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

Memetic algorithms (MAs) are population-based meta-heuristic search algorithms that combine the composite benefits of natural and cultural evolutions. An adaptive MA (AMA) incorporates an adaptive selection of memes (units of cultural transmission) from a meme pool to improve the cultural characteristics of the individual member of a population-based search algorithm. This paper presents a novel approach to design an AMA by utilizing the composite benefits of differential evolution (DE) for global search and Q-learning for local refinement. Four variants of DE, including the currently best self-adaptive DE algorithm, have been used here to study the relative performance of the proposed AMA with respect to runtime, cost function evaluation, and accuracy (offset in cost function from the theoretical optimum after termination of the algorithm). Computer simulations performed on a well-known set of 25 benchmark functions reveal that incorporation of Q-learning in one popular and one outstanding variants of DE makes the corresponding algorithm more efficient in both runtime and accuracy. The performance of the proposed AMA has been studied on a real-time multirobot path-planning problem. Experimental results obtained for both simulation and real frameworks indicate that the proposed algorithm-based path-planning scheme outperforms the real-coded genetic algorithm, particle swarm optimization, and DE, particularly its currently best version with respect two standard metrics defined in the literature.

Realization of an Adaptive Memetic Algorithm Using Differential Evolution and Q-Learning: A Case Study in Multirobot Path Planning

In J Robot Syst 13(3):177---185 (1996), Ma proposed an efficient technique to stabilize local torque optimization solution of redundant manipulators, which prevents occurrence of high joint-velocity and guarantees the final joint-velocity to be near zero. To prevent the same problems, a different-level simultaneous minimization scheme is proposed in this paper for robotic redundancy resolution, which combines the minimum two-norm joint-velocity and joint-torque solutions via two weighting factors. Physical constraints such as joint-angle limits, joint-velocity limits and joint-acceleration limits are also taken into consideration in such a scheme-formulation. Moreover, the proposed different-level simultaneous minimization scheme is resolved at the joint-acceleration level and reformulated as a general quadratic program (QP). Computer-simulation results based on the PUMA560 robot manipulator performing different types of end-effector path-tracking tasks demonstrate the validity and advantage of the proposed different-level simultaneous minimization scheme. Furthermore, experimental verification conducted on a practical six-link planar robot manipulator substantiates the effectiveness and the physical realizability of the proposed scheme.

Different-Level Simultaneous Minimization of Joint-Velocity and Joint-Torque for Redundant Robot Manipulators

Mobile robot path planning algorithm based on single-chain sequential backtracking Q-learning

Quantum-like mutation-induced dragonfly-inspired optimization approach

Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms

The invention provides an intelligent robot for removing ice in a high-voltage transmission line, which is characterized by comprising a machine body, and a thread drawing mechanism, a walking drive mechanism, a brake mechanism and an ice removing mechanism which are mounted on the machine body, wherein the thread drawing mechanism comprises a wheel frame, three coplanar walking wheels capable of drawing threads mounted on the wheel frame, an L-shaped thread feeding groove, a thread holding mechanism, four coplanar thread holding wheels mounted on the top of the thread holding mechanism, and a lever mechanism used for regulating upper-lower position of the thread holding mechanism; the brake mechanism comprises a braking claw, a return spring used for resetting the claw and an electric push rod used for driving the claw to open and close; and the ice removing mechanism comprises an ice removing motor, a cam, an ice breaking steel knife, a sliding block, a tension spring and a support disk for fixing the ice breaking steel knife. The intelligent robot for removing ice in a high-voltage transmission line has simple and reliable structure, portability, high level of automation and low energy consumption and can automatically effectively remove ice in a transmission line.

Intelligent robot for removing ice in high-voltage transmission line

The invention discloses a traveling mechanism of a robot on a high voltage transmission line, which is characterized in that 3 traveling arms including a fore arm, a middle arm and a rear arm are arranged on a robot main body When the robot travels on the transmission line, the fore arm and the rear arm are on the same side of the transmission line, and the middle arm is on the other side of thetransmission line Each traveling arm comprises a traveling wheel, a lifting arm, a braking mechanism and a swing arm, wherein one end of the swing arm is connected with the robot main body through aswing joint, and the other end is connected with the lower end of the lifting arm; the traveling wheel is installed at the side part on the upper end of the lifting arm; and the braking mechanism is installed on the lifting arm and below the traveling wheel The traveling mechanism can drive the robot to conveniently cross various obstacles on the transmission line, and the stability is good

Traveling mechanism of robot on high voltage transmission line

Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms: Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms

The invention discloses an automatic line-grasping control method of a deicing robot in a high-voltage transmission line. An enhanced learning system is established by setting a state set, an action set, a Q table and a reward function. The method comprises the following steps of: selecting an action a to act on the environment by the system; after the action is accepted, transferring the environment to next state s'; meanwhile, giving out an evaluation signal r; if r meets certain conditions, stopping controlling, and otherwise deciding next action a' by the enhanced learning system according to the evaluation signal and the state s' through a behavior selection strategy; and entering next iteration after updating the Q table. The method overcomes the influence brought by the model errorof the robot, inaccurate posture at the tail end of robot arms, complicated environment and the like, and can be used for adjusting the posture at the tail end of the arm on line in real time for realizing the quick and accurate line grasping of the arm without human remote control, thereby achieving the aim of the automatic line-grasping control.

Shuning Wei

Papers

Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms

Intelligent robot for removing ice in high-voltage transmission line

Traveling mechanism of robot on high voltage transmission line

Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms: Inverse Kinematic Solution for Robot Manipulator Based on Electromagnetism-like and Modified DFP Algorithms

Automatic line-grasping control method of deicing robot in high-voltage transmission line