Application of Sampling-Based Motion Planning Algorithms in Autonomous Vehicle Navigation

With the development of the autonomous driving technology, the autonomous vehicle has become one of the key issues for supporting our daily life and economical activities. One of the challenging research areas in autonomous vehicle is the development of an intelligent motion planner, which is able to guide the vehicle in dynamic changing environments. In this chapter, a novel sampling-based navigation architecture is introduced, which employs the optimal properties of RRT* planner and the low running time property of low-dispersion sampling-based algorithms. Furthermore, a novel segmentation method is proposed, which divides the sampling domain into valid and tabu segments. The resulted navigation architecture is able to guide the autonomous vehicle in complex situations such as takeover or crowded environments. The performance of the proposed method is tested through simulation in different scenarios and also by comparing the performances of RRT and RRT* algorithms. The proposed method provides near-optimal solutions with smaller trees and in lower running time

A review on robot motion planning approaches

The ability of a robot to plan its own motion seems pivotal to its autonomy, and that is why the motion planning has become part and parcel of modern intelligent robotics. In this paper, about 100 research are reviewed and briefly described to identify and classify the amount of the existing work for each motion planning approach. Meanwhile, around 200 research were used to determine the percentage of the application of each approach. The paper includes comparative tables and charts showing the application frequency of each approach in the last 30 years. Finally, some open areas and challenging topics are presented based on the reviewed papers

An agile FCM for real-time modeling of dynamic and real-life systems

Fuzzy cognitive map (FCM) is a well-established model of control and decision making based on neural network and fuzzy logic methodologies. It also serves as a powerful systematic way for analyzing real-life problems where tens of known, partially known, and even unknown factors contribute to complexity of a system. FCM-based inference requires a neural activation function much like other neural network systems. In modeling, in addition to an activation function, FCM involves with weight training to learn about relationships as they exist among contributing factors. Therefore, numerous contributing factors could be analyzed to understand the behaviors of factors within a real-life system and to represent it in form of tangible matrices of weights. This article discusses a new incremental FCM activation function, named cumulative activation, and introduces a new weight training technique using simulated annealing (SA) known as agile FCM. Smooth variation of FCM nodes that is due to cumulative nature of inference results into faster convergence, while a unique minimum cost solution is guaranteed using the SA training module that is entirely expert-independent. A combination of these two techniques suits time-related applications where inclusion of temporal features is necessary. The resulted system is examined through numerical example datasets where the candidate FCM shows sensitivity to dynamic variables over time. A real-life example case is included as well to further support the effectiveness of the developed FCM in modeling of natural and complex systems

Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): an attempt towards an ensemble forecasting method

Accurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurat

Automatic navigation of mobile robots in unknown environments

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work

Development of an Educational Robotic Training Kit.

Robotics is an applied science subject in engineering course that concerning control and automation in manufacturing industry now. Engineering students are forced to learn all theories and formulas that encompass the areas of mathematics, physics, micro-electronics, dynamics and kinematics, control systems, human factors, and manufacturing operation in a single subject without any hands-on practical skills. This paper provides an idea to break the barriers of conventional lecturing and laboratory exercising for engineering students with new development of educational robotic kit. This practical approach is able to support low cost robotics course and offers simple, time saving instruction outcomes to engineering students in the kinematics study of robot manipulators with consideration of structure design, serial servo circuit board, microcontroller programming, and graphical user interface (GUI) of PC controller. General architecture of 3-degree-of-freedom (DOF) robot manipulator with two links can be solved easily with the help of this robotic kit

An Algorithm for Navigation of Mobile Robots in Cluttered Environments

Service robots have brought convenience to the disabled. Housekeeping robots, e.g., floor cleaners, must be able to handle obstacles while moving toward designated targets. In real life, yet robots face difficulty in maze-like and in very cluttered environment of crammed indoor spaces. This article introduces a new decision mechanism for path planning in indoors e.g., home, and office. A method is presented for implementing sub-goal network together with wall following. Various subroutines are used to direct the robot towards the target while each subroutine has its own sub-goal. Trajectory results are included to evaluate the performance of the algorithm

Robotic motion planning in unknown dynamic environments: existing approaches and challenges

Path planning with obstacles avoidance in dynamic environments is a crucial issue in robotics. Numerous approaches have been suggested for the navigation of mobile robots with moving obstacles. In this paper, about 50 articles have been reviewed and briefly described to offer an outline of the research progress in motion planning of mobile robot approaches in dynamic environments for the last five years. The benefits and drawbacks of each article are also explained. These papers are classified based on their issues into ten groups which are: stability, efficiency, smooth path, run time, path length, accuracy, safety, future prediction (uncertainties), control, and less computation cost. Finally, some scope and challenging topics are presented based on the papers mentioned

New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior

This study focuses on existing drawbacks and inefficiencies of the available path planning approaches within unknown dynamic environments. The drawbacks are the inability to plan under uncertain dynamic environments, non-optimality, failure in crowded complex situations, and difficulty in predicting the velocity vector of obstacles. This study aims (1) to develop a new predictive method to avoid static and dynamic obstacles in planning the path of a mobile robot in unknown dynamic environments in which the obstacles are moving and their speed profiles are not pre-identified, to find a safe path and to react rapidly and (2) to integrate a decision-making process with the predictive behavior of the velocity vector of obstacles by using the sensory system information of the robot. Information on the locations, shapes, and velocities of static and dynamic obstacles is presumed to be unavailable. Such information is determined online using rangefinder sensors. Thus, the robot recognizes free directions that lead it toward its destination and keep it safe and prevent collision with obstacles. Extensive simulations confirm the efficiency of the suggested approach and its success in handling complex and extremely dynamic environments that contain various obstacle shapes. Findings indicate that the proposed method exhibits attractive features, such as high optimality, high stability, low running time, and zero failure rates. The failure rate is zero for all test problems. The average path length for all test environments is 16.51 with a standard deviation of 0.49, which provides an average optimality rate of 89.79%. The average running time is 4.74 s (the standard deviation is 0.26)

A hybrid method using analytic hierarchical process and artificial neural network for supplier selection

A hybrid approach between the Analytic Hierarchical Process (AHP) and Artificial Neural Network (ANN) has been developed to evaluate and select the best supplier for shoes manufacturing. Firstly, questionnaire was setup based on previous study to obtain supplier selection criteria for shoes manufacturing. The proposed hybrid methodology uses the AHP to determine the local and global weights of criteria, and the ANN method to select the best supplier. In order to grasp this evaluation and selection, result calculated by AHP is compared to the result of ANN