Trajectory solutions for a game-playing robot using nonprehensile manipulation methods and machine vision

The need for autonomous systems designed to play games, both strategy-based and physical, comes from the quest to model human behaviour under tough and competitive environments that require human skill at its best. In the last two decades, and especially after the 1996 defeat of the world chess champion by a chess-playing computer, physical games have been receiving greater attention. Robocup TM, i.e. robotic football, is a well-known example, with the participation of thousands of researchers all over the world. The robots created to play snooker/pool/billiards are placed in this context. Snooker, as well as being a game of strategy, also requires accurate physical manipulation skills from the player, and these two aspects qualify snooker as a potential game for autonomous system development research. Although research into playing strategy in snooker has made considerable progress using various artificial intelligence methods, the physical manipulation part of the game is not fully addressed by the robots created so far. This thesis looks at the different ball manipulation options snooker players use, like the shots that impart spin to the ball in order to accurately position the balls on the table, by trying to predict the ball trajectories under the action of various dynamic phenomena, such as impacts. A 3-degree of freedom robot, which can manipulate the snooker cue on a par with humans, at high velocities, using a servomotor, and position the snooker cue on the ball accurately with the help of a stepper drive, is designed and fabricated. [Continues.

A theoretical analysis of billiard ball dynamics under cushion impacts

The last two decades have seen a growing interest in research related to billiards. There have been a number of projects aimed at developing training systems, robots, and computer simulations for billiards. Determination of billiard ball trajectories is important for all of these systems. The ball’s collision with a cushion is often encountered in billiards and it drastically changes the ball trajectory, especially when the ball has spin. This work predicts ball bounce angles and bounce speeds for the ball’s collision with a cushion, under the assumption of insignificant cushion deformation. Differential equations are derived for the ball dynamics during the impact and these equations are solved numerically. The numerical solutions together with previous experimental work by the authors predict that for the ball–cushion collision, the values of the coefficient of restitution and the sliding coefficient of friction are 0.98 and 0.14, respectively. A comparison of the numerical and experimental results indicates that the limiting normal velocity under which the rigid cushion assumption is valid is 2.5 m/s. A number of plots that show the rebound characteristics for given ball velocity–spin conditions are also provided. The plots quantify various phenomena that have hitherto only been described in the billiards literature

Pothole 3D Reconstruction With a Novel Imaging System and Structure From Motion Techniques

Machine vision based evaluation systems are receiving increased attention, day by day, for automated quality inspection of roads. Industrial pavement scanners consist of laser scanners and are very expensive, hence inaccessible for everyone. The proposed work presents a simple and novel approach for 3D reconstruction of potholes for an automated inspection and road surface evaluation. The technique utilizes a Structure from Motion based 3D reconstruction algorithm, along with laser triangulation, to generate 3D point clouds of potholes. Alongside, a novel low-cost system, consisting of a single camera and a laser pointer, is also proposed. Keypoint matching techniques are employed, with the 5-point algorithm, on successive image frames to generate a point cloud. However, this point cloud is not metric yet, without scale information. The scale ambiguity is solved by making use of the laser pointer, and using the principle of triangulation. The laser spot is also detected in the same image sequence that is used for point-cloud building, cutting down the image capturing and processing overhead. The system has been benchmarked on artificial indentations with known dimensions, proving the robustness of the measurement scheme and hardware. Static and dynamic tests have been performed. The mean depth errors for measurement made by the imager statically and at dynamic speeds of 10 km/hr, 15 km/hr, and 20 km/hr are 5.3%, 7.9%, 14.4%, and 26.6%, whereas for perimeter the errors are 5.2%, 6.83 %, 11.8%, and 27.8%. The proposed, low-cost technique shows promising results in generating 3D point clouds for potholes

Trajectory Solutions for a Game-Playing Robot using Nonprehensile Manipulation Methods and Machine Vision

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Development of an on-board visual display for early knock detection in SI engine

The main purpose of this project is to develop an on-board visual display for early knock detection in SI car engines. The system consists of a low-cost piezoelectric accelerometer and two signal processing boards with wireless data transmission capability. One board is placed on the engine side and other board is used to visualise engine vibration on the user end. This kind of visualization is useful for a maintenance person to detect knock in early stages. The signal processing board contains dsPIC microcontroller that processes the vibration data acquired by a low-cost ADXL accelerometer. The board is programmed with Fast Fourier Transform (FFT) algorithm to perform the frequency calculations in order to detect knock in the car engine. Once the knock calculation is completed by the dsPIC microcontroller, it transmits the data to the main board on the user’s end through X-Bee wireless protocol. Finally, the FFT spectrum is displayed on a Graphic Liquid Crystal Display (GLCD) and hence the possible frequency of interest on which knock occurs. This kind of device helps the car user and maintenance person to be aware of the early knock symptoms

A unified artificial neural network model for asphalt pavement condition prediction

Most performance prediction models for asphalt pavements are either based on laboratory data or numerical distress data collected from field surveys. However, these models do not fully reflect the true performance of pavements in different traffic and environmental conditions. In the study reported in this paper, a multi-input unified prediction model based on an artificial neural network was developed by using a mixture of numerical and categorical features for in-service pavement test sections in the USA. Pavement age, cracking length and area, cumulative traffic loading, two functional classes of roads, four climatic zones and maintenance effects were considered as input variables while changes in the pavement condition index (PCI) were determined as the output. The developed model was found to be efficient in terms of processing time and accuracy in dealing with the complexity and non-linearity of multiple input parameters. The results showed that the model provided a high correlation between observed and predicted deterioration at the training stage. The testing and validation results also yielded high accuracy in predicting the PCI and could be combined with a pavement management system to plan timely and accurate maintenance strategies.</p

Ball positioning in robotic billiards: a nonprehensile manipulation-based solution

The last two decades have seen a number of developments in creating robots to play billiards. The designed robotic systems have successfully incorporated the kinematics required and have had appropriate machine vision elements for a decent gameplay. Independently, computer scientists have also developed the artificial intelligence programs needed for the strategy to play billiards. Despite these developments, the accurate ball manipulation aspect of the game, needed for good performance, has not been addressed enough; two important parameters are the potting accuracy and advantageous cue ball positioning for next shot. In this regard, robotic ball manipulation by predicting the ball trajectories under the action of various dynamic phenomena, such as ball spin, impacts and friction, is the key consideration of this research. By establishing a connection to the methods used in nonprehensile robotic manipulation, a forward model is developed for the rolling, sliding and two distinct types of frictional impacts of billiards balls are developed. High-speed camera based tracking is performed to determine the physical parameters required for the developed dynamic models. To solve the inverse manipulation problem, i.e. the decision on shot parameters, for accurate ball positioning, an optimization based solution is proposed. A simplistic ball manipulator is designed and used to test the theoretical developments. Experimental results show that a 90% potting accuracy and a 100–200 mm post-shot cue ball positioning accuracy has been achieved by the autonomous system within a table area of 6 × 5 ft2

Sustainable operations of a combined cycle power plant using artificial intelligence based power prediction

Combined Cycle Power Plants (CCPP) are an effective method for Power generation due to their high thermal efficiency, low fuel consumption, and low greenhouse emissions. However, investing millions into building a power plant without knowledge of the power generation capacity seems unproductive. With the help of AI, we have tried to eliminate this conundrum. The present study focuses on the prediction of power produced by a 747 MW Combined Cycle Power Plant (CCPP) using a Back Propagation Neural Network (BPNN) and compares its results with the actual data from CCPP. BPNN is a regression-based prediction technique that is utilized in this study to develop a predictive model and train it using the following input features: Ambient Temperature, Ambient Pressure, Mass Flow rate of fuel in Gas Turbine 1, and Mass Flow rate of fuel in Gas Turbine 2. The Predictive Model with 10 neurons in the hidden layer was found to be most effective with Mean Squared Error (MSE) value, for the validation dataset, of 0.0063237. CCPP is also analyzed through a thermodynamic model, developed using EES. A detailed energy analysis is carried out and the results were compared with predicted and actual data. It was found that the thermal efficiency and total power generation of actual, predicted, and simulated models were 27.541% &amp; 667.32 MW, 28.238% &amp; 683.48 MW and 28.201% &amp; 683.16 MW, respectively. A parametric study was further carried out to investigate the significance of operating parameters on power output and it was concluded that the temperatures across the Gas turbines have a significant impact on the performance of CCPP. Finally, Methane was replaced by 3 different fuels, one by one, and the effect of each fuel was investigated thermodynamically. It was found that the Lower Heating Value (LHV) of fuel was an important parameter in achieving a higher power output. It can be summarized from this research work that predictive models do have accuracy and such data science techniques can be used as a substitute for extensive thermodynamic calculations

A Hybrid Approach for Noise Reduction in Acoustic Signal of Machining Process Using Neural Networks and ARMA Model

Intelligent machining has become an important part of manufacturing systems because of the increased demand for productivity. Tool condition monitoring is an integral part of these systems. Airborne acoustic emission from the machining process is a vital indicator of tool health, however, it is highly affected by background noise. Reducing the background noise helps in developing a low-cost system. In this research work, a feedforward neural network is used as an adaptive filter to reduce the background noise. Acoustic signals from four different machines in the background are acquired and are introduced to a machining signal at different speeds and feed-rates at a constant depth of cut. These four machines are a three-axis milling machine, a four-axis mini-milling machine, a variable speed DC motor, and a grinding machine. The backpropagation neural network shows an accuracy of 75.82% in classifying the background noise. To reconstruct the filtered signal, a novel autoregressive moving average (ARMA)-based algorithm is proposed. An average increase of 71.3% in signal-to-noise ratio (SNR) is found before and after signal reconstruction. The proposed technique shows promising results for signal reconstruction for the machining process