On-Board Electronic Control Systems of Future Automated Heavy Machinery

The level of automation and wireless communication has increased in heavy machinery recently. This requires utilizing new devices and communication solutions in heavy machinery applications which involve demanding operating conditions and challenging life-cycle management. Therefore, the applied devices have to be robust and hardware architectures flexible, consisting of generic modules. In research and development projects devices that have various communication interfaces and insufficient mechanical and electrical robustness need to be applied. Although this thesis has its main focus on machines utilized as research platforms, many of the challenges are similar with commercial machines.The applicability of typical solutions for data transfer is discussed. Controller area network with a standardized higher level protocol is proposed to be applied where data signalling rates above 1 Mb/s are not required. The main benefits are the availability of robust, generic devices and well-established software tools for configuration management. Ethernet can be utilized to network equipment with high data rates, typically used for perception. Although deterministic industrial Ethernet protocols would fulfil most requirements, the conventional internet protocol suite is likely to be applied due to device availability.Sometimes sensors and other devices without a suitable communication interface need to be applied. In addition, device-related real-time processing or accurate synchronization of hardware signals may be required. A small circuit board with a microcontroller can be utilized as a generic embedded module for building robust, small and cost-efficient prototype devices that have a controller area network interface. Although various microcontroller boards are commercially available, designing one for heavy machinery applications, in particular, has benefits in robustness, size, interfaces, and flexible software development. The design of such a generic embedded module is presented.The device-specific challenges of building an automated machine are discussed. Unexpected switch-off of embedded computers has to be prevented by the control system to avoid file system errors. Moreover, the control system has to protect the batteries against deep discharge when the engine is not running. With many devices, protective enclosures with heating or cooling are required.The electronic control systems of two automated machines utilized as research platforms are presented and discussed as examples. The hardware architectures of the control systems are presented, following the proposed communication solutions as far as is feasible. Several applications of the generic embedded module within the control systems are described. Several research topics have been covered utilizing the automated machines. In this thesis, a cost-efficient operator-assisting functionality of an excavator is presented and discussed in detail.The results of this thesis give not only research institutes but also machine manufacturers and their subcontractors an opportunity to streamline the prototyping of automated heavy machinery

Progress toward multi‐robot reconnaissance and the MAGIC 2010 competition

Tasks like search‐and‐rescue and urban reconnaissance benefit from large numbers of robots working together, but high levels of autonomy are needed to reduce operator requirements to practical levels. Reducing the reliance of such systems on human operators presents a number of technical challenges, including automatic task allocation, global state and map estimation, robot perception, path planning, communications, and human‐robot interfaces. This paper describes our 14‐robot team, which won the MAGIC 2010 competition. It was designed to perform urban reconnaissance missions. In the paper, we describe a variety of autonomous systems that require minimal human effort to control a large number of autonomously exploring robots. Maintaining a consistent global map, which is essential for autonomous planning and for giving humans situational awareness, required the development of fast loop‐closing, map optimization, and communications algorithms. Key to our approach was a decoupled centralized planning architecture that allowed individual robots to execute tasks myopically, but whose behavior was coordinated centrally. We will describe technical contributions throughout our system that played a significant role in its performance. We will also present results from our system both from the competition and from subsequent quantitative evaluations, pointing out areas in which the system performed well and where interesting research problems remain. © 2012 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93532/1/21426_ftp.pd

The Underpinnings of Workload in Unmanned Vehicle Systems

This paper identifies and characterizes factors that contribute to operator workload in unmanned vehicle systems. Our objective is to provide a basis for developing models of workload for use in design and operation of complex human-machine systems. In 1986, Hart developed a foundational conceptual model of workload, which formed the basis for arguably the most widely used workload measurement techniquethe NASA Task Load Index. Since that time, however, there have been many advances in models and factor identification as well as workload control measures. Additionally, there is a need to further inventory and describe factors that contribute to human workload in light of technological advances, including automation and autonomy. Thus, we propose a conceptual framework for the workload construct and present a taxonomy of factors that can contribute to operator workload. These factors, referred to as workload drivers, are associated with a variety of system elements including the environment, task, equipment and operator. In addition, we discuss how workload moderators, such as automation and interface design, can be manipulated in order to influence operator workload. We contend that workload drivers, workload moderators, and the interactions among drivers and moderators all need to be accounted for when building complex, human-machine systems

Flex-Ro: A Robotic High Throughput Field Phenotyping System

Research in agriculture is critical to developing techniques to meet the world’s demand for food, fuel, fiber, and feed. Optimization of crop production per unit of land requires scientists across disciplines to collaborate and investigate new areas of science and tools for data collection. The use of robotics has been adopted in several industries to supplement labor, and accurately perform repetitious tasks. However, the use of autonomous robots in commercial agricultural production is still limited. The Flex-Ro (Flexible structured Robotic platform) was developed for use in large area fields as a multipurpose tool to perform monotonous agricultural tasks. This work presents the design and implementation of the control system for the Flex-Ro machine. The machine control architecture was developed for safe operation with redundant emergency stops and checks. Operators use the remote-control device to maneuver the machine in uncontrolled environments. Autonomous field coverage was developed using global positioning system (GPS) guidance. The guidance system tracked within 4 cm of the guidance line 95% of the time at a travel speed of 4 kph. Waypoint guidance was implemented and demonstrated such that Flex-Ro could be programmed to follow complex paths and curves. High-throughput plant phenotyping is a continuously developing and evolving field of plant science. The methods used to collect phenotyping data include drones, satellites, manual measurement, and ground rovers. A suite of phenotyping sensors was installed onto the Flex-Ro to cover large field areas. The system was verified in soybean research plots at the University of Nebraska-Lincoln (UNL) Spidercam phenotyping facility. Positive correlations between the Spidercam and Flex-Ro phenotyping data were established. The Flex-Ro was able to statistically distinguish between soybean variety emergence and maturity differences. The late season phenotyping data showed statistical differences between the fully irrigated versus deficit plots. Basic economic calculations estimated the cost to operate the Flex-Ro machine for field phenotyping use at approximately $5.50/ha. Advisor: Santosh K. Pitl

Improving the safety and efficiency of rail yard operations using robotics

textSignificant efforts have been expended by the railroad industry to make operations safer and more efficient through the intelligent use of sensor data. This work proposes to take the technology one step further to use this data for the control of physical systems designed to automate hazardous railroad operations, particularly those that require humans to interact with moving trains. To accomplish this, application specific requirements must be established to design self-contained machine vision and robotic solutions to eliminate the risks associated with existing manual operations. Present-day rail yard operations have been identified as good candidates to begin development. Manual uncoupling, in particular, of rolling stock in classification yards has been investigated. To automate this process, an intelligent robotic system must be able to detect, track, approach, contact, and manipulate constrained objects on equipment in motion. This work presents multiple prototypes capable of autonomously uncoupling full-scale freight cars using feedback from its surrounding environment. Geometric image processing algorithms and machine learning techniques were implemented to accurately identify cylindrical objects in point clouds generated in real-vi time. Unique methods fusing velocity and vision data were developed to synchronize a pair of moving rigid bodies in real-time. Multiple custom end-effectors with in-built compliance and fault tolerance were designed, fabricated, and tested for grasping and manipulating cylindrical objects. Finally, an event-driven robotic control application was developed to safely and reliably uncouple freight cars using data from 3D cameras, velocity sensors, force/torque transducers, and intelligent end-effector tooling. Experimental results in a lab setting confirm that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock up to two miles per hour. Additionally, subcomponents of the autonomous pin-pulling system (APPS) were designed to be modular to the point where they could be used to automate other hazardous, labor-intensive tasks found in U.S. classification yards. Overall, this work supports the deployment of autonomous robotic systems in semi-unstructured yard environments to increase the safety and efficiency of rail operations.Mechanical Engineerin

Flex-Ro: A Robotic High Throughput Field Phenotyping System

Research in agriculture is critical to developing techniques to meet the world’s demand for food, fuel, fiber, and feed. Optimization of crop production per unit of land requires scientists across disciplines to collaborate and investigate new areas of science and tools for data collection. The use of robotics has been adopted in several industries to supplement labor, and accurately perform repetitious tasks. However, the use of autonomous robots in commercial agricultural production is still limited. The Flex-Ro (Flexible structured Robotic platform) was developed for use in large area fields as a multipurpose tool to perform monotonous agricultural tasks. This work presents the design and implementation of the control system for the Flex-Ro machine. The machine control architecture was developed for safe operation with redundant emergency stops and checks. Operators use the remote-control device to maneuver the machine in uncontrolled environments. Autonomous field coverage was developed using global positioning system (GPS) guidance. The guidance system tracked within 4 cm of the guidance line 95% of the time at a travel speed of 4 kph. Waypoint guidance was implemented and demonstrated such that Flex-Ro could be programmed to follow complex paths and curves. High-throughput plant phenotyping is a continuously developing and evolving field of plant science. The methods used to collect phenotyping data include drones, satellites, manual measurement, and ground rovers. A suite of phenotyping sensors was installed onto the Flex-Ro to cover large field areas. The system was verified in soybean research plots at the University of Nebraska-Lincoln (UNL) Spidercam phenotyping facility. Positive correlations between the Spidercam and Flex-Ro phenotyping data were established. The Flex-Ro was able to statistically distinguish between soybean variety emergence and maturity differences. The late season phenotyping data showed statistical differences between the fully irrigated versus deficit plots. Basic economic calculations estimated the cost to operate the Flex-Ro machine for field phenotyping use at approximately $5.50/ha. Advisor: Santosh K. Pitl

Navigation of Automatic Vehicle using AI Techniques

In the field of mobile robot navigation have been studied as important task for the new generation of mobile robot i.e. Corobot. For this mobile robot navigation has been viewed for unknown environment. We consider the 4-wheeled vehicle (Corobot) for Path Planning, an autonomous robot and an obstacle and collision avoidance to be used in sensor based robot. We propose that the predefined distance from the robot to target and make the robot follow the target at this distance and improve the trajectory tracking characteristics. The robot will then navigate among these obstacles without hitting them and reach the specified goal point. For these goal achieving we use different techniques radial basis function and back-propagation algorithm under the study of neural network. In this Corobot a robotic arm are assembled and the kinematic analyses of Corobot arm and help of Phidget Control Panel a wheeled to be moved in both forward and reverse direction by 2-motor controller have to be done. Under kinematic analysis propose the relationships between the positions and orientation of the links of a manipulator. In these studies an artificial techniques and their control strategy are shown with potential applications in the fields of industry, security, defense, investigation, and others. Here finally, the simulation result using the webot neural network has been done and this result is compared with experimental data for different training pattern

Enhanced vision-based localization and control for navigation of non-holonomic omnidirectional mobile robots in GPS-denied environments

New Zealand’s economy relies on primary production to a great extent, where use of the technological advances can have a significant impact on the productivity. Robotics and automation can play a key role in increasing productivity in primary sector, leading to a boost in national economy. This thesis investigates novel methodologies for design, control, and navigation of a mobile robotic platform, aimed for field service applications, specifically in agricultural environments such as orchards to automate the agricultural tasks. The design process of this robotic platform as a non-holonomic omnidirectional mobile robot, includes an innovative integrated application of CAD, CAM, CAE, and RP for development and manufacturing of the platform. Robot Operating System (ROS) is employed for the optimum embedded software system design and development to enable control, sensing, and navigation of the platform. 3D modelling and simulation of the robotic system is performed through interfacing ROS and Gazebo simulator, aiming for off-line programming, optimal control system design, and system performance analysis. Gazebo simulator provides 3D simulation of the robotic system, sensors, and control interfaces. It also enables simulation of the world environment, allowing the simulated robot to operate in a modelled environment. The model based controller for kinematic control of the non-holonomic omnidirectional platform is tested and validated through experimental results obtained from the simulated and the physical robot. The challenges of the kinematic model based controller including the mathematical and kinematic singularities are discussed and the solution to enable an optimal kinematic model based controller is presented. The kinematic singularity associated with the non-holonomic omnidirectional robots is solved using a novel fuzzy logic based approach. The proposed approach is successfully validated and tested through the simulation and experimental results. Development of a reliable localization system is aimed to enable navigation of the platform in GPS-denied environments such as orchards. For this aim, stereo visual odometry (SVO) is considered as the core of the non-GPS localization system. Challenges of SVO are introduced and the SVO accumulative drift is considered as the main challenge to overcome. SVO drift is identified in form of rotational and translational drift. Sensor fusion is employed to improve the SVO rotational drift through the integration of IMU and SVO. A novel machine learning approach is proposed to improve the SVO translational drift using Neural-Fuzzy system and RBF neural network. The machine learning system is formulated as a drift estimator for each image frame, then correction is applied at that frame to avoid the accumulation of the drift over time. The experimental results and analyses are presented to validate the effectiveness of the methodology in improving the SVO accuracy. An enhanced SVO is aimed through combination of sensor fusion and machine learning methods to improve the SVO rotational and translational drifts. Furthermore, to achieve a robust non-GPS localization system for the platform, sensor fusion of the wheel odometry and the enhanced SVO is performed to increase the accuracy of the overall system, as well as the robustness of the non-GPS localization system. The experimental results and analyses are conducted to support the methodology

Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments