Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: 1 simplifying the task, 2 enhancing the robot, 3 defining requirements and measuring performance, and 4 considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops.

Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead

Overview and analysis of safety management studies in the construction industry

To examine the effects of variation in body form on the mechanics of terrestrial locomotion, we used a miniature force platform to measure the ground reaction forces of the smallest and, relative to its mass, one of the fastest invertebrates ever studied, the American cockroach Periplaneta americana (mass = 0.83 g). From 0.44-1.0 ms-1, P. americana used an alternating tripod stepping pattern. Fluctuations in gravitational potential energy and horizontal kinetic energy of the center of mass were nearly in phase, characteristic of a running or bouncing gait. Aerial phases were observed as vertical ground reaction force approached zero at speeds above 1 ms-1. At the highest speeds (1.0-1.5 ms-1 or 50 body lengths per second), P. americana switched to quadrupedal and bipedal running. Stride frequency approached the wing beat frequencies used during flight (27 Hz). High speeds were attained by increasing stride length, whereas stride frequency showed little increase with speed. The mechanical power used to accelerate the center of mass increased curvilinearly with speed. The mass-specific mechanical energy used to move the center of mass a given distance was similar to that measured for animals five orders of magnitude larger in mass, but was only one-hundredth of the metabolic cost.

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Mechanics of a rapid running insect: two-, four- and six-legged locomotion.

This article surveys traditional research topics in industrial robotics and mobile robotics and then expands on new trends in robotics research that focus more on the interaction between human and robot. The new trends in robotics research have been denominated service robotics because of their general goal of getting robots closer to human social needs, and this article surveys research on service robotics such as medical robotics, rehabilitation robotics, underwater robotics, field robotics, construction robotics and humanoid robotics. The aim of this article is to provide an overview of the evolution of research topics in robotics from classical motion control for industrial robots to modern intelligent control techniques and social learning paradigms, among other aspects.

The evolution of robotics research

This paper describes the design and control concepts of a wall-climbing robot. It has an hexapod configuration and it is able to manoeuvre on vertical surfaces carrying high payloads. Configuration and leg design criteria specific for climbing tasks are discussed. The controller architecture showing decentralised parallel control and hard real-time performance is outlined. New stability criteria for wall locomotion are introduced and a climbing gait using force distribution shows the working of our control scheme for wall gait generation. We call this four phase discontinuous sawing gait. This prototype is an example of a climber specifically tailored for industrial applications.

A six-legged climbing robot for high payloads

A six-legged robot-based system for humanitarian demining missions

Minimization of energy expenditure in autonomous mobile robots for industrial and service applications is a topic of huge importance, as it is the best way of lengthening mission time without modifying the power supply. This paper presents a method to minimize the energy consumption of a hexapod robot on irregular terrain. An energy-consumption model is derived for statically stable gaits before applying minimization criteria. Then, some parameters that define the foot trajectories of the legged robot are computed to minimize energy expenditure during every half a locomotion cycle. The method is evaluated using an accurate geometric model of the SILO-6 walking robot.

Minimizing Energy Consumption in Hexapod Robots

Purpose – The purpose of this paper is to develop a climbing cleaning robot with reasonably high productivities for vertical surfaces.Design/methodology/approach – The paper analyzes the reasons for the low productivities or unreliable work of cleaning robots for vertical surfaces. Based on this analysis, a design of a new robot is created. Pilot studies were performed of laboratory and industrial prototypes of a new robot.Findings – The paper shows that the new design provides a reliable and high‐performance work of the cleaning robot for vertical surfaces. Thus, the new design provides increasing in productivities more than ten times compared with the known robot.Research limitations/implications – In this paper, it is assumed that the speed of the robot during the process of surface cleaning is constant. For future research the algorithm is proposed that automatically maintains such speed of the robot, which depends on the degree of dirtiness and is optimal for the surface to be cleaned.Practical impli...

Manuel Armada

Papers

The evolution of robotics research

A six-legged climbing robot for high payloads

A six-legged robot-based system for humanitarian demining missions

Minimizing Energy Consumption in Hexapod Robots

Climbing cleaning robot for vertical surfaces