Autonomous Snowblower

The purpose of this project is to research and develop a system to autonomously clear a specified area of snow. The resultant system includes a modified snowblower, a base station, and a computer application to monitor and define the area for the snowblower to clear. A high precision GPS is equipped on both the snowblower and the base station to provide accurate location data. The snowblower is additionally equipped with a LIDAR sensor for obstacle detection in the snow, as well as a microcontroller to run embedded software and interface with the computer application. The team faced many design challenges and learned a substantial amount through the implementation of the research they conducted

Towards a Prototype Platform for ROS Integrations on a Ground Robot

The intent of this work was to develop, evaluate, and demonstrate a prototype robot platform on which ROS integrations could be explored. With observations of features and requirements of existing industrial and service mobile ground robots, a platform was designed and outfitted with appropriate components to enable the most common operational-critical functionalities and account for unforeseen components and features. The resulting Arlo Demonstration Robot accommodates basic mapping, localization, and navigation in both two and three-dimensional space as well as additional safety and teleoperation features. The control system is centered around the Zybo Z7 FPGA SoC hosting a custom hardware design. The platform is validated through an analysis of feature requirements and limitations and additional evaluations of a series of real-world use cases demonstrating high-level behaviors. In order to promote further development, this work serves as detailed documentation of the selection, implementation, and testing of this platform and complements initial binary releases for the Zybo Z7 control system and accompanying source code for the functionalities implemented. This prototype robot stack can be further developed to enable additional capabilities and validate its performance in other real-world scenarios or used as a reference for porting to alternative robot platforms

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

A study of interactive control scheduling and economic assessment for robotic systems

A class of interactive control systems is derived by generalizing interactive manipulator control systems. Tasks of interactive control systems can be represented as a network of a finite set of actions which have specific operational characteristics and specific resource requirements, and which are of limited duration. This has enabled the decomposition of the overall control algorithm simultaneously and asynchronously. The performance benefits of sensor referenced and computer-aided control of manipulators in a complex environment is evaluated. The first phase of the CURV arm control system software development and the basic features of the control algorithms and their software implementation are presented. An optimal solution for a production scheduling problem that will be easy to implement in practical situations is investigated

Applying a qualitative framework of acceptance of personal robots

Personal robots can help people live safer, more efficient and comfortable lives. However, such benefits cannot be achieved if people do not use, or accept, personal robots. The use of a technology is predominantly influenced by an individual's intention to use it, which is influenced by his or her attitudes toward it (Davis, 1989). Presently, the key factors that impact the use of personal robots are not fully understood. Two studies were conducted as first step assessments of the Smarr, Fisk, and Rogers (2013) theoretically-based framework of acceptance of personal robots. In study 1, 14 participants used a personal robot (a robot lawn mower) at their homes for about six weeks. Their acceptance and factors important for acceptance identified in the framework were measured using pre-use and post-use interviews and questionnaires, and weekly diaries. The framework was conceptually validated; participants mentioned 16 of the 20 factors in the Smarr et al. (2013) framework. However, the framework did not fully account for the breadth of factors discussed by participants, thereby suggesting variables may need to be added to or removed from the framework. In study 2, 280 participants reported their initial acceptance of a personal robot (a robot mower) with different levels of reliability and communication of feedback in an online survey. Level of robot reliability did significantly affect attitudinal and intentional acceptance. Follow up analyses indicated a trend that participants who received no information on reliability had numerically higher acceptance than participants who were informed that the robot had 70% reliability or 90% reliability. Neither communication of feedback nor its interaction with reliability affected acceptance. The Smarr et al. (2013) framework explained about 60% of the variance in intentional acceptance and 57% in attitudinal acceptance of a personal robot. Eight of the 15 relationships tested were supported via path analysis. Findings largely supported the Smarr et al. (2013) framework in explaining what impacts intentional and attitudinal acceptance of a personal robot. Results from these studies can inform the Smarr et al. (2013) framework of robot acceptance and other models of acceptance, and can guide designers in developing acceptable personal robots.Ph.D

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production