Optimal Path Generation Research Articles

Pareto Optimal Path Generation Algorithm in Stochastic Transportation Networks

Routing problems play a crucial part in urban transportation network operation and management. This study addresses the problem of finding a set of non-dominated shortest paths in stochastic transportation networks. Instead of the previous practice of assuming the travel time variability to be tracked by a known probability density function, it is extracted from the existing correlation between the traffic flow and the corresponding links' time. The time horizon is divided into time intervals/slots in which the network is assumed to experience a static traffic equilibrium with different traffic conditions for each slot. Starting with Priori demand information, prior generated paths, and a chosen traffic assignment method, the proposed methodology conducts successive simulations to the network intervals. It manages to draw both links and paths probability distribution of their travel time considering the correlation among them. Then, multi-objective analysis is conducted on the generated paths to produce the Pareto-optimal set for each demand node pair in the network. Numerical studies are conducted to show the methodology efficiency and generality for any network. The expected travel time and the reliability could be drawn for each path in the network.

A distributed congestion avoidance routing algorithm in mega-constellation network with multi-gateway

Recently several large-scale constellation networks have been proposed, requiring inter-satellite links and multiple gateways to be equipped. The gateway assignment and traffic congestion avoidance issues are of fundamental importance for the network. In this work, we establish the network model for mega-constellation network with multi-gateway by introducing the concept of satellite router and satellite gateway. We propose a distributed routing algorithm consisting of three processes: 1) Gateway assignment; 2) Path candidate region partitioning and 3) Optimal path generation. In Optimal path generation, Longer Side Priority (LSP) strategy is proposed, aiming at maximizing the path searching space. By solving the path survival problem, LSP strategy is proved to perform better in congestion avoidance. Through simulation, we verify the routing algorithm and find the significant effects of seam movement in network performance. Results suggest that the proposed strategy performs better in load balance and congestion avoidance compared with the minimum delay strategy.

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners’ requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

AI based combined scheduling and motion planning in flexible robotic assembly lines

One of the latest trends in EU manufacturing research, is to increase reconfigurability of production systems by introducing mobile dual arm workers able to collaborate with humans in dynamic changing environments. The flexibility of the system is increased by the ability of the mobile robots to easily re-locate themselves inside the shopfloor for performing various assembly operations. This paper presents a multi – level decision making framework targeting on the dynamic work balancing among the human operators and robot resources. The proposed mechanism exploits Artificial Intelligence techniques for generating efficient task sequences while ensuring optimal and collision free path generation for the assigned resources. The focus is given on the integration of multi- criteria decision-making mechanisms with mobile robots’ low-level planning modules for deriving effective reconfiguration plans considering the shopfloors real time status. The developments have been tested in a case study from the automotive industry.

Effect of information contagion during train service disruption for an integrated rail-bus transit system

Disruption in urban rail services can severely affect passengers’ daily travel activities. Transit agencies and operators, besides providing reliable urban transit services and maintaining rail infrastructures, are also interested in developing effective and efficient disruption management strategies. This paper investigates the effect of information provision and contagion under train service disruption for typical disruption management strategies adopted by transit operators. A dynamic modelling framework is proposed for flow assignment within an integrated rail-bus transit network. Information-based user equilibrium, time-dependent line station waiting time estimation, and dynamic optimal travel path generation are integrated into the network loading procedure. A case study based on the Singapore public transit network is presented. Passenger evolution at selected bus stops around the disrupted train stations, the average time loss and the behavior of mode switch are analyzed under different levels of passenger information awareness. The computational results show the influence of information penetration rate and spread speed on the network performance and demonstrate the crucial role of information awareness in passengers’ travel behaviors during disruption. It also shows that our proposed methodology can comprehensively model the train service disruption in terms of the passenger behaviors, the disruption information contagion mechanism and the disruption effects, which support the public transit agencies to evaluate the system performance based on different information provision plans in order to enhance the disruption management.

Generation of Optimal Testing Paths using Anti –Ant Colony Algorithm

Generation of Optimal Testing Paths using Anti –Ant Colony Algorithm

Multi-Objective Optimal Design of Obstacle-Avoiding Two-Dimensional Steiner Trees With Application to Ascent Assembly Engineering

We present an effective optimization strategy that is capable of discovering high-quality cost-optimal solution for two-dimensional (2D) path network layouts (i.e., groups of obstacle-avoiding Euclidean Steiner trees) that, among other applications, can serve as templates for complete ascent assembly structures (CAA-structures). The main innovative aspect of our approach is that our aim is not restricted to simply synthesizing optimal assembly designs with regard to a given goal, but we also strive to discover the best tradeoffs between geometric and domain-dependent optimal designs. As such, the proposed approach is centered on a variably constrained multi-objective formulation of the optimal design task and on an efficient coevolutionary solver. The results we obtained on both artificial problems and realistic design scenarios based on an industrial test case empirically support the value of our contribution to the fields of optimal obstacle-avoiding path generation in particular and design automation in general.

A modelling framework for automotive software design and optimal test path generation

A modelling framework for automotive software design and optimal test path generation

Distributed Optimal Path Generation Based on Delayed Routing in Smart Camera Networks

With the rapid development of urban traffic system and fast increasing of vehicle numbers, the traditional centralized ways to generate the source-destination shortest path in terms of travel time(the optimal path) encounter several problems, such as high server pressure, low query efficiency, roads state without in-time updating. With the widespread use of smart cameras in the urban traffic and surveillance system, this paper maps the optimal path finding problem in the dynamic road network to the shortest routing problem in the smart camera networks. The proposed distributed optimal path generation algorithm employs the delay routing and caching mechanism. Real-time route update is also presented to adapt to the dynamic road network. The test result shows that this algorithm has advantages in both query time and query packet numbers.

Path planning of mechanical polishing process for freeform surface with a small polishing tool

Products with freeform surface are widely applied in industries, and the surface quality plays an important role in order to fulfill the targeted functions. As polishing path of small polishing tool affects the polishing removal function considerably, it is highly necessary to study the polishing path of freeform surface for obtaining good polishing efficiency and well-proportioned surface quality. By combining the Preston polishing removal function, the material removal model of small polishing tool under the control of constant polishing force and pressure is established. Based on this model, the material removal functions of scan line, Archimedean spiral, and Hilbert fractal polishing path are derived. The simulation results show that the Hilbert fractal polishing path has the best comprehensive performance. By using the projection relation of differential geometry, the optimal path generation algorithm of the Bezier surface based on Hilbert fractal polishing path is established. The polishing experiments are conducted on a self-developed polishing machine which is based on a parallel manipulator. The experimental results demonstrate that the surface roughness is improved from level 9 to level 11.

Intelligent Vehicle Local Planning Based on Optimized Path Generation and Selection

Local planning is of great importance to intelligent vehicle driving at high speed where the traffic environment is complex. The local planning must satisfy the performance of good real-time and guarantee feasibility and safety. In this paper, a novel local planning method based on the optimization of curve generation and selection is proposed. By optimizing the curve parameter model, we can get the feasible path which is satisfied with vehicle dynamics constraints. Then taking into account the obstacle constraint, we apply this method to generate a cluster of candidate paths used for tracking global path. Next, we use optimization indicators presented in this paper to choose the suitable desired local path. Experimental results show that: using our method, the change in the curvature of the generated desired local path is slight and it meet the requirement of safety and control smoothness. Besides, this method can satisfy the meeting of tracking global path. What’s more, in real traffic environment, this method reflects a good performance in real-time and safety.

Optimal air-move path generation based on MMAS algorithm

Air-move paths are executed by machine tools to cut 2D patterns that must retract (or turn off) when travelling from one pattern to the next. These retractions are nonproductive and hence their travelling distances should be minimised. For instance, reducing air-move time can increase the efficiency of cloth cutting in the garment industry. In this paper, a novel algorithm is proposed to solve this problem. In several past works, this problem is formulated as an instance of the generalised travelling salesman problem. We divide this problem into two independent sub-optimal problems (pattern cutting order and entry/exit cutting point) and iteratively solve them using a max–min ant system. This strategy can greatly narrow down the search space. We also perturb the pattern cutting order to avoid dowelling at a local minimum. Experiments show that the proposed algorithm is able to obtain near-optimal solutions, with the results beating the three state-of-the-art algorithms that were used as the benchmarks.

Tool path generation framework for accurate manufacture of complex 3D sheet metal parts using single point incremental forming

The prototyping of complex sheet metal parts using single point incremental forming (SPIF) requires the generation of optimal tool paths and/or tool path sequences that ensure that the formed part is within geometric design specifications. The presence of a multitude of features on complex parts leads to multiple inaccuracy inducing phenomena occurring simultaneously due to interactions between the features. This paper proposes a network analysis methodology using topological conceptual graphs to capture the effects of different phenomena on the final accuracy of a sheet metal part manufactured by SPIF. Using this framework optimized tool paths can be generated that compensate for the inaccuracy inducing behavior. Tool path generation algorithms to create partial tool paths that account for the accuracy of specific features in the part based on the proposed framework are also presented. Finally, the creation of integrated tool paths maintaining complementarity between tool paths and desired continuity behavior using non-uniform cubic B-splines is illustrated. A number of case studies demonstrating the applicability of the integrated framework are discussed, where the maximum deviations in the part are significantly reduced and the average absolute deviations for the complete part are brought down to less than 0.5mm.

Optimal path choice in railway passenger travel network based on residual train capacity.

Passenger's optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.

Advanced feature detection algorithms for incrementally formed sheet metal parts

New advanced algorithms for the detection of detailed features in parts formed by single point incremental forming (SPIF) were developed. The features were detected in STL part specifications that took into account the geometry, curvature, location, orientation and process parameters to detect 33 different features within an expert CAPP system for SPIF. The detection process was facilitated by using multi-level edge segmentation routines that first created a frame of edge features. Within this frame, the remaining features were then detected using region growing algorithms. The results show successful detection for a number of test cases. A case study for a double curved hemisphere illustrates the generation of optimal tool paths using compensation for the detected features in the part. These tool paths lead to the improvement in the accuracy of the formed sheet metal parts.

Optimal egress time calculation and path generation for large evacuation networks

Finding the optimal clearance time and deciding the path and schedule of evacuation for large networks have traditionally been computationally intensive. In this paper, we propose a new method for finding the solution for this dynamic network flow problem with considerably lower computation time. Using a three phase solution method, we provide solutions for required clearance time for complete evacuation, minimum number of evacuation paths required for evacuation in least possible time and the starting schedules on those paths. First, a lower bound on the clearance time is calculated using minimum cost dynamic network flow model on a modified network graph representing the transportation network. Next, a solution pool of feasible paths between all O-D pairs is generated. Using the input from the first two models, a flow assignment model is developed to select the best paths from the pool and assign flow and decide schedule for evacuation with lowest clearance time possible. All the proposed models are mixed integer linear programing models and formulation is done for System Optimum (SO) scenario where the emphasis is on complete network evacuation in minimum possible clearance time without any preset priority. We demonstrate that the model can handle large size networks with low computation time. A numerical example illustrates the applicability and effectiveness of the proposed approach for evacuation.

Knowledge-based approach to optimal path generation for an internet-enabled robot

In this paper, a knowledge-based control system for generating an optimal path with obstacle avoidance for an internet-enabled robot is proposed. The novel knowledge-based control system is used for guiding a robot to move from an initial location to a final destination with free collision based on the issued control commands from remote users. In order to address general environment, the obstacles of the robot include static objects and free moving objects. For simplifying the problems, relative distance and velocity have been employed to transform a dynamic object into static one. Simulation results demonstrate effectiveness and feasibility of the proposed knowledge-based control system for the internet-enabled robot.

Multi-robot path planning using co-evolutionary genetic programming

Motion planning for multiple mobile robots must ensure the optimality of the path of each and every robot, as well as overall path optimality, which requires cooperation amongst robots. The paper proposes a solution to the problem, considering different source and goal of each robot. Each robot uses a grammar based genetic programming for figuring the optimal path in a maze-like map, while a master evolutionary algorithm caters to the needs of overall path optimality. Co-operation amongst the individual robots’ evolutionary algorithms ensures generation of overall optimal paths. The other feature of the algorithm includes local optimization using memory based lookup where optimal paths between various crosses in map are stored and regularly updated. Feature called wait for robot is used in place of conventionally used priority based techniques. Experiments are carried out with a number of maps, scenarios, and different robotic speeds. Experimental results confirm the usefulness of the algorithm in a variety of scenarios.

Evolutionary computation approaches to the Curriculum Sequencing problem

Within the field of e-Learning, a learning path represents a match between a learner profile and his preferences from one side, and the learning content presentation and the pedagogical requirements from the other side. The Curriculum Sequencing problem (CS) concerns the dynamic generation of a personal optimal learning path for a learner. This problem has gained an increased research interest in the last decade, as it is not possible to have a single learning path that suits every learner in the widely heterogeneous e-Learning environment. Since this problem is NP-hard, heuristics and meta-heuristics are usually used to approximate its solutions, in particular Evolutionary Computation approaches (EC). In this paper, a review of recent developments in the application of EC approaches to the CS problem is presented. A classification of these approaches is provided with emphasis on the tools necessary for facilitating learning content reusability and automated sequencing.

Distributed multicast multichannel paths

Supporting multimedia applications in QoS-aware multicast deployment has become an important research dimension in recent years. Future communication networks will face an increase in traffic driven by multimedia applications with stringent requirements in the following important functions: (1) nodes and links used distributing, (2) packets duplication distributing, (3) QoS supporting, (4) multichannel routing. For improving these four functions, in this paper we propose a new polynomial time algorithm, named Nodes Links Distributed-Multicast Multichannel Routing (NLD-MMR), based on the Constraint-Based Routing (CBR) and Linear Programming (LP). The new algorithm by constructing Distributed Multicast Multichannel Paths (DMMCP) can distribute or compact both paths and traffic. Our simulation study shows that the proposed algorithm, as compared to other available algorithms, performs well and constructs a new generation of optimal paths with the best cost and efficiency.