the working principles of swarm intelligence algorithms, identify the impact of parameters and other design choices on performance, and thus help to use swarm intelligence more effectively. The tutorial will be divided into a first, larger part on ACO and a second, smaller part on PSO. For ACO we will consider simple variants of the MAX-MIN ant system. Investigations of example functions in pseudo-Boolean optimization demonstrate that the choices of the pheromone update strategy and the evaporation rate have a drastic impact on the running time. We further consider the performance of ACO on illustrative problems from combinatorial optimization: constructing minimum spanning trees, solving shortest path problems with and without noise, and finding short tours for the TSP. For particle swarm optimization, the tutorial will cover results on PSO for pseudo-Boolean optimization as well as a discussion of theoretical results in continuous spaces.

Towards a Service-Oriented Architecture for Teams of Heterogeneous Autonomous Robots

Abstract: Developing an infrastructure for efficiently coordinating a group of autonomous robots has become a challenging need in robotics community. In recent years, the use of teams of coordinated mobile robots has been explored in several application fields such as military, exploration, surveillance, and search and rescue missions. For such fields, the use of multiple robots enable for robustness at mission accomplishment. In this paper we present a service-oriented, distributed architecture for coordinating a set of mobile robots on the way to a common goal. The main design aspects concern the ease of extendibility, scalability, re-usability and integration of rapidly changing robotic hardware and software, and the support of different application domains rather than limiting to specific tasks' requirements and algorithms. We integrate state of the art ideas such as Service-Oriented Robotics in order to achieve novel solutions. We demonstrate working cooperative autonomous operations using multiple robots with time-suitable communications. This work is an early phase of our ultimate goal, which is to have multiple heterogeneous autonomous robots forming an intelligent system that can be able to deal with highly dynamic and challenging environments such as a first responders team in search and rescue missions.

Implementation of a cue-based aggregation with a swarm robotic system

Abstract: This paper presents an aggregation behavior using a robot swarm. Swarm robotics takes inspiration from behaviors of social insects. BEECLUST is an aggregation control that is inspired from thermotactic behavior of young honeybees in producing clusters. In this study, aggregation method is implemented with a modification on original BEECLUST. Both aggregations are performed using real and simulated robots. We aim to demonstrate that, a simple change in control of individual robots results in significant changes in collective behavior of the swarm. In addition, the behavior of the swarm is modeled by a macroscopic modeling based on a probability control. The presented model in this study could depict the behavior of swarm throughout the performed scenarios with real and simulated robots.

The study of ZigBee technology's application in swarm robotics system

Abstract: With the development of robot technology, the traditional single robot can not do some tasks in complex environment. Although the concept of swarm robotics has bee put forward, but it is hard to implement the system. There are difficulties in coordination, communication and routing. In this article, ZigBee technology was introduced, and a design schema of the communication was proposed, which is to solve problems of communication among individual robots of swarm robotics system. It is suitable for swarm robotics system and can improve the efficiency of communication among robots.

Swarm Intelligence Optimization and Its Applications

Abstract: Swarm Intelligence is a computational and behavioral metaphor for solving distributed problems inspired from biological examples provided by social insects such as ants, termites, bees, and wasps and by swarm, herd, flock, and shoal phenomena in vertebrates such as fish shoals and bird flocks An example of successful research direction in Swarm Intelligence is ant colony optimization (ACO), which focuses on combinatorial optimization problems Ant algorithms can be viewed as multi-agent systems (ant colony), where agents (individual ants) solve required tasks through cooperation in the same way that ants create complex social behavior from the combined efforts of individuals

Collective self-detection scheme for adaptive error detection in a foraging swarm of robots

Abstract: In this paper we present a collective detection scheme using receptor density algorithm to self-detect certain types of failure in swarm robotic systems Key to any fault-tolerant system, is its ability to be robust to failure and have appropriate mechanisms to cope with a variety of such failures In this work we present an error detection scheme based on T-cell signalling in which robots in a swarm collaborate by exchanging information with respect to performance on a given task, and self-detect errors within an individual While this study is focused on deployment in a swarm robotic context, it is possible that our approach could possibly be generalized to a wider variety of multi-agent systems

Enabling Large-Scale Collective Systems in Outdoor Aerial Robotics

Abstract: For many real-life applications such as monitoring, mapping, search-and-rescue or ad-hoc communication networks, fleets of flying robots are expected to out-perform existing solutions. Robots can join forces to cover larger areas in less time, act as efficient communication relays and overcome difficult terrain more easily than ground-based robots. Compared to a single robot, the main advantages of collective systems are robustness, parallelism, flexibility, and the fact that they enable tasks that could not be solved by a single robot. While a multitude of theoretical models for collective operation have already been developed and tested in simulation, they have rarely been validated with physical robots yet. Transition to reality has so far been inhibited because of strong limitations in scalability. Indeed, the cost, safety risk, and number of operators associated with a collective aerial system increase proportionally with the number of robots. Substituting such systems for single robots is therefore unattractive or even impracticable. In previous experimental approaches, a maximum of five physical robots were operated simultaneously in outdoor scenarios, assisted by human backup pilots on the ground. In contrast, most theoretical models rely on a minimum of ten robots to obtain interesting swarm effects. With respect to this discrepancy, we consider here ten robots to be a large-scale system in aerial robotics. In order to scale real collective aerial systems to at least ten robots, we suggest the following paradigm: flying robots must be low-cost, inherently safe, deal with mid-air collisions and require minimal supervision from an operator on the ground, such that the entire system warrants similar cost as well as similarly safe and easy deployment as a single, classical flying robot. Moreover, this would make swarms of flying robots as accessible as wheeled robots that have already been used successfully in larger collective systems. The above criteria can best be addressed with a global, systematic approach on all major design levels, which are the robot's airframe, low-level control, collective supervision and control as well as mid-air collision avoidance. On each level, we identified the bottlenecks related to scaling and developed methods for compliant robot design. Based on a systematic analysis of airframe configurations, a flying-wing is proposed that is made of low-cost, safe and durable foam material and can be deployed everywhere by hand-launch. A model for the dimensioning of such an airframe is presented. For low-level control, a novel minimalist control strategy is proposed, implemented on an inexpensive, custom-made autopilot and validated in field tests. In order to enable robots for collective operation, we suggest to use WiFi communication links and embedded Linux-computers onto which swarm designers can download distributed controllers for collective behaviors directly from their computer simulation. Furthermore, the idea is put forward to enable collective supervision through an operator interface with the modality for direct group-control. Implemented and field-proven, these solutions can serve as guidelines for other developers. Finally, the problem that robots may collide with each other during collective operation has been addressed with a model assessing the collision risk and a distributed strategy for mid-air collision avoidance, validated on real robots. Applying the proposed methodology allowed us to demonstrate the world's first collective system composed of ten autonomous flying robots in outdoor experiments with several different collective behaviors. Robots are low-cost (about 1/10 the cost of commercially available robots), inherently safe (kinetic energy of a medium-sized bird) and can be configured, deployed and supervised by a single operator on the ground. This represents a significant step for the scaling of flying collective systems with respect to the state of the art as well as an unprecedented operator-to-robot ratio.

Towards the development of self-ant systems

Abstract: We propose a computational framework for the self-generation of components used by an Ant Colony Optimization algorithm. The approach relies on Strongly Typed Genetic Programming to automatically seek for effective update pheromone strategies. Best evolved strategies are then inserted in an Ant Colony Algorithm used to find good quality solutions for the Quadratic Assignment Problem. Results reveal that evolved update rules are competitive with human designed variants and can be effectively reused on different instances of the same problem. Moreover, we investigate the possibility of evolving general strategies that can be used across different optimization problems.