Jan Carlo Barca

Swarm robotics reviewed

SUMMARY We present a review of recent activities in swarm robotic research, and analyse existing literature in the field to determine how to get closer to a practical swarm robotic system for real world applications. We begin with a discussion of the importance of swarm robotics by illustrating the wide applicability of robot swarms in various tasks. Then a brief overview of various robotic devices that can be incorporated into swarm robotic systems is presented. We identify and describe the challenges that should be resolved when designing swarm robotic systems for real world applications. Finally, we provide a summary of a series of issues that should be addressed to overcome these challenges, and propose directions for future swarm robotic research based on our extensive analysis of the reviewed literature.

Search and tracking algorithms for swarms of robots

Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. Surveys algorithms applicable to swarm robotic systems for target search and tracking.Identifies variations of the search and tracking problem addressed in the literature.Discusses desired capabilities of search and tracking algorithms for robot swarms.

Exploring sensor gloves for teaching children sign language

This research investigates if a computer and an alternative input device in the form of sensor gloves can be used in the process of teaching children sign language. The presented work is important, because no current literature investigates how sensor gloves can be used to assist children in the process of learning sign language. The research presented in this paper has been conducted by assembling hardware into sensor gloves, and by designing software capable of (i) filtering out sensor noise, (ii) detecting intentionally posed signs, and (iii) correctly evaluating signals in signs posed by different children. Findings show that the devised technology can form the basis of a tool that teaches children sign language, and that there is a potential for further research in this area.

A Modified K-means Algorithm for Noise Reduction in Optical Motion Capture Data

This paper presents a modified K-means algorithm that can be used for removing noise in multicolor motion capture image sequences. These images have been produced using the illuminated line segment based marker system. The proposed algorithm takes into account the nature of the motion capture images in terms of the number of data pixels normally clustered together and the acceptable degree of compactness of a data cluster. The cleaned data can be used for accurate and effective tracking of the captured motion.

Controlling Formations of Robots with Graph Theory

A number of techniques that allow autonomous multi-robot systems to be held within formation-like structures exist but they are limited by a high communication load, high energy usage and a lack of robustness. This research improves on state-of-the-art formation control schemes for leader-follower type multi-robot systems by employing mechanisms that enable groups of robots to move in two-dimensional formations without the need for inter robot communication. We also incorporate techniques that enable the robots to move back into formation in a precise manner when external interferences have caused the formation shape to deteriorate. The control system is derived through the use of graph theory and has been tested rigorously in a realistic simulator to prove its applicability to multi-robot control.

A Concept for Optimizing Behavioural Effectiveness & Efficiency

Both humans and machines exhibit strengths and weaknesses that can be enhanced by merging the two entities. This research aims to provide a broader understanding of how closer interactions between these two entities can facilitate more optimal goal-directed performance through the use of artificial extensions of the human body. Such extensions may assist us in adapting to and manipulating our environments in a more effective way than any system known today. To demonstrate this concept, we have developed a simulation where a semi interactive virtual spider can be navigated through an environment consisting of several obstacles and a virtual predator capable of killing the spider. The virtual spider can be navigated through the use of three different control systems that can be used to assist in optimising overall goal directed performance. The first two control systems use, an onscreen button interface and a touch sensor, respectively to facilitate human navigation of the spider. The third control system is an autonomous navigation system through the use of machine intelligence embedded in the spider. This system enables the spider to navigate and react to changes in its local environment. The results of this study indicate that machines should be allowed to override human control in order to maximise the benefits of collaboration between man and machine. This research further indicates that the development of strong machine intelligence, sensor systems that engage all human senses, extra sensory input systems, physical remote manipulators, multiple intelligent extensions of the human body, as well as a tighter symbiosis between man and machine, can support an upgrade of the human form.

Distributed formation control of networked mobile robots in environments with obstacles

A distributed control mechanism for ground moving nonholonomic robots is proposed. It enables a group of mobile robots to autonomously manage formation shapes while navigating through environments with obstacles. The formation can be maintained without the need of any inter-robot communication. Obstacle avoidance is designed to be performed by the individual robots themselves. Formation scaling is implemented to ensure the formation shape is maintained for as long as possible. If the formation fails to hold its shape when navigating through environments with obstacles, formation morphing has been incorporated to preserve the interconnectivity of the robots, thus reducing the possibility of losing robots from the formation. The algorithm has been implemented on a nonholonomic multi-robot system for empirical analysis. Experimental results demonstrate formations completing an obstacle course within 12 seconds with zero collisions. Furthermore, the system is capable of withstanding up to 25% sensor noise.

Distributed formation control in cluttered environments

A graph theory based control mechanism that enables groups of ground moving nonholonomic robots is proposed. The mechanism allows the robot to dynamically manage formation shapes and follow the leader through environments with obstacles. It improves upon a state of the art formation control algorithm where a formation can be maintained without the need of inter-robot communications. Obstacle avoidance is designed to be scalable and allows the robots to dynamically manage their formation according to the environment. The formation is also capable of rebuilding itself when individual robots within the formation fail. The algorithm has been tested on a nonholonomic multi-robot system, with results showing that the proposed algorithm enables a formation to complete an obstacle course and regenerate original formation shapes within 12 seconds with no collisions.

Augmenting the Human Entity Through Man/Machine Collaboration

Both humans and machines exhibit strengths and weaknesses that can be enhanced by merging the two separate entities. This research aims to provide a broader understanding of how closer interaction between these two entities can facilitate more optimal goal related performance through the use of artificial extension of the human body. Such extension may assist us in adapting to and manipulate our environments in a more effective way than any organism observed to day. To demonstrate this concept, the researchers have developed a simulation where a semi interactive virtual spider can be navigated through an environment that includes several obstacles and a virtual predator capable of killing the spider. The virtual spider in turn, can be navigated through the use of three different control systems that when combined optimises overall goal related performance. An onscreen button interface and a touch sensor facilitate human navigation of the spider, while a third control system is based on automatic navigation through the use of Classical AI. The last mentioned control system enables the spider to navigate and react to changes in its local environment with the benefits provided by machine intelligence. The results of this study indicate that machines should be allowed to disobey human control, to obtain optimal benefits of man machine collaboration. This research further indicates that the development of; Strong AI; sensor systems that engage all human senses; extra sensory input systems; physical remote manipulators; intelligent extension of the human body; and a tighter symbiosis between man and machine can support an upgrade of the human form.

Autonomous formation selection for ground moving multi-robot systems

Multi-robot systems have many useful real world applications including disaster management, exploration and surveying. Formation control is critical in these contexts as the success of groups often depend on the ability to generate and maintain particular formation shapes. It is also important that a multi-robot system can evaluate and select appropriate alternative formations when an ideal formation cannot be upheld, particularly in dynamic real world scenarios. A distributed formation selection mechanism that addresses these issues by enabling groups of unmanned ground vehicles to autonomously select, scale and morph formation shapes when navigating through dynamic environments is presented in this paper. Experiments on non-holonomic ground moving robots demonstrate the suitability of the proposed technology.