940 research outputs found

    Modular Robots Morphology Transformation And Task Execution

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    Self-reconfigurable modular robots are composed of a small set of modules with uniform docking interfaces. Different from conventional robots that are custom-built and optimized for specific tasks, modular robots are able to adapt to many different activities and handle hardware and software failures by rearranging their components. This reconfiguration capability allows these systems to exist in a variety of morphologies, and the introduced flexibility enables self-reconfigurable modular robots to handle a much wider range of tasks, but also complicates the design, control, and planning. This thesis considers a hierarchy framework to deploy modular robots in the real world: the robot first identifies its current morphology, then reconfigures itself into a new morphology if needed, and finally executes either manipulation or locomotion tasks. A reliable system architecture is necessary to handle a large number of modules. The number of possible morphologies constructed by modules increases exponentially as the number of modules grows, and these morphologies usually have many degrees of freedom with complex constraints. In this thesis, hardware platforms and several control methods and planning algorithms are developed to build this hierarchy framework leading to the system-level deployment of modular robots, including a hybrid modular robot (SMORES-EP) and a modular truss robot (VTT). Graph representations of modular robots are introduced as well as several algorithms for morphology identification. Efficient mobile-stylereconfiguration strategies are explored for hybrid modular robots, and a real-time planner based on optimal control is developed to perform dexterous manipulation tasks. For modular truss robots, configuration space is studied and a hybrid planning framework (sampling-based and search-based) is presented to handle reconfiguration activities. A non-impact rolling locomotion planner is then developed to drive an arbitrary truss robot in an environment

    Heterogeneous Self-Reconfiguring Robotics: Ph.D. Thesis Proposal

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    Self-reconfiguring robots are modular systems that can change shape, or reconfigure, to match structure to task. They comprise many small, discrete, often identical modules that connect together and that are minimally actuated. Global shape transformation is achieved by composing local motions. Systems with a single module type, known as homogeneous systems, gain fault tolerance, robustness and low production cost from module interchangeability. However, we are interested in heterogeneous systems, which include multiple types of modules such as those with sensors, batteries or wheels. We believe that heterogeneous systems offer the same benefits as homogeneous systems with the added ability to match not only structure to task, but also capability to task. Although significant results have been achieved in understanding homogeneous systems, research in heterogeneous systems is challenging as key algorithmic issues remain unexplored. We propose in this thesis to investigate questions in four main areas: 1) how to classify heterogeneous systems, 2) how to develop efficient heterogeneous reconfiguration algorithms with desired characteristics, 3) how to characterize the complexity of key algorithmic problems, and 4) how to apply these heterogeneous algorithms to perform useful new tasks in simulation and in the physical world. Our goal is to develop an algorithmic basis for heterogeneous systems. This has theoretical significance in that it addresses a major open problem in the field, and practical significance in providing self-reconfiguring robots with increased capabilities

    Addressing Tasks Through Robot Adaptation

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    Developing flexible, broadly capable systems is essential for robots to move out of factories and into our daily lives, functioning as responsive agents that can handle whatever the world throws at them. This dissertation focuses on two kinds of robot adaptation. Modular self-reconfigurable robots (MSRR) adapt to the requirements of their task and environments by transforming themselves. By rearranging the connective structure of their component robot modules, these systems can assume different morphologies: for example, a cluster of modules might configure themselves into a car to maneuver on flat ground, a snake to climb stairs, or an arm to pick and place objects. Conversely, environment augmentation is a strategy in which the robot transforms its environment to meet its own needs, adding physical structures that allow it to overcome obstacles. In both areas, the presented work includes elements of hardware design, algorithms, and integrated systems, with the common goal of establishing these methods of adaptation as viable strategies to address tasks. The research takes a systems-level view of robotics, placing particular emphasis on experimental validation in hardware

    Cyber-Physical Systems for Micro-/Nano-assembly Operations: a Survey

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    Abstract Purpose of Review Latest requirements of the global market force manufacturing systems to a change for a new production paradigm (Industry 4.0). Cyber-Physical Systems (CPS) appear as a solution to be deployed in different manufacturing fields, especially those with high added value and technological complexity, high product variants, and short time to market. In this sense, this paper aims at reviewing the introduction level of CPS technologies in micro/nano-manufacturing and how these technologies could cope with these challenging manufacturing requirements. Recent Findings The introduction of CPS is still in its infancy on many industrial applications, but it actually demonstrates its potential to support future manufacturing paradigm. However, only few research works in micro/nano-manufacturing considered CPS frameworks, since the concept barely appeared a decade ago. Summary Some contributions have revealed the potential of CPS technologies to improve manufacturing performance which may be scaled to the micro/nano-manufacturing. IoT-based frameworks with VR/AR technologies allow distributed and collaborative systems, or agent-based architectures with advance algorithm implementations that improve the flexibility and performance of micro-/nano-assembly operations. Future research of CPS in micro-/nano-assembly operations should be followed by more studies of its technical deployment showing its implications under other perspectives, i.e. sustainable, economic, and social point of views, to take full advance of all its features

    Comprehensive review on controller for leader-follower robotic system

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    985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

    Deployment of Heterogeneous Swarm Robotic Agents Using a Task-Oriented Utility-Based Algorithm

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    In a swarm robotic system, the desired collective behavior emerges from local decisions made by robots, themselves, according to their environment. Swarm robotics is an emerging area that has attracted many researchers over the last few years. It has been proven that a single robot with multiple capabilities cannot complete an intended job within the same time frame as that of multiple robotic agents. A swarm of robots, each one with its own capabilities, are more flexible, robust, and cost-effective than an individual robot. As a result of a comprehensive investigation of the current state of swarm robotic research, this dissertation demonstrates how current swarm deployment systems lack the ability to coordinate heterogeneous robotic agents. Moreover, this dissertation's objective shall define the starting point of potential algorithms that lead to the development of a new software environment interface. This interface will assign a set of collaborative tasks to the swarm system without being concerned about the underlying hardware of the heterogeneous robotic agents. The ultimate goal of this research is to develop a task-oriented software application that facilitates the rapid deployment of multiple robotic agents. The task solutions are created at run-time, and executed by the agents in a centralized or decentralized fashion. Tasks are fractioned into smaller sub-tasks which are, then, assigned to the optimal number of robots using a novel Robot Utility Based Task Assignment (RUTA) algorithm. The system deploys these robots using it's application program interfaces (API's) and uploads programs that are integrated with a small routine code. The embedded routine allows robots to configure solutions when the decentralized approach is adopted. In addition, the proposed application also offers customization of robotic platforms by simply defining the available sensing and actuation devices. Another objective of the system is to improve code and component reusability to reduce efforts in deploying tasks to swarm robotic agents. Usage of the proposed framework prevents the need to redesign or rewrite programs should any changes take place in the robot's platform

    Dynamic Reconfiguration in Modular Self-Reconfigurable Robots Using Multi-Agent Coalition Games

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    In this thesis, we consider the problem of autonomous self-reconfiguration by modular self-reconfigurable robots (MSRs). MSRs are composed of small units or modules that can be dynamically configured to form different structures, such as a lattice or a chain. The main problem in maneuvering MSRs is to enable them to autonomously reconfigure their structure depending on the operational conditions in the environment. We first discuss limitations of previous approaches to solve the MSR self-reconfiguration problem. We will then present a novel framework that uses a layered architecture comprising a conventional gait table-based maneuver to move the robot in a fixed configuration, but using a more complex coalition game-based technique for autonomously reconfiguring the robot. We discuss the complexity of solving the reconfiguration problem within the coalition game-based framework and propose a stochastic planning and pruning based approach to solve the coalition-game based MSR reconfiguration problem. We tested our MSR self-reconfiguration algorithm using an accurately simulated model of an MSR called ModRED (Modular Robot for Exploration and Discovery) within the Webots robot simulator. Our results show that using our coalition formation algorithm, MSRs are able to reconfigure efficiently after encountering an obstacle. The average “reward” or efficiency obtained by an MSR also improves by 2-10% while using our coalition formation algorithm as compared to a previously existing multi-agent coalition formation algorithm. To the best of our knowledge, this work represents two novel contributions in the field of modular robots. First, ours is one of the first research techniques that has combined principles from human team formation techniques from the area of computational economics with dynamic self-reconfiguration in modular self-reconfigurable robots. Secondly, the modeling of uncertainty in coalition games using Markov Decision Processes is a novel and previously unexplored problem in the area of coalition formation. Overall, this thesis addresses a challenging research problem at the intersection of artificial intelligence, game theory and robotics and opens up several new directions for further research to improve the control and reconfiguration of modular robots
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