Path Computation Time Research Articles

Robot Path Planning and Tracking with the Flower Pollination SearchOptimization Algorithm

Robot path planning and tracking involve two critical aspects of autonomous navigation systems: determining an optimal route for the robot to follow and ensuring that it accurately adheres to this path in real-time. Path planning focuses on generating a feasible and efficient trajectory from a starting point to a destination while considering obstacles, dynamic environments, and constraints. This paper investigates the effectiveness of the Flower Pollination Search Optimization (FPSO) algorithm for robot path planning and compares its performance with traditional algorithms such as A* Algorithm, Dijkstra, and Rapidlyexploring Random Tree (RRT). The FPSO algorithm was evaluated across three distinct scenarios, demonstrating superior performance in terms of path length, computation time, and path smoothness. In Scenario 1, FPSO achieved an optimized path length of 10.5 meters with a computation time of 3.2 seconds, a path smoothness score of 8.9, and a path efficiency of 95%. In Scenario 2, FPSO resulted in a path length of 12.3 meters and a computation time of 3.5 seconds, with a smoothness score of 8.7 and an efficiency of 93%. Scenario 3 showed FPSO's best performance, with a path length of 9.8 meters, computation time of 3.0 seconds, a smoothness score of 9.0, and a path efficiency of 96%. Comparatively, the A* Algorithm, Dijkstra, and RRT exhibited longer path lengths and higher computation times, with lower smoothness scores and efficiencies.

Experimental evaluation of a latency-aware routing and spectrum assignment mechanism based on deep reinforcement learning

The introduction of futuristic and challenging use cases of 5G and 6G communications will demand strict requirements in terms of high bandwidth and low latency. Optical backbone networks need to tackle these new network scenarios by offering highly efficient, flexible, and scalable technologies and solutions. In this context, elastic optical networks (EONs) have been recognized as a promising technological transport infrastructure for the future Internet since they can manage the optical spectrum with enhanced flexibility and efficiency. The service provisioning in EONs is a challenging issue to tackle since the routing and spectrum assignment (RSA) is characterized by a high degree of complexity. This work presents an approach for RSA in EONs leveraging the advantages of deep reinforcement learning (DRL) solutions. The devised approach jointly considers the constraints imposed by the optical technologies and the demanded connectivity service requirements (i.e., guaranteed bandwidth and maximum end-to-end latency) when computing and selecting the optical path and spectral resources. We first evaluate our approach through simulation experiments considering two reference network topologies, demonstrating its effectiveness in reducing the bandwidth blocking ratio, the path computation time, and the number of rejected connectivity services requiring lower latencies when compared to a baseline k-shortest path routing and first-fit spectrum allocation algorithm. Then, the trained DRL agent is integrated within a real proof of concept to attain an ML-assisted SDN control plane in the CTTC ADRENALINE testbed. The attained performance improvements highlight the potential benefits brought by using DRL mechanisms and its feasible integration within production EON transport infrastructures.

Research on Path-Planning Algorithm Integrating Optimization A-Star Algorithm and Artificial Potential Field Method

A fusion pathfinding algorithm based on the optimized A-star algorithm, the artificial potential field method and the least squares method is proposed to meet the performance requirements of path smoothing, response speed and computation time for the path planning of home cleaning robots. The fusion algorithm improves the operation rules of the traditional A-star algorithm, enabling global path planning to be completed quickly. At the same time, the operating rules of the artificial potential field method are changed according to the path points found by the optimal A-star algorithm, thus greatly avoiding the dilemma of being trapped in local optima. Finally, the least squares method is applied to fit the complete path to obtain a smooth path trajectory. Experiments show that the fusion algorithm significantly improves pathfinding efficiency and produces smoother and more continuous paths. Through simulation comparison experiments, the optimized A-star algorithm reduced path-planning time by 60% compared to the traditional A-star algorithm and 65.2% compared to the bidirectional A-star algorithm path-planning time. The fusion algorithm reduced the path-planning time by 65.2% compared to the ant colony algorithm and 83.64% compared to the RRT algorithm path-planning time.

Research on a Random Route-Planning Method Based on the Fusion of the A* Algorithm and Dynamic Window Method

Path planning is a hot topic at present. Considering the global and local path planning of mobile robot is one of the challenging research topics. The objective of this paper is to create a rasterized environment that optimizes the planning of multiple paths and solves barrier avoidance issues. Combining the A* algorithm with the dynamic window method, a robo-assisted random barrier avoidance method is used to resolve the issues caused by collisions and path failures. Improving the A* algorithm requires analyzing and optimizing its evaluation function to increase search efficiency. The redundant point removal strategy is then presented. The dynamic window method is utilized for local planning between each pair of adjacent nodes. This method guarantees that random obstacles are avoided in real-time based on the globally optimal path. The experiment demonstrates that the enhanced A* algorithm reduces the average path length and computation time when compared to the traditional A* algorithm. After fusing the dynamic window method, the local path is corrected using the global path, and the resolution for random barrier avoidance is visualized.

Optimizing the Path of Plug Tray Seedling Transplanting by Using the Improved A* Algorithm

In greenhouse nurseries, one of the important tasks of the automatic transplanter is replanting missing or bad seedling holes with healthy seedlings. This requires the transplanter to spend significant time moving between the supply trays and target trays during replanting. The diversity and complexity of the transplanting routes affect transplanter efficiency. Path planning method can find a better path for the manipulator and improve the efficiency of transplantation. The A* algorithm (A*), which is one of the optimal path search algorithms, is often used in practical applications of path planning. In this paper, the heuristic function of the A* is optimized by the ant colony algorithm (ACA), and an improved A* algorithm (Imp-A*) is obtained. Simulation tests and transplanting trials of Imp-A*, A*, ACA, Dijkstra (DA), and common sequence method (CSM) were carried out using 32-, 50-, 72-, and 128-hole plug trays. The results show that Imp-A* inherits the advantages of A* and ACA in terms of path planning length and computation time. Compared to A*, ACA, DA, and CSM, the transplanting time for Imp-A* was reduced by 2.4%, 12.84%, 11.63%, and 14.27%, respectively. In just six trays of transplanting tasks, Imp-A* saves 60.91 s compared to CSM, with an average time saving of 10.15 s per tray. The combination optimization algorithm has similar application prospects in agriculture.

A Quasistraight Line Routing Protocol for Square Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network (WSN) are both energy-constrained and vulnerable to faults and disasters. Communication between the sensor nodes is generally hop-by-hop, and the nodes are distributed throughout the area to be covered. Broadcast-based routing protocols are not preferable in sensor networks since broadcasting is considered costly in terms of battery power consumption. In this paper, a digital quasistraight line segment- (DQSS-) based approach is employed for the detection of quasistraight line segments, i.e., for quasistraight path finding between WSN sensors arranged in a square grid. Comparative results show that the method is comparable with the best-known straight line finding algorithm in terms of path lengths and computation time. Moreover, the proposed method is capable of avoiding dead nodes by updating DQSS parameters dynamically during path finding. Hence, the proposed method is promising to be used in WSN square grids as a quasistraight line routing protocol.

Improved RRT-Connect Algorithm for Urban low-altitude UAV Route Planning

Path length and computation time in path planning seriously affect the safety and efficiency of urban low-altitude UAV flight. This paper improves RRT-connect Algorithm for path planning, the search step length, parent node selection and branch orientation selection are optimized to reduce the path length and algorithm time. Starting from the shortest parent node of the two trees, track search is carried out in the Middle Direction, the search step length is changed according to the field of view, the search step length is cut down quickly through the open field, and the search step length in narrow space is reduced, at the same time, the growth direction of the main branch of the search tree is controlled by the angle to reduce the probability of the UAV turning sharply. Simulation experiments show that the proposed algorithm has better time and space advantages and convergence than the current popular algorithm, the comparison results show that the optimized Algorithm RRT-connect is superior to the current flow Algorithms RTT-extend and Lazy-RTT in terms of the time consumption of short-time fast search and the length of path generation.

A Map-Reduce Approach for the Dijkstra Algorithm in SDN Over Osmotic Computing Systems

Osmotic Computing represents a glue solution able to manage the deployment and orchestration of interconnected microelements across heterogeneous physical and virtual infrastructures (e.g., IoT, Edge and Cloud nodes) according to the behavior of hardware and software components during the time. The adoption of Osmotic Computing is challenging, but addressing networking issues is a key research topic due to the emergence of new problems in terms of QoS requirements. In this paper, we analyze how to exploit well-known networking solutions, such as the Dijkstra’s algorithm, and Big Data oriented technologies, such as the Hadoop and MapReduce, to provide efficient newtorking functionalities in Osmotic Computing. In particular, our objective is to minimize the routing path computation time in the software defined network (SDN) at the basis of microelement networking, as well as to ensure a global view and a high level of dynamism of our network topology. To accomplish this task, we process routing tables through a MapReduce based implementation of the Dijkstra’s algorithm whenever a topology change occurs, and we export routing results into the SDN. Our experimental results show that our networking strategy drastically reduces the best path computation time whenever the network of microelements is very large.

DeepMDR: A Deep-Learning-Assisted Control Plane System for Scalable, Protocol-Independent, and Multi-Domain Network Automation

This article discusses DeepMDR, which is a deep learning (DL)-assisted control plane (CP) system to realize scalable and protocol-independent path computation in multi-domain packet networks. We develop DeepMDR based on ONOS, make it support protocol-oblivious forwarding (POF) in the data plane, facilitate a hierarchical CP architecture for multi-domain operations, and propose a DL model to achieve fast and high-quality path computation in each domain. Simulation results verify that our DL-assisted routing module achieves better trade-off between path computation time and routing performance than existing approaches. The effectiveness of our proposed DeepMDR is also demonstrated with experiments, which show that it serves inter-domain flow requests quickly with a processing capacity of ~166,000 messages/s or higher.

Beyond Euclidean Distance for Error Measurement in Pedestrian Indoor Location

Indoor positioning systems (IPSs) suffer from a lack of standard evaluation procedures enabling credible comparisons: this is one of the main challenges hindering their widespread market adoption. Traditionally, accuracy evaluation is based on positioning errors defined as the Euclidean distance between the true positions and the estimated positions. While Euclidean is simple, it ignores obstacles and floor transitions. In this article, we describe procedures that measure a positioning error defined as the length of the pedestrian path that connects the estimated position to the true position. The procedures apply pathfinding on floor maps using visibility graphs (VGs) or navigational meshes (NMs) for vector maps and fast marching (FM) for raster maps. Multifloor and multibuilding paths use the information on vertical in-building communication ways and outdoor paths. The proposed measurement procedures are applied to position estimates provided by the IPSs that participated in the EvAAL-ETRI 2015 competition. Procedures are compared in terms of pedestrian path realism, indoor model complexity, path computation time, and error magnitudes. The VGs algorithm computes shortest distance paths; NMs produce very similar paths with significantly shorter computation time; and FM computes longer, more natural-looking paths at the expense of longer computation time and memory size. The 75th percentile of the measured error differs among the methods from 2.2 to 3.7 m across the evaluation sets.

A Hierarchical Distributed Control Plane for Path Computation Scalability in Large Scale Software-Defined Networks

Given the shortcomings of traditional networks, software-defined networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability, and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. This paper proposes HiDCoP, a distributed high-performance control plane for path computation in large-scale SDNs along with its related solutions. HiDCoP employs a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that HiDCoP outperforms existing schemes in terms of path computation time, end-to-end delay, and transmission overhead.

An Intelligent Gain based Ant Colony Optimisation Method for Path Planning of Unmanned Ground Vehicles

In many of the military applications, path planning is one of the crucial decision-making strategies in an unmanned autonomous system. Many intelligent approaches to pathfinding and generation have been derived in the past decade. Energy reduction (cost and time) during pathfinding is a herculean task. Optimal path planning not only means the shortest path but also finding one in the minimised cost and time. In this paper, an intelligent gain based ant colony optimisation and gain based green-ant (GG-Ant) have been proposed with an efficient path and least computation time than the recent state-of-the-art intelligent techniques. Simulation has been done under different conditions and results outperform the existing ant colony optimisation (ACO) and green-ant techniques with respect to the computation time and path length.

Critical Path Reduction of Distributed Arithmetic Based FIR Filter

Operating speed, which is reciprocal of critical path computation time, is one of the prominent design matrices of finite impulse response (FIR) filters. It is largely affected by both, system architecture as well as technique used to design arithmetic modules. A large computation time of multipliers in conventionally designed multipliers, limits the speed of system architecture. Distributed arithmetic is one of the techniques, used to provide multiplier-free multiplication in the implementation of FIR filter. However suffers from a sever limitation of exponential growth of look up table (LUT) with order of filter. An improved distributed arithmetic technique is addressed here to design for system architecture of FIR filter. In proposed technique, a single large LUT of conventional DA is replaced by number of smaller indexed LUT pages to restrict exponential growth and to reduce system access time. It also eliminates the use of adders. Selection module selects the desired value from desired page, which leads to reduce computational time of critical path. Trade off between access times of LUT pages and selection module helps to achieve minimum critical path so as to maximize the operating speed. Implementations are targeted to Xilinx ISE, Virtex IV devices. FIR filter with 8 bit data width of input sample results are presented here. It is observed that, proposed design perform significantly faster as compared to the conventional DA and existing DA based designs.

An Efficient Algorithm for Vehicle Guidance Combining Dijkstra and A Algorithm with Fuzzy Inference Theory

Most of the studies on vehicle navigation are based on the use of an existing path-searching algorithm. However, finding the optimum path requires a lot of computation, which is quite limited on mobile devices. To improve the efficiency of vehicle navigation systems, we need to develop a suboptimum path-searching technique to save on computation for car navigation systems. In this study, fuzzy theory is applied to the path-searching algorithm. The drivers' collected expertise is collected into the fuzzy rule base system. Combining fuzzy inference theory with Dijkstra's algorithm and A algorithm significantly reduces path computation time, and the result is more similar to drivers' actual driving habits. In the simulation in the northern area of Taiwan, the results of our experiments show our proposed method is either more efficient or has a shorter path than Dijkstra algorithm, A algorithm and other algorithms.

Application of Ant Colony Algorithm Based on Optimization Parameters in Equipment Material Transport

In order to solve the path selection problem in the transport of equipment and materials, while improving the quality of solutions, this paper uses ant colony algorithm based on optimization parameters to achieve. Through genetic algorithm to solve the parameters of ant colony algorithm, resulting in a better performance parameters. The experimental results show that ant colony algorithm based on optimization parameters has been improved on path length and computation time than the traditional ant colony algorithm.

A speedup technique for dynamic graphs using partitioning strategy and multithreaded approach

There are many pre-processing-based speedup techniques for shortest path problems that are available in the literature. These techniques have an increased demand because of large datasets in such applications such as roadmaps, web search engines and mobile data sets. Pre-processing for the Time-Dependent Shortest Path Problem is still a demanding process that involves graph or network partitioning strategy. Efficient pre-processing of graphs or networks reduces the shortest path computation time while parallelizing the pre-processing phase improves the speedup of the system. In this paper, a speedup technique called Recursive Spectral Bisection (RSB) combined with the Elliptic Convolution of the shortest path method is proposed for dynamic Time-Dependent networks. The same method has been parallelized, and the results are tested on three types of graphs. It is observed that the Time-Dependent RSB combined with the Elliptic Convolution of the shortest path method has no update time, and the Query Performance Loss (QPL) is reduced in planar and road networks compared to random networks. In road networks, the proposed method achieves an average speedup in a QPL of 140. The use of the Parallel speedup technique results in an average speedup in a QPL of more than 1 in the planar and road networks.

Experimental assessment of dynamic integrated restoration in GMPLS multi-layer (MPLS-TP/WSON) networks

We present the implementation of the GMPLS control plane functions and path computation algorithm deployed within the CTTC ADRENALINE testbed for the dynamic integrated restoration in multi-layer (MPLS-TP over WSON) networks. The experimental assessment is conducted in terms of the blocking probability, path computation time, restorability and restoration time.

The complexity of routing in hierarchical PNNI networks

We study the complexity of computing a route in a hierarchical PNNI network, with H levels of hierarchy, in which N nodes are grouped into clusters at each level. We determine cluster sizes that minimize an upper bound on the total time for all the path computations required to compute a route. Our model casts the problem as a nonlinear convex optimization problem, and employs nonlinear duality theory. We derive explicit closed form upper bounds on the minimum total path computation time, as a function of N, for H=2 and H=3, and show how the upper bound, and the optimal cluster sizes, can be computed for any H. We provide a conjecture on the complexity of PNNI routing for any H, and use this conjecture to determine the limit of the complexity as H→∞. We also prove that the minimum total path computation time is a non-increasing function of H. Our results provide counterexamples to a claim by Van Mieghem that a related top-down hierarchical routing method has lower computational complexity.

Optimal PNNI complex node representations for restrictive costs

The Private Network-to-Network Interface (PNNI) is a scalable hierarchical protocol that allows ATM switches to be aggregated into clusters called peer groups. To provide good accuracy in choosing optimal paths in a PNNI network, the PNNI standard provides a way to represent a peer group with a structure called the complex node representation. It allows the cost of traversing the peer group between any ingress and egress to be advertised in a compact form. Complex node representations using a small number of links result in a correspondingly short path computation time and therefore in good performance. It is, accordingly, desirable that the complex node representation contains as few links as possible. In earlier work, a method was presented for constructing the set of the optimal complex node representations in the restrictive and symmetric cost case, under the assumption of a restricted set of optimal paths and a corresponding minimal path computation time. Here this method is extended to constructing the set of the optimal complex node representations appropriate for deployment in a heterogeneous environment where no uniform policy is used to derive them. These representations are not confined by a reduced optimal path constraint, and consequently use the absolute minimum possible number of links, resulting in a minimum path computation time.

Optimal PNNI complex node representations for restrictive costs and minimal path computation time

The private network-to-network interface (PNNI) protocol, which specifies how topology information is to be distributed in an ATM network, allows ATM switches to be aggregated into clusters called peer groups. Outside of a peer group its topology is aggregated into a single logical node. This method can be applied recursively so that PNNI can hierarchically aggregate network topology state information. To provide good accuracy in choosing optimal paths in a PNNI network, the PNNI standard provides a way to represent a peer group with a structure called the complex node representation. It allows the cost of traversing the peer group between any ingress and egress to be advertised in a compact form. Complex node representations using a small number of links result in a correspondingly short path computation time and therefore in good performance. It is, therefore, desirable that the complex node representation contains as few links as possible. This paper considers the class of complex node representations for which the path computation time is minimal. It assumes that the path selection is based on restrictive costs, such as bandwidth, and considers the symmetric case. It presents a method for constructing the set of the optimal complex node representations in the sense that they use the minimum possible number of links. Central to the development of this method is the establishment of the optimal substructure property of the optimal complex node representations.