Optimal route re-planning for mobile robots: a massively parallel incremental A* algorithm

Interconnection Networks are widely used in many applications such as parallel and massively parallel systems, industrial applications, VLSI circuits, electronic devices and telephone switches etc. The realization of data communication in parallel system is possible all the way through interconnection networks. The whole performance of the system depends on the type of interconnection networks used. Interconnection networks with embed ability by some other interconnection structure, high resilience, simple routing, constant node degree, and scalability are highly desirable. We had introduced Octagon-Cell Networks (OCN), in the past research, which is a new interconnection network. In this research, we introduce and prove the fundamental theorems for finding the optimal path length between any pair of source and destination nodes in Octagon-Cell Network of any depth. The basic objective of these theorems (excluding Horizontal and Vertical Moves) to calculate the optimal path length, is to check whether any intermediate node exists along the optimal path, for which its line number or serial number matches with the line number or the serial number of destination node respectively. We also introduce the theorems to find the total number of horizontal lines, which is the maximum line number of a node, as well the total number of vertical lines, which is the maximum serial number of a node in Octagon-Cell Network of any depth. The theorems are proved mathematically and are tested successfully on all possible cases.

Profitable tour problem (PTP) belongs to the class of vehicle routing problem (VRP) with profits seeking to maximize the difference between the total collected profit and the total cost incurred. Traditionally, PTP involves single vehicle. In this paper, we consider PTP with multiple vehicles. Unlike the classical VRP that seeks to serve all customers, PTP involves the strategic-level customer selection so as to maximize the total collected profit and the operational-level route optimization to minimize the total cost incurred. Therefore, PTP is essentially the knapsack problem at the strategic level with VRP at the operational level. That means the evolutionary bi-level programming would be a suitable choice of methodology for solving the NP-hard PTP. Employing some evolutionary method to solve the bi-level program naively would undoubtedly be prohibitively expensive. We thus present in this paper the notion of knowledge adoption to approximate the initial solution to the lower-level optimization problem for a given trial solution of the upper-level decision variables. One may consider the knowledge adoption as a special case of knowledge transfer in which the transfer takes place within the same problem domain. Refining the approximate initial solution with local search forces it to quickly converge to some locally optimal solution. The better the estimation of the initial solution, the closer the local optimum will be to the global one. PTP finds its important application in the fields of transportation and logistics. In addressing last-mile problem using auction at the urban consolidation center (UCC), PTP plays a significant role in the winner determination problem (WDP) that follows. Empirical study demonstrates the efficacy of the proposed approach in solving the PTP-based WDP, yielding significantly higher profit, utilization, and service level compared to when the UCC adopts the conventional WDP based on multiple knapsack problem (i.e. the MKP-based WDP).

Reviews and experimental verification have found that existing solution methods can be used to solve UAV path planning problems, but each approximate solution has its own advantages and disadvantages. For example, ant colony algorithm easily falls into the local optimum in the process of realizing path planning. In order to prevent too low pheromones on the longer path and too high pheromones in the shorter path, the upper and lower limits of pheromones as well as their volatile factors are set to avoid falling into the local optimum. Secondly, multi-heuristic factors are introduced, and the overall length of the path serves as an adaptive heuristic function factor that determines the probability of state transition, which affects the probability of ants choosing the corresponding path. The experimental results show that the path length planned by the improved algorithm is 93.6% of the original algorithm, and the optimal path length variance is only 14.22% of the original algorithm. The improved ant colony algorithm shortens the optimal path length and solves the UAV path planning problem in terms of local optima. At the same time, multiple enlightening factors are introduced to increase the suitability of UAV for complex environments and improve the performance of UAV.

Logistics activity is both a major energy consumption user and an important CO₂ emission source. As logistics service activity rises in the city, it is crucial to improve the efficiency of fuel usage, as well as decrease the CO₂ emission of unit shipment using vehicle routing optimization. Based on the analysis of the characteristics of energy consumption and emission factors of CO₂ in the vehicle routing problem with multiple vehicles, this paper investigates the problem of fleet size and mixed vehicle routing problem considering CO₂ emissions cost (Green Fleet Size and Mixed Vehicle Routing Problem, G-FSMVRP ). A heuristic algorithm based on a genetic algorithm is given. Finally, a numerical simulation example is given to illustrate the optimization model and its algorithm. The findings show that: (1) the shortest route does not necessarily produce the least energy consumption; (2) compared with the traditional vehicle routing (based on shortest path), vehicle routing considering CO₂ emissions has a lower total cost, but longer distance travelled; and, (3) the genetic algorithm is an effective algorithm to solve the green vehicle routing problem.

Vehicle Routing Problem is a NP-Hard classical complex combinatorial problem described as task of determining efficient and shortest delivery or pickup routes to service several customers scattered in different geographical regions with a fleet of vehicles with additional predefined constraints to satisfy real-life scenarios. Vehicle Routing Problem has wide applications in Logistics and Transportation with growing economic importance. In Solutions of Vehicle Routing Problem maintaining the defined restrictions is of high Interest. Exact solutions of Vehicle Routing Problem can't be obtained due requirement of high computation time. Due to Genetic Algorithm's stochastic characteristics and efficiency in solving combinatorial problems it is used to find true and approximate solutions of Vehicle Routing Problem. For constrained variants of Vehicle Routing Problem feasible space is smaller than whole search space and Genetic Algorithm finds a solution with high precision that doesn't violate any of the constraints.

Making use of a newly available rapid-tuning diode laser operating at wavelengths up to 2.9 µm, an absorption-based temperature sensor was developed for in situ measurements in low-pressure flames. Based on the systematic analysis of H2O vapor transitions in the fundamental vibrational bands (ν1 and ν3) of H2O in the range of 2.5–3.0 µm, an optimal closely-spaced spectral line pair near 2.9 µm was selected for its temperature sensitivity in the range of 1000–2500 K. The narrow-linewidth room-temperature laser was scanned repetitively across these spectral features at 5 kHz, enabling fast, accurate temperature sensing. Use of the temperature sensor was investigated in low-pressure flames supported on a McKenna burner at 15, 25 and 60 Torr. To avoid absorption by the cold gases in the flame edges and the recirculation region between the burner and the vacuum chamber wall, a variable-path in situ probe was designed and an optimal path length was determined to accurately measure the flame centerline temperature. Different flame conditions were investigated to illustrate the potential of this sensor system for sensitive measurements of combustion temperature in low-pressure flames.

For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved.

For the requirements of indoor mobile robot planning global optimal path, traversing multi goals and dynamic obstacle avoidance, a multi-goal global dynamic path planning method is proposed in this paper, which is combined improved A * algorithm, traveling salesman problem (TSP), and dynamic window approach (DWA). The improved A* is used to plan the optimal collision-free path between two goals, and a key node selection scheme is used to remove the redundant nodes. And the TSP distance search square matrix is formed by using the optimal path length between the two goals, and the optimal order of traversing all goals is determined by ant colony algorithm. Then DWA evaluation function is constructed, which is considered with the global optimal path and speed, and real-time obstacle avoidance. On the basis of ensuring the global optimality of the planned path, all goals are traversed at the optimal speed, and the path is smoother, the ability of dynamic obstacle avoidance is improved. The simulation results demonstrate that compared with traditional methods, the proposed method is more efficient, smoother path, real-time dynamic obstacle avoidance, which has applications in autonomous navigation of multi targets mobile robots.

This paper presents an algorithm called CSP-IRPTW for the vehicles routing problem with time windows (VRPTW), which applies the PCP method (precedence constraint posting) used for models of scheduling as a CSP (constraint satisfaction problem). PCP involves the calculation of the shortest path in partial and global form, between pairs of nodes and among all the nodes respectively, in the graph that represents the VRPTW model. In order to apply PCP to VRPTW, the problem is treated as a CSP. The results show that the proposed search algorithm is efficient in the search for the global optimum for some problems.

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.

Navigation is an important function when we want to travel or reach a new place. Although the navigation can lead the people to go to the destination, the user always hopes that it can fast to achieve the site. However, the situation on the road is unpredictable, so that the vehicle routes need to adjust in real-time. Fortunately, the concept with mobile edge computing (MEC) and vehicle to anything (V2X) in the fifth-generation wireless network (5G), that can help us to achieve the goal. In this paper, we take the achieved destination time of vehicle, vehicle route, and retransmission times for network package into account for the multi-objective optimization (MOO) problem in 5G. Then, we through the Efficiently Vehicle Route Planning based on simulated annealing algorithm (EVRPSA) to find the best vehicle route. The simulation results show using our method can planning the vehicle route efficiently.

Dynamic rerouting of a fleet of vehicles in agricultural operations through a Dynamic Multiple Depot Vehicle Routing Problem representation

A dynamic vehicle routing problem with time-dependent travel times

Distribution is an action of supplying goods and services from one place to another. This company is an official agency which its main business is distributing subsidized Liquefied Petroleum Gas (LPG). Every day the company distributes to 29-agents that bounded by a company contract. The number of deliveries everyday varies depending on the agents’ demands. The company has 3 fleet vehicles, which are a six-wheel truck, 2 four-wheel trucks, and 2 three-wheel motorcycles. Those vehicles have different load capacity and some of them are able to distribute goods more than once in a day, therefore the company is facing Multiple Trips Heterogeneous Fleet Vehicle Routing Problem (MTHFVRP). At the moment the company does not know whether the vehicle routing they have now is the most effective and efficient. A solution is needed to determine a better distribution route in order to minimize the company's cost by using sweep algorithm to cluster all agents based on their vicinity. After clustering, establishment of distribution routes is created by using nearest neighbor algorithm with taking into account the minimum distance between the agents in order to produce an effective and efficient distribution route. Based on the result, distribution route created using sweep algorithm and nearest neighbor algorithm are able to generate effective and efficient distribution routes, so that the company can minimize the cost of current distribution route as much as 118,834.86 IDR/day or 2,852,036.64 IDR/month.

A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows