Programming platform for distributed robotics: primitives and portability

The Stabilizing Robotics Language (StarL) programming framework aims to simplify development of distributed robotic applications by providing programming abstractions and building blocks for communication, motion control and coordination between robots. It has been used to develop applications such as formation control, automatic intersection protocol, and distributed collaborative search. In this thesis, we introduce the programming abstractions as StarL primitives that are platform independent and useful across hardware platforms, resulting in portability. We first introduce the primitives as building blocks to easily develop, simulate and debug distributed robotic applications in StarL. Then, we discuss the design of the StarL framework which enables us to achieve portability of robot programs across hardware platforms. Thus, the same application program, say, for formation control, can now be ported and deployed on multiple, heterogeneous robotic platforms. We evaluate the design of these new features by simulating several applications

StarL: Toward a web interface for distributed robotics

Most first-time users find it complicated to use the StarL programming framework, especially when they have little experience with Java. The major challenges for programming distributed robotic applications are (1) the learning curve for Java, (2) setting up the StarL development environment, and (3) learning curve for effectively using the Java functions in StarL. We therefore introduce the StarL web interface that provides a more user-friendly access to the StarL programming framework while emphasizing more on the StarL high-level coordination of distributed robots. The StarL web interface enables researchers to implement their applications on distributed robots in the StarL high-level language, run the project and then plot the experiment data for analyzing the robot's traces. The main contribution of this thesis is the user-friendly interface with syntax highlighting and data visualization of the robots' traces obtained through simulation. A Formation example application will illustrate the many aspects of the StarL web interface.Ope

Swarm-Based Spatial Sorting

Purpose: To present an algorithm for spatially sorting objects into an annular structure. Design/Methodology/Approach: A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired "attraction-repulsion" mechanism. Findings: The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. Research limitations/implications: Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects. Practical implications: The algorithm may find applications in distributed robotics. Originality/value: The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.Comment: Accepted by the Int. J. Intelligent Computing and Cybernetic

Solving the Task Variant Allocation Problem in Distributed Robotics

We consider the problem of assigning software processes (or tasks) to hardware processors in distributed robotics environments. We introduce the notion of a task variant, which supports the adaptation of software to specific hardware configurations. Task variants facilitate the trade-off of functional quality versus the requisite capacity and type of target execution processors. We formalise the problem of assigning task variants to processors as a mathematical model that incorporates typical constraints found in robotics applications; the model is a constrained form of a multi-objective, multi-dimensional, multiple-choice knapsack problem. We propose and evaluate three different solution methods to the problem: constraint programming, a constructive greedy heuristic and a local search metaheuristic. Furthermore, we demonstrate the use of task variants in a real instance of a distributed interactive multi-agent navigation system, showing that our best solution method (constraint programming) improves the system’s quality of service, as compared to the local search metaheuristic, the greedy heuristic and a randomised solution, by an average of 16, 31 and 56% respectively

Design, Implementation, and Evaluation of a Distributed Real-Time Kernel for Distributed Robotics (Dissertation Proposal)

Modern robotics applications are becoming more complex due to greater numbers of sensors and actuators. The control of such systems may require multiple processors to meet the computational demands and to support the physical topology of the sensors and actuators. A distributed real-time system is needed to perform the required communication and processing while meeting application-specified timing constraints. We are designing and implementing a real-time kernel for distributed robotics applications. The kernel\u27s salient features are consistent, user-definable scheduling, explicit dynamic timing constraints, and a two-tiered interrupt approach. The kernel wi1l be evaluated by implementing a two-arm robot control example. Its goal is to locate and manipulate cylindrical objects with spillable contents. Using the application and the kernel, we will investigate the effects of time granularity, network type and protocol, and the handling of external events using interrupts versus polling. Our research will enhance understanding of real-time kernels for distributed robotics control

Task Variant Allocation in Distributed Robotics

This paper tackles the problem of allocating tasks to a distributed heterogeneous robotic system, where tasks---named *task variants* in the paper---can vary in terms of trade-off between resource requirements and quality of service provided. Three different methods (constraint programming, greedy, and metaheuristic) are proposed to solve such a problem and are evaluated both in simulation and in a real scenario, showing the goodness of the constraint programming method

RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

Modular Self-Reconfigurable Robot Systems

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing fiel