Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm

An algorithm that integrates the improved artificial fish swarm algorithm with continuous segmented Bézier curves is proposed, aiming at the path planning and smoothing of mobile robots. On the one hand, to overcome the low accuracy problems, more inflection points and relatively long planning paths in the traditional artificial fish swarm algorithm for path planning, feasible solutions and a range of step sizes are introduced based on Dijkstra's algorithm. To solve the problems of poor convergence and degradation that hinder the algorithm's ability to find the best in the later stage, a dynamic feedback horizon and an adaptive step size are introduced. On the other hand, to ensure that the planned paths are continuous in both orientation and curvature, the Bessel curve theory is introduced to smooth the planned paths. This is demonstrated through a simulation that shows the improved artificial fish swarm algorithm achieving 100% planning accuracy, while ensuring the shortest average path in the same grid environment. Additionally, the smoothed path is continuous in both orientation and curvature, which satisfies the kinematic characteristics of the mobile robot.

A Hierarchical Restoration Mechanism for Distribution Networks Considering Multiple Faults

Service restoration of distribution networks in contingency situations is one of the highly investigated and challenging problems. In the conventional service restoration method, utilities reconfigure the topological structure of the distribution networks to supply the consumer load demands. However, the advancements in renewable distributed generations define a new dimension for developing service restoration methodologies. This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The service restoration problem is modeled as a complicated and hierarchical program. The objectives are to achieve the maximization of loads restored with minimization of switch operations while simultaneously satisfying grid operational constraints and ensuring a radial operation configuration. We present the service restoration mechanism, which includes the dynamic topology analysis, matching isolated islands with renewable distributed generations, network reconfiguration, and network optimization. A new code scheme that avoids feasible solutions is applied to generate candidate solutions to reduce the computational burden. We evaluate the proposed mechanism on the IEEE 33 and 69 systems and report on the collected results under multitype fault cases. The results demonstrate the importance of the available renewable distributed generations in the proposed mechanism. Moreover, simulation results verify that the proposed mechanism can obtain reasonable service restoration plans to achieve the maximization of loads restored and minimization of switching operations under different faults.

Configuring ADAS Platforms for Automotive Applications Using Metaheuristics

Modern Advanced Driver-Assistance Systems (ADAS) combine critical real-time and non-critical best-effort tasks and messages onto an integrated multi-core multi-SoC hardware platform. The real-time safety-critical software tasks have complex interdependencies in the form of end-to-end latency chains featuring, e.g., sensing, processing/sensor fusion, and actuating. The underlying real-time operating systems running on top of the multi-core platform use static cyclic scheduling for the software tasks, while the communication backbone is either realized through PCIe or Time-Sensitive Networking (TSN). In this paper, we address the problem of configuring ADAS platforms for automotive applications, which means deciding the mapping of tasks to processing cores and the scheduling of tasks and messages. Time-critical messages are transmitted in a scheduled manner via the timed-gate mechanism described in IEEE 802.1Qbv according to the pre-computed Gate Control List (GCL) schedule. We study the computation of the assignment of tasks to the available platform CPUs/cores, the static schedule tables for the real-time tasks, as well as the GCLs, such that task and message deadlines, as well as end-to-end task chain latencies, are satisfied. This is an intractable combinatorial optimization problem. As the ADAS platforms and applications become increasingly complex, such problems cannot be optimally solved and require problem-specific heuristics or metaheuristics to determine good quality feasible solutions in a reasonable time. We propose two metaheuristic solutions, a Genetic Algorithm (GA) and one based on Simulated Annealing (SA), both creating static schedule tables for tasks by simulating Earliest Deadline First (EDF) dispatching with different task deadlines and offsets. Furthermore, we use a List Scheduling-based heuristic to create the GCLs in platforms featuring a TSN backbone. We evaluate the proposed solution with real-world and synthetic test cases scaled to fit the future requirements of ADAS systems. The results show that our heuristic strategy can find correct solutions that meet the complex timing and dependency constraints at a higher rate than the related work approaches, i.e., the jitter constraints are satisfied in over 6 times more cases, and the task chain constraints are satisfied in 41% more cases on average. Our method scales well with the growing trend of ADAS platforms.

Artificial Neural Networks and Deep Learning Techniques Applied to Radar Target Detection: A Review

Radar target detection (RTD) is a fundamental but important process of the radar system, which is designed to differentiate and measure targets from a complex background. Deep learning methods have gained great attention currently and have turned out to be feasible solutions in radar signal processing. Compared with the conventional RTD methods, deep learning-based methods can extract features automatically and yield more accurate results. Applying deep learning to RTD is considered as a novel concept. In this paper, we review the applications of deep learning in the field of RTD and summarize the possible limitations. This work is timely due to the increasing number of research works published in recent years. We hope that this survey will provide guidelines for future studies and applications of deep learning in RTD and related areas of radar signal processing.

Internet of Things-Empowered Next-Generation Healthcare Systems

Internet of things is seen in many fields like civil engineering, consumer goods, oil and gas fields, smart cities, agriculture, etc. Apart from these, it is applicable to the medical field to detect and treat many kinds of diseases and can find the different health parameters quickly. It became important in the health sector to mitigate the challenges of health problems. Internet of things (IoT) is an amalgamation of pervasive computing, intelligent processing, and real-time response systems. Mechanics, devices, sensors make this machine-to-machine communication a feasible solution to dynamic requirements of tech-aspiring world. This chapter highlights the possibilities of further empowerment of healthcare systems using IoT or in other words IoMT (internet of medical things). Nanotechnology-driven IoT development or internet of nano things (IoNT) has become an added advantage in healthcare applications. So, IoNT with IoMT is another exciting research prospect of the near future. This chapter introduces a technique used in healthcare applications, PUF (physical unclonable function), and it is technique for solving many problems related to privacy and security. Security of data transmission, issues pertinent to reliability, and inter-operability are inherently affecting the progress of IoT-based healthcare systems. This chapter of focuses upon these issues and feasible solutions viewed from the dimension of technology-driven healthcare costs in the modern world and economic implications. The treatment used in this chapter will be more interesting for the casual readers. The analysis of IoMT implications in the near future will be helpful to the ardent learners. The research dimensions of IoT-empowered healthcare systems will add value to the thought process of young researchers.

An effective hybrid search mode for multi-objective optimization with constraints

In this paper a new search strategy for multi-objective optimization (MOO) with constraints is proposed based on a hybrid search mode (HSM). The search processes for feasible solutions and optimal solutions are executed in a mixed way for the existing methods. With regard to HSM, a hybrid search mode is proposed, which consists of two processes: Feasibility search mode (FSM) and optimal search mode (OSM). The executions of these two search modes are independent relatively and also adjusted according to the population distribution. In the early stage, FSM plays the leading role for exploring the feasible space since most of the individuals are infeasible. With the increase of the feasible individuals, OSM is the primary operation for the search of optimal individuals. The proposed method is simple to implement and need few extra parameter tuning. The handing method of constraints is tested on several multi-objective optimization problems with constraints. The remarkable results demonstrate its effectiveness and good performance.

Penerapan Bat Algorithm Dalam Penyelsaian Kasus Travelling Salesman Problem (TSP) Pada Internship Program

This optimization is an optimization case that organizes all possible and feasible solutions in discrete form. One form of combinatorial optimization that can be used as material in testing a method is the Traveling Salesman Problem (TSP). In this study, the bat algorithm will be used to find the optimum value in TSP. Utilization of the Metaheuristic Algorithm through the concept of the Bat Algorithm is able to provide optimal results in searching for the shortest distance in the case of TSP. Based on trials conducted using data on the location of student street vendors, the Bat Algortima is able to obtain the global minimum or the shortest distance when compared to the nearest neighbor method, Hungarian method, branch and bound method.