Constructive controllability algorithms for motion planning and optimization

Aiming at the problem of robot path planning in complex maps, an algorithm of robot path planning based on triangular grid graph is proposed. Firstly, a weighted undirected loop graph and a feasible domain of nodes are obtained by discretizing the triangular mesh map. Next, the Dijkstra search algorithm is applied to find the feasible shortest path from an initial to a final configuration. Finally, The Douglas-Peucker algorithm is applied to remove duplicate and redundant nodes in the feasible path, and the waypoint are extracted. The final path is a curve that is obtained by connecting the several extracted waypoint. The proposed algorithm is tested for various maps. Compared with probabilistic roadmap method, the experimental results show that the proposed method can overcome the shortcomings of the random sampling method. Furthermore, the experimental result of triangular mesh map method used in two labyrinth maps show that the triangular mesh map method can solve the robot path planning problem in complex map very well, and it is an excellent algorithm for robot path planning.

Frequency planning is one of the most expensive aspects of deploying a cellular network. If a set of base stations can be deployed with minimal service and planning, the cost of both deploying and maintaining the network will decrease. To ensure that the scarce frequency is utilized to its maximum, planning and optimization are done. This is also carried out to ensure that there is high efficiency in cellular radio systems and little or minimum interference due to co-channeling. This paper focuses on coming up with an automatic way of planning and optimizing the frequency in the cellular network. The approach replaces the inefficient, inaccurate and tedious manual approach. The automatic approach simplifies work for the radio frequency(RF) engineers and also reduces the cost of operation. The automatic approach ensures that the cellular network is extensively deployed in a way that criteria of maximum quality, quantity and good coverage are met. The paper focuses on coming up with an automatic planning and optimization algorithm that minimizes the intra-system interference levels to reasonable ranges within the key performance indicators (KPIs) defined for any acceptable cellular network.

Computer‐assisted design of experiments based on the D‐optimal design methodology and multiple linear regression analysis has been applied to the control and optimization of solar‐control properties of a sulfate‐fined soda–lime–silicate glass (72.7% SiO2, 14.2% Na2O, 10.0% CaO, 2.5% MgO, 0.6% Al2O3) which has been modified with Fe2O3 (0.0 to 0.8 wt%), NiO (0.0 to 0.15 wt%), CoO (0.0 to 0.15 wt%), V2O5 (0.0 to 0.225 wt%), TiO2 (0.0 to 1.5 wt%), SnO (0.0 to 3.0 wt%), ZnS (0.0 to 0.09 wt%), ZnO (0.0 to 2.0 wt.), CaF2 (0.0 to 2.0 wt%), and P2O5 (0.0 to 2.0 wt%) additions. The experimental design consisted of 64 initial glass compositions which were fired as 500 g batches under ambient conditions in platinum crucibles at 1500°C for 3 h. The samples were then measured for total solar transmittance in accordance with the specifications of ASTM E903–96 (weighted ordinate method) using a Cary‐500 UV/vis/near‐IR spectrophotometer equipped with an integrating sphere. CIE‐LAB color and visible transmittance (illuminant‐A) was measured in accordance with the specification of ASTM E308. The total metal content, calculated as of V2O5, CoO, NiO, Fe2O3, FeO, P2O5, TiO2, SnO, ZnO, F, and SO3, was measured for each glass sample. A modified Lambert–Beer absorption law was utilized to correlate the apparent optical density at 210 discrete wavelengths necessary for the calculation of solar and visible transmittances to the glass batch composition. The developed model was then validated and was shown to exhibit excellent agreement with the measured optical transmission data. Innovative solar‐control glasses with exceptional performance characteristics have been realized in the current investigation by application of novel computer‐assisted design and multiple correlation methodologies.

This paper investigates the influence of the volute geometry on the hydraulic performance of a low-specific-speed centrifugal pump using numerical simulations. The performance characteristics for the pump with the volute geometry designed using the constant velocity method show a significant discrepancy between the design point and the best efficiency point (BEP). This design methodology also results in a relatively flat head–capacity curve. These are both undesirable characteristics which can be mitigated by a reduction in the volute throat area. This design methodology also leads to a reduction in the power consumption and an increase in efficiency, especially at underload and design flow conditions. These impacts of the volute throat area on performance characteristics are investigated in terms of the change in internal flow characteristics due to the reduction in the volute throat area. Another aspect of the study is the impact of the width of the volute gap on performance characteristics. A reduction in the gap width results in a nearly vertical shift of the head–capacity curve, so that head delivered is higher across all the flow rates as the gap width is reduced. This is also accompanied by a slight improvement in efficiency under design flow and overload conditions. Numerical simulations are used to relate the change in performance characteristics with internal flow characteristics.

Aiming at solving the problem that existing automatic route-planning algorithms fail to consider the navigation rules in traffic separation scheme (TSS) zones, this paper proposes a ship automatic route-planning algorithm that fully considers TSS-zone navigation constraints. First, a formalized TSS-zone automatic planning module with a quadrilateral decomposition mechanism is designed, which realizes standardized processing of regular TSS zones and completes TSS-compliant route replanning through three core steps: invalid waypoint deletion, TSS-zone-traversal-order determination, and constrained route replanning. Second, a particle swarm optimization (PSO) algorithm is selected as the base global route-planning algorithm via a multi-algorithm comparative framework, considering the requirements of optimality, stability and real-time performance for ship-navigation. The TSS module is deeply integrated with the PSO algorithm, forming a unified global route-planning algorithm that balances TSS compliance and route optimality. Comparative experiments with four mainstream swarm intelligence algorithms (PSO/SSA/IVY/GOA) show that the PSO algorithm outperforms the others in terms of route length, stability and comprehensive efficiency, with an optimal route length of 57.71 and a low standard deviation of 3.42. Furthermore, the proposed algorithm is validated by real nautical chart data of Bohai Bay under single- and double-TSS-zone scenarios. The results indicate that the algorithm can stably generate TSS-compliant routes, with only a small increase in route length (0.6% and 4.4% for a single TSS zone, 1.1% and 1.8% for two TSS zones) and computational time and can automatically adjust the traversal strategy according to the start–end point settings. The designed TSS module has good scalability and can be integrated with other optimization algorithms, providing a feasible technical solution for an intelligent ship navigation system to realize automatic and compliant route planning in TSS zones with dense traffic.

At present, the walking routes of service robots are mostly formulated and fixed. Therefore, there are many places that the robots cannot reach and appear inflexible. When obstacles are on the walking route of the robot, collisions may occur. Thus, an area-optional regeneration Rapidly-Exploring Random Tree (RRT) algorithm for dynamic path planning is proposed to plan the walking route of the service robot in real-time in an environment with moving obstacles in this paper. First, sub-regions that need path planning can be determined according to the target points, and each sub-region corresponds to a root node, so that a feasible path that does not touch obstacles through a narrow area can be found faster, and the speed of path planning can be improved. Secondly, in real-time path planning, it is analyzed that whether tree nodes near moving obstacles need to be removed. Besides, how to remove, add, and reconnect are also analyzed, avoiding global planning and improving the efficiency of path planning in frequently changing environments. It is verified by simulation that the efficiency of path planning can be improved after dividing the area, and the effectiveness and superiority of the algorithm are verified in a real-time changing environment.KeywordsArea-optionalRegeneration RRT algorithmObstacle avoidanceReal-time path planning

Model-driven engineering of planning and optimisation algorithms for pervasive computing environments

A double-layer coding model with a rotation-based particle swarm algorithm for unmanned combat aerial vehicle path planning

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

Demand for 3D planning and guidance algorithms is increasing due, in part, to the increase in unmanned vehicle-based applications. Traditionally, two-dimensional (2D) trajectory planning algorithms address the problem by using the approach of maintaining a constant altitude. Addressing the problem of path planning in a three-dimensional (3D) space implies more complex scenarios where maintaining altitude is not a valid approach. The work presented here implements an architecture for the generation of 3D flight paths for fixed-wing unmanned aerial vehicles (UAVs). The aim is to determine the feasible flight path by minimizing the turning effort, starting from a set of control points in 3D space, including the initial and final point. The trajectory generated takes into account the rotation and elevation constraints of the UAV. From the defined control points and the movement constraints of the UAV, a path is generated that combines the union of the control points by means of a set of rectilinear segments and spherical curves. However, this design methodology means that the problem does not have a single solution; in other words, there are infinite solutions for the generation of the final path. For this reason, a multiobjective optimization problem (MOP) is proposed with the aim of independently maximizing each of the turning radii of the path. Finally, to produce a complete results visualization of the MOP and the final 3D trajectory, the architecture was implemented in a simulation with Matlab/Simulink/flightGear.

BackgroundThe digit span task, a measure of auditory verbal short‐term and working memory, is widely used globally. Emerging research has revealed variations in digit span performance across languages among young adults; however, studies focusing on older populations are scarce and typically involving limited languages. This study investigates digit span performance among older adults (40‐90 year‐old) across fourteen languages and explored the influence of lexical and numerical properties on cognitive assessment.MethodWe examined digit span performance among cognitively normal participants (CN), individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) across fourteen language cohorts totalling 3,681 participants: English (n = 446), Mandarin (n = 97), Cantonese (n = 65), Spanish (n = 218), Kannada (n = 69), Hindi (n = 72), Telugu (n = 69), Malayalam (n = 70), Bengali (n = 70), French (n = 299), Korean (n = 1098), Italian (n = 540), Arabic (n = 50), and Turkish (n = 518). First, we analyzed language differences in digit span performance among CN using ANOVA and general linear models. We then conducted Receiver operating characteristic (ROC) analyses to identify the optimal cutoff values for AD. Next, we computed the digit count, syllable count, and numerical magnitude (i.e. the average sum of the digits) of all digit stimuli in the English cohort and analyzed their effects via linear and ridge regression analyses.ResultThe forward (FDS) and backward digit span (BDS) tests revealed significant differences among CN across the fourteen language cohorts even after adjusting for age and education (FDS:F=38.62, p <0.001; BDS:F=19.23, p <0.001). ROC analysis revealed varying optimal cutoff values across languages: English (FDS:6, BDS:4), Italian and Turkish (FDS:5, BDS:4), Mandarin (FDS:7, BDS:5), Cantonese (FDS:7, BDS:4), and French (FDS:6, BDS:3). Further analysis indicated that the interaction between digit and syllable counts significantly impacted FDS accuracy in English speakers (linear:p=0.00035; ridge:p< 0.000001), with no significant effect from digit count alone after adjusting for interaction. Conversely, BDS performance showed a significant negative influence from digit count (p = 0.00858), with numerical magnitude and syllable count nearing significance (p = 0.083 and p = 0.066, respectively).ConclusionVariations in digit span performance across languages illustrate the role of linguistic and numerical factors in cognitive assessments, even with tests targeting non‐language domains using digit stimuli. These findings underscore the critical value of language diversity in cognitive research.

A Time-of-Flight camera can help a service robot to sense its 3D environment. The modeled environment information can be used by motion planning algorithms to plan a collision free movement for the robot, or by grasp planning algorithms to find feasible grasps for an a priori unknown object in the scene. To guarantee that the models used by the planning algorithms are consistent with the real world, calibration of the Time-of-Flight camera is necessary. In this article, the intrinsic and extrinsic parameter calibration procedures applied in the DESIRE project are presented. A segmentation using GPU calculation is used to separate the sensed model and a priori information of the environment. We use a motion planning algorithm based on Probabilistic Roadmaps to plan collision free movement for the DESIRE platform.

The flyback topology is proven to be a very strong candidate solution for use in ac-PV module applications. Operation in the boundary condition mode (BCM) provides high power density, while maintaining the characteristics of a current source inverter. In this paper, a design methodology is presented, that maximizes the weighted efficiency of the converter through an optimization algorithm. The inverter operation is investigated and the behavior under the improved BCM is documented by analytical equations followed by the power loss calculations for each component. This enables to accurately define the relation between the design parameters and the efficiency of the implemented converter and so, an optimization algorithm is established, that takes into consideration the design specifications and constraints. The proposed methodology is also verified with an experimental prototype.

Safe navigation for mobile robots in unstructured and dynamic environments is still a challenging research topic. Most approaches use separate algorithms for global path planning and local obstacle avoidance. However, this generally results in globally sub-optimal navigation strategies. In this paper, we present an algorithm which combines these two navigation tasks in a single integrated approach. For this purpose, we introduce a novel search space, namely, a × lattice with hybrid dimensionality. We describe a procedure for generating high-quality motion primitives for a mobile robot with four-wheel steering to define the motion in this lattice. Our algorithm computes a hybrid solution for the path planning problem consisting of a trajectory (i.e., a path with time component) in the imminent future, a dynamically feasible path in the near future, and a kinematically feasible path for the remaining time to the goal. Finally, we provide some results of our algorithm in action to prove its high solution quality and real-time capability.

PurposeThe purpose of this paper is to present a new nested rapidly‐exploring random tree (RRT) algorithm for fault tolerant motion planning of robotic manipulators.Design/methodology/approachAnother RRT algorithm is nested within the general RRT algorithm. This second nested level is used to check whether the new sampled node in the first nested level is fault tolerant. If a solution can be found in the second nested RRT, the reduced manipulator after failures at the new sampled node can still fulfill the remaining task and this new sampled node is added into the nodes of RRT in the first level. Thus, the nodes in the first level RRT algorithm are all fault tolerant postures. The final trajectory joined by these nodes is also obviously fault tolerant. Besides fault tolerance, this new nested RRT algorithm also can fulfill some secondary tasks such as improvement of dexterity and obstacle avoidance. Sufficient simulations and experiments of this new algorithm on fault tolerant motion planning of robotic manipulators are implemented.FindingsIt is found that the new nested RRT algorithm can fulfill fault tolerance and some other secondary tasks at the same time. Compared to other existing fault tolerant algorithms, this new algorithm is more efficient.Originality/valueThe paper presents a new nested RRT algorithm for fault tolerant motion planning.