Papers by Aleksandar Jevtic
Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant c... more Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant colonies in nature. Like their biological counterparts, a colony of artificial ants is able to adapt to the changes in their environment, such as exhaustion of a food source and discovery of a new one. In this paper, one of the basic ACO algorithms, the Ant System algorithm, was applied for edge detection where the edge pixels represent food for the ants. A set of grayscale images obtained by a nonlinear contrast enhancement technique called Multiscale Adaptive Gain is used to create a variable environment. As the images change, the ant colony adapts to those changes leaving pheromone trails where the new edges appear while the pheromone trails that are not reinforced evaporate over time. Although the images were used to create an environmental setup in which the ants move, the colony's adaptive behavior could be demonstrated on any type of digital habitat.
Robotics and Autonomous Systems, 2015
This paper presents the interactive robotics concept being developed by the INTRO research networ... more This paper presents the interactive robotics concept being developed by the INTRO research network. The aim is to create a new generation of intelligent mobile robots that operate in close interaction with humans in unstructured, dynamically changing environments. The INTRO network consists of a team of researchers, from academia and industry, which create a multidisciplinary framework that entails Cooperative Robot Learning, Cognitive Human-Robot Interaction (HRI), and Intelligent Interface Design. The robotic system being developed will be tested in two application scenarios: the Robot Waiter, and the Urban Search and Rescue (USAR). For these scenarios, two different robotic platforms are used in the implementation stage. This paper presents an overview of the obtained research objectives, and proposes a framework for the integration of work and the implementation of the expected results. Finally, the paper describes a potential impact through development and use of research results and proposes future lines of research.
In this paper, a novel edge detection method that computes image gradient using the concept of Ce... more In this paper, a novel edge detection method that computes image gradient using the concept of Center of Mass (COM) is presented. The algorithm runs with a constant number of operations per pixel independently from its scale by using integral image. Compared with the conventional convolutional edge detector such as Sobel edge detector, the proposed method performs faster when region size is larger than 9×9. The proposed method can be used as framework for multi-scale edge detectors when the goal is to achieve fast performance. Experimental results show that edge detection by COM is competent with Canny edge detection.
Robotics and Autonomous Systems, 2015
Concepts, Methodologies, Tools, and Applications, 2014
This chapter introduces a swarm intelligence-inspired approach for target allocation in large tea... more This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm's performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA's control parameters by means of a genetic algorithm. rescue, communication networks, monitoring, surveillance, cleaning, maintenance, and so forth. In order to efficiently perform their tasks, robots require high level of autonomy and cooperation. They use their sensing abilities to explore an unknown environment and deploy on the sites of interest, i.e. targets. However, the coordination of a robot swarm is not an easy problem, especially when the resources for the deployment task are limited. Such a large group of robots, if organized in a centralized manner, could experience information overflow that can lead to the overall system failure . For this reason, the communication between the robots can be realized through local interactions, either directly with one another or indirectly via environment .
This chapter introduces a swarm intelligence-inspired approach for target allocation in large tea... more This chapter introduces a swarm intelligence-inspired approach for target allocation in large teams of autonomous robots. For this purpose, the Distributed Bees Algorithm (DBA) was proposed and developed by the authors. The algorithm allows decentralized decision-making by the robots based on the locally available information, which is an inherent feature of animal swarms in nature. The algorithm's performance was validated on physical robots. Moreover, a swarm simulator was developed to test the scalability of larger swarms in terms of number of robots and number of targets in the robot arena. Finally, improved target allocation in terms of deployment cost efficiency, measured as the average distance traveled by the robots, was achieved through optimization of the DBA's control parameters by means of a genetic algorithm.
IEEE Systems Journal, 2012
In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of ... more In this paper, we propose the distributed bees algorithm (DBA) for task allocation in a swarm of robots. In the proposed scenario, task allocation consists in assigning the robots to the found targets in a 2-D arena. The expected distribution is obtained from the targets' qualities that are represented as scalar values. Decision-making mechanism is distributed and robots autonomously choose their assignments taking into account targets' qualities and distances. We tested the scalability of the proposed DBA algorithm in terms of number of robots and number of targets. For that, the experiments were performed in the simulator for various sets of parameters, including number of robots, number of targets, and targets' utilities. Control parameters inherent to DBA were tuned to test how they affect the final robot distribution. The simulation results show that by increasing the robot swarm size, the distribution error decreased.
Sensors, 2011
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on si... more Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA's control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots' distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce. Even though cheap robot hardware has become widely accessible on the market, application of multi-robot systems in our everyday lives is limited. Nevertheless, due to the potential that this field has, great efforts have been made by various research groups to investigate the algorithms for coordination and control of multi-robot systems consisting of large number of units. In order to unify the research under a single framework, some researchers have proposed different multi-robot system taxonomies. Dudek et al.
Three advanced natural interaction modalities for mobile robot guidance in an indoor environment ... more Three advanced natural interaction modalities for mobile robot guidance in an indoor environment were developed and compared using two tasks and quantitative metrics to measure performance and workload. The first interaction modality is based on direct physical interaction requiring the human user to push the robot in order to displace it. The second and third interaction modalities exploit a 3-D vision-based human skeleton tracking allowing the user to guide the robot by either walking in front of it or by pointing towards a desired location. In the first task, the participants were asked to guide the robot between different rooms in a simulated physical apartment requiring rough movement of the robot through designated areas. The second task evaluated robot guidance in the same environment through a set of waypoints, which required accurate movements. The three interaction modalities were implemented on a generic differential drive mobile platform equipped with a pan-tilt system and a Kinect camera. Task completion time and accuracy were used as metrics to assess the users’ performance, while the NASA-TLX questionnaire was used to evaluate the users’ workload. A study with 24 participants indicated that choice of interaction modality had significant effect on completion time (F(2,61)=84.874, p<0.001), accuracy (F(2,29)=4.937, p=0.016), and workload (F(2,68)=11.948, p<0.001). The direct physical interaction required less time, provided more accuracy and less workload than the two contactless interaction modalities. Between the two contactless interaction modalities, the person-following interaction modality was systematically better than the pointing-control one: the participants completed the tasks faster with less workload.
IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, 2010
Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant c... more Ant Colony Optimization (ACO) is a group of algorithms inspired by the foraging behavior of ant colonies in nature. Like their biological counterparts, a colony of artificial ants is able to adapt to the changes in their environment, such as exhaustion of a food source and discovery of a new one. In this paper, one of the basic ACO algorithms, the Ant System algorithm, was applied for edge detection where the edge pixels represent food for the ants. A set of grayscale images obtained by a nonlinear contrast enhancement technique called Multiscale Adaptive Gain is used to create a variable environment. As the images change, the ant colony adapts to those changes leaving pheromone trails where the new edges appear while the pheromone trails that are not reinforced evaporate over time. Although the images were used to create an environmental setup in which the ants move, the colony's adaptive behavior could be demonstrated on any type of digital habitat.
2009 IEEE International Conference on Systems, Man and Cybernetics, 2009
In this paper, Ant Colony System (ACS) algorithm is applied for edge detection in grayscale image... more In this paper, Ant Colony System (ACS) algorithm is applied for edge detection in grayscale images. The novelty of the proposed method is to extract a set of images from the original grayscale image using Multiscale Adaptive Gain for image contrast enhancement and then apply the ACS algorithm to detect the edges on each of the extracted images. The resulting set of images represents the pheromone trails matrices which are summed to produce the output image. The image contrast enhancement makes ACS algorithm more effective when accumulating pheromone trails on the true edge pixels. The results of the experiments are presented to confirm the effectiveness of the proposed method.
Lecture Notes in Computer Science, 2007
2010 IEEE International Systems Conference, 2010
This paper describes the use of networked control algorithms in designing a robotic swarm. The ma... more This paper describes the use of networked control algorithms in designing a robotic swarm. The main goal of a robotic swarm is to divide one task into multiple simpler tasks. Have we designed a swarm this way, the main challenge would be the problem of delay in communication between individual robots. This paper also goes through the Swarm Intelligence concept and proposes the Network Formation Control algorithms to control a group of robots.
2010 5th International Conference on System of Systems Engineering, 2010
Unmanned Aerial Vehicle (UAV) is defined as aircraft without the onboard presence of pilots. UAVs... more Unmanned Aerial Vehicle (UAV) is defined as aircraft without the onboard presence of pilots. UAVs have been used to perform intelligence, surveillance, and reconnaissance missions. The UAVs are not limited to military operations, they can also be used in commercial applications such as telecommunications, ground traffic control, search and rescue operations, crop monitoring, etc. In this paper, we propose a swarm intelligence-based method for UAVs' route optimization. The team of UAVs is used for area coverage with the defined set of waypoints. The problem can be interpreted as a well-known Traveling Salesman Problem where the task is to find the route of minimal length such that all the waypoints are visited only once. We applied the Ant System algorithm and compared it with the Nearest Neighbor Search. The experimental results confirm the effectiveness of our method, especially for a large number of waypoints.
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Papers by Aleksandar Jevtic