Metode Improved Gaussian Sampling pada Algoritma Rapidly Exploring Random Tree*

Penelitian ini bertujuan merancang metode improved gaussian sampling dan mengimplementasikan pada algoritma Rapidly Exploring Random Tree* (RRT*). Perancangan yang dilakukan menggunakan bahasa pemograman Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW). Improved gaussian sampling merupakan pengembangan dari metode gaussian sampling dengan menambahkan jumlah sampling. Jumlah sampling yang digunakan pada metode ini yaitu sejumlah 10 sampling. Untuk mengukur performansi dari metode sampling yang diusulkan, maka kami melakukan perbandingan performansi metode improved gaussian sampling, gaussian sampling dan random sampling. Berdasarkan hasil pengujian improved gaussian sampling didapatkan rata-rata nilai path cost dan waktu komputasi senilai: clutter sepanjang 8,89 dengan waktu 40,05; narrow sepanjang 12,58 dengan waktu 40,03 dan trap sepanjang 9,93 dengan waktu 40,04. Sedangkan hasil pengujian gaussian sampling didapatkan: clutter sepanjang 10 dengan waktu 40,09; narrow sepanjang 13,53 dengan waktu 40,03 dan trap sepanjang 10,95 dengan waktu 40,12. Hasil pengujian random sampling didapatkan: clutter sepanjang 10,86 dengan waktu 0,03; narrow sepanjang 14,82 dengan waktu 0,25 dan trap sepanjang 11,71 dengan waktu 0,21. Disimpulkan bahwa pada algoritma RRT* dengan menggunakan metode improved gaussian sampling menghasilkan performansi yang lebih baik dibandingkan algoritma RRT* yang menggunakan metode sampling lainnya. Hasil perbandingan pengukuran berdasarkan nilai sampling didapatkan rata-rata nilai path cost rata-rata 10,12 dengan jumlah sampling hanya 1 dan nilai path cost terpendek 8,9 dengan jumlah sampling 10. Berdasarkan pengukuran tersebut didapatkan semakin banyak jumlah sampling yang diberikan maka nilai path cost yang dihasilkan optimal.

Analisis Performansi Algoritma Informed Rapidly-exploring Random Tree* Dengan Metode Boundary Sampling dan Metode Goal Biasing Sampling

Perencanaan jalur merupakan salah satu program penunjang untuk robotika atau kendaraan. Pada perencanaan jalur dibutuhkan algoritma yang tepat agar mendapatkan hasil yang optimal. Algoritma Informed Rapidly-exploring Random Tree* (Informed-RRT*) merupakan algoritma untuk perencanaan jalur yang berbasis sampling atau pengambilan sampel acak pada ruang pencarian. Pada penelitian ini akan dilakukan analisis performansi algoritma Informed-RRT* dengan metode boundary sampling dan metode goal biasing sampling. Tujuan penelitian ini yaitu membandingkan algoritma Informed-RRT* metode boundary sampling dengan Informed-RRT* metode goal biasing sampling untuk mendapatkan algoritma dengan performansi terbaik dalam melakukan perencanaan jalur. Pengujian dilakukan berbasis simulasi menggunakan software Labview. Dalam pengujian ini digunakan beberapa kasus lingkungan obstacle yang ada, yaitu lingkungan narrow, trap dan clutter. Kriteria untuk menentukan performansi yang optimal yaitu jarak jalur (path cost), waktu komputasi, dan jumlah node. Dari hasil yang didapatkan, nilai rata-rata algoritma Informed-RRT* metode boundary sampling menghasilkan waktu komputasi yang lebih cepat dan juga jumlah node yang lebih sedikit dibandingkan dengan Informed-RRT* metode goal biasing sampling, sedangkan untuk jarak jalur algoritma dengan kedua metode tersebut memiliki hasil yang hampir sama. Dapat disimpulkan bahwa algoritma Informed-RRT* metode boundary sampling memiliki performansi lebih optimal dibandingkan dengan algoritma Informed-RRT* metode goal biasing sampling. Kata kunci : Boundary Sampling, Goal Biasing Sampling, Informed-RRT*, Labview, Perencanaan jalur

A new path planning method based on sparse A* algorithm with map segmentation

Due to the complexity of map modeling, the massive computation and high redundancy of the traditional A* algorithm will greatly reduce the efficiency of pathfinding, resulting in huge performance consumption. Meanwhile, limited by neighborhood search strategy in grid map, the traditional A* algorithm is actually unable to achieve the optimal path in the global sense. To solve these problems, this paper proposes an improved A* algorithm based on graph preprocessing. First, the free space on the map was decomposed into several polygon regions using the improved convex decomposition method based on Maklink. Then, each region was coded into feature nodes according to A* algorithm. Finally, an optimal region passage was found based on the principle of A* algorithm, in which the global optimal path solution was obtained. Compared with the traditional A* algorithm and other classical path planning algorithms, the proposed algorithm has significant advantages in planning speed, path cost, stability, and completeness.

Disruption-Free Load Balancing for Aerial Access Network

A fundamental issue of 6G networks with aerial access networks (AAN) as a core component is that user devices will send high-volume traffic via AAN to backend servers. As such, it is critical to load balance such traffic such that it will not cause network congestion or disruption and affect users’ experience in 6G networks. Motivated by the success of software-defined networking-based load balancing, this paper proposes a novel system called Tigris, to load balance high-volume AAN traffic in 6G networks. Different from existing load balancing solutions in traditional networks, Tigris tackles the fundamental disruption-resistant challenge in 6G networks for avoiding disruption of continuing flows and the control-path update challenge for limiting the throughput of updating load balancing instructions. Tigris achieves disruption-free and low-control-path-cost load balancing for AAN traffic by developing an online algorithm to compute disruption-resistant, per-flow load balancing policies and a novel bottom-up algorithm to compile the per-flow policies into a highly compact rule set, which remains disruption-resistant and has a low control-path cost. We use extensive evaluation to demonstrate the efficiency and efficacy of Tigris to achieve zero disruption of continuing AAN flows and an extremely low control-path update overhead, while existing load balancing techniques in traditional networks such as ECMP cause high load variance and disrupt almost 100% continuing AAN flows.

The research on a Multipath Forwarding Strategy in Wireless Sensor Networks

Wireless communication signals are unstable, WSN network topology changes frequently, network dynamics are strong, and single path forwarding is difficult to provide high quality of service. A vector address-based end-to-end multi-path forwarding mechanism and path recovery algorithm are proposed. Although a single path cannot continuously guarantee network connection, multiple paths together can provide network connection with high reliability. In this method, the caller applies for and stores multiple relatively independent communication paths, and ranks them for use according to the path cost. The experimental results show that the performance of the above method is significantly improved.

Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN

The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.

Simulation Study on Coverage Path Planning of Autonomous Tasks in Hilly Farmland Based on Energy Consumption Model

The hilly farmland in China is characterized by small farmland areas and dense farmland distribution, and the working environment is three-dimensional topographic farmland, so the working conditions in the field are relatively complex. In this working environment, the coverage path planning technique of a farmland autonomous task is harder than that of 2D farmland autonomous task. Generally, the path planning problem of 2D farmland is to construct the path cost model to realize the planning of agricultural machinery driving route, while for the path planning problem of three-dimensional terrain farmland in the hilly region, this paper proposes a covering path planning scheme that meets the requirements of autonomous work. Based on the energy consumption model, the scheme searches the optimal driving angle of agricultural machinery, prioritizes solutions to the problem of covering path planning within the scattered fields in the working area, and then searches through the genetic algorithm for the optimal order of traversing the paths of each field to complete the coverage path planning in the working area. On the one hand, the scheme optimizes the planning route in the fields from the angle of optimal energy consumption; on the other hand, through the genetic algorithm, the fields are connected in an orderly manner, which solves the comprehensive problems brought by the unique agricultural environment and farming system in China’s hilly areas to the agricultural machinery operation. The algorithm program is developed according to the research content, and a series of simulation experiments are carried out based on the program using actual farmland data and agricultural machinery parameters. The results show that the planned path obtained at the cost of energy consumption has a total energy consumption of 4771897.17J, which is 17.4% less energy consumption than the optimal path found by the path cost search; the optimization effect is evident.

LOCK-FREE MULTITHREADED SEMI-GLOBAL MATCHING WITH AN ARBITRARY NUMBER OF PATH DIRECTIONS

This paper describes an efficient implementation of the semi-global matching (SGM) algorithm on multi-core processors that allows a nearly arbitrary number of path directions for the cost aggregation stage. The scanlines for each orientation are discretized iteratively once, and the regular substructures of the obtained template are reused and shifted to concurrently sum up the path cost in at most two sweeps per direction over the disparity space image. Since path overlaps do not occur at any time, no expensive thread synchronization will be needed. To further reduce the runtime on high counts of path directions, pixel-wise disparity gating is applied, and both the cost function and disparity loop of SGM are optimized using current single instruction multiple data (SIMD) intrinsics for two major CPU architectures. Performance evaluation of the proposed implementation on synthetic ground truth reveals a reduced height error if the number of aggregation directions is significantly increased or when the paths start with an angular offset. Overall runtime shows a speedup that is nearly linear to the number of available processors.