Lite Algorithm Research Articles

BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis

BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.

Path Planning Method Based on D* lite Algorithm for Unmanned Surface Vehicles in Complex Environments

Path Planning Method Based on D* lite Algorithm for Unmanned Surface Vehicles in Complex Environments

Multi-obstacle path planning and optimization for mobile robot

In the past few decades, many results have been achieved in the research of mobile robot path planning, and they have been applied in simple scenarios, such as factory AGV, bank guide robot. However, path planning in highly dense and complex scenarios has become an important challenge for applications. Robots face dense map and complex obstacles and hardly find out an optimal path within a reasonable period, such as unmanned vehicles in freight ports and rescue robots in earthquake environment. Therefore, a multi-obstacle path planning and optimization method is proposed. In order to simplify complex environmental obstacles, the obstacles will be divided into basis obstacles and extension obstacles. Firstly, the basis obstacles and their contour point sets are determined according to the starting point and goal point. Furthermore, the basis obstacles are optimized by convex hulls, and then the corresponding basis point set is obtained. Secondly, the extension obstacles are determined by the basis point set, starting point and goal point, and then the corresponding extension point set is generated. After that, a path planner is designed by the multi-objective D* Lite algorithm for distance and smoothness in order to get reasonable and optimized path in a complex environment. Moreover, the path is smoothed by cubic bezier curves to fit the kinematic model of the robot. Finally, The proposed method conduct comparative experiments with other algorithms to verify its accuracy and computational efficiency of planning in complex environments.

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

A Cloudware Architecture for Collaboration of Multiple AGVs in Indoor Logistics: Case Study in Fabric Manufacturing Enterprises

In Industry 4.0 compatible workshops, the demand for Automated Guided Vehicles (AGVs) used in indoor logistics systems has increased remarkably. In these indoor logistics systems, it may be necessary to execute multiple transport tasks simultaneously using multiple AGVs. However, some challenges require special solutions for AGVs to be used in industrial autonomous transportation. These challenges can be addressed under four main headings: positioning, optimum path planning, collision avoidance and optimum task allocation. The solutions produced for these challenges may require special studies that vary depending on the type of tasks and the working environment in which AGVs are used. This study focuses on the problem of automated indoor logistics carried out in the simultaneous production of textile finishing enterprises. In the study, a centralized cloud system that enables multiple AGVs to work in collaboration has been developed. The finishing enterprise of a denim manufacturing factory was handled as a case study and modelling of mapping-planning processes was carried out using the developed cloud system. In the cloud system, RestFul APIs, for mapping the environment, and WebSocket methods, to track the locations of AGVs, have been developed. A collaboration module in harmony with the working model has been developed for AGVs to be used for fabric transportation. The collaboration module consists of task definition, battery management-optimization, selection of the most suitable batch trolleys (provides mobility of fabrics for the finishing mills), optimum task distribution and collision avoidance stages. In the collaboration module, all the finishing processes until the product arrives the delivery point are defined as tasks. A task allocation algorithm has been developed for the optimum performance of these tasks. The multi-fitness function that optimizes the total path of the AGVs, the elapsed time and the energy spent while performing the tasks have been determined. An assignment matrix based on K nearest neighbor (k-NN) and permutation possibilities was created for the optimal task allocation, and the most appropriate row was selected according to the optimal path totals of each row in the matrix. The D* Lite algorithm has been used to calculate the optimum path between AGVs and goals by avoiding static obstacles. By developing simulation software, the problem model was adapted and the operation of the cloud system was tested. Simulation results showed that the developed cloud system was successfully implemented. Although the developed cloud system has been applied as a case study in fabric finishing workshops with a complex structure, it can be used in different sectors as its logistic processes are similar.

Design of Cable Trench Inspection Robot System Based on Improved D*Lite Algorithm

Design of Cable Trench Inspection Robot System Based on Improved D*Lite Algorithm

Research and Optimization of D-Start Lite Algorithm in Track Planning

With the widespread application of the aircraft, how to achieve the safety and efficiency of the aircraft during flight has attracted widespread attention. The track planned by the traditional D-Start Lite algorithm is very close to the obstacle. Due to the large size of the aircraft itself, it is very likely that the aircraft will collide with the obstacle when operating in a real environment. However, the existing D-Start Lite algorithm is only a single solution to the safety problem of trajectory planning or to improve the efficiency of the algorithm's trajectory search. Therefore, this article optimizes the existing D-Start Lite algorithm. First, this article adopts the strategy of selecting priority level by child nodes. The child nodes in the vertical direction are selected as the first-level nodes. The state of the first-level node determines whether the second-level node on the diagonal needs to be expanded, so that a safe distance is generated between the planned path and the obstacle, thus improving the safety of the path. However, a certain space is reserved between the track and the obstacle, which results in the increase of the number of nodes in the path and the low efficiency of search. This article improves and optimizes the valuation function and heuristic function in the algorithm. Therefore, the number of expansion nodes and the number of inflection points are reduced, and the search efficiency of the algorithm is improved. Therefore, the improved algorithm improves the efficiency of trajectory search while ensuring the safety of trajectory planning. And the effectiveness and feasibility of the algorithm are proved by the MATLAB platform.

Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain

This paper describes a novel physics-based path planning architecture for autonomous navigation of tracked vehicles in rough terrain conditions. Unlike conventional path planning applications for smooth and structured environments, factors such as slip, slope of the terrain, robot actuator limitations, and dynamics of robot terrain interactions must be considered for rough terrain applications. The proposed path planning method consists of a hybrid planner/simulator, which takes into account all of the above factors by simulating the closed loop motion of the robot with a low-level controller on a realistic terrain model inside a physics engine. Once a feasible path to the goal is obtained, the same low-level closed loop controller is then used to execute the proposed path on the actual robot. The proposed architecture uses the D* Lite algorithm working on a 2D grid representation of the terrain as the high-level planner, Bullet as the physics engine and a hybrid automaton as the low-level closed loop controller. The proposed method is validated both in simulation and through experiments. Inferences based on the results from simulations and experiments show that the proposed planner is more effective in providing an optimal feasible path as compared to existing methodologies, demonstrating clear advantages for rough, unstructured terrain planning. Based on the results, possible improvements to the method are proposed for future work.

D* Extra Lite: A Dynamic A* With Search–Tree Cutting and Frontier–Gap Repairing

Abstract Searching for the shortest-path in an unknown or changeable environment is a common problem in robotics and video games, in which agents need to update maps and to perform re-planning in order to complete their missions. D* Lite is a popular incremental heuristic search algorithm (i.e., it utilizes knowledge from previous searches). Its efficiency lies in the fact that it re-expands only those parts of the search-space that are relevant to registered changes and the current state of the agent. In this paper, we propose a new D* Extra Lite algorithm that is close to a regular A*, with reinitialization of the affected search-space achieved by search-tree branch cutting. The provided worst-case complexity analysis strongly suggests that D* Extra Lite’s method of reinitialization is faster than the focused approach to reinitialization used in D* Lite. In comprehensive tests on a large number of typical two-dimensional path-planning problems, D* Extra Lite was 1.08 to 1.94 times faster than the optimized version of D* Lite. Moreover, while demonstrating that it can be particularly suitable for difficult, dynamic problems, as the problem-complexity increased, D* Extra Lite’s performance further surpassed that of D*Lite. The source code of the algorithm is available on the open-source basis.

Real-time path planning algorithm for autonomous vehicles in unknown environments

In this paper, a method of dynamic path planning algorithm for autonomous vehicles in cluttered environments is presented. The proposed method is based on the D* Lite algorithm with a smoothing method and local optimisation to enhance its performance in cluttered environments. The Lowess smoothing method has useful properties for the path planning problem especially in complex environments which involve lots of corners or direction changes required to avoid dynamic obstacles. Cubic hermite spline interpolation is used to describe curvature continuous trajectories for autonomous vehicles. Knowing the start and goal position, a continuous smooth trajectory can be decided. Results of proposed method are shown in simulations and experiments. Trajectories are demonstrated in various environments with and without additional obstacles. Quantitative results are shown and analysed to validate the proposed method.

Navigation Method of the Transportation Robot Using Fuzzy Line Tracking and QR Code Recognition

In this paper, a navigation method using fuzzy line tracking, QR code recognition and path planning algorithm is proposed for the transportation robot. We build a grid map by attaching QR codes on a floor and using lines that are gaps between tiles. The transportation robot is developed with a camera to capture floor images that is used to detect QR code and extract lines’ parameters by applying linear equation and FIR filter. A fuzzy decision-maker is designed to solve the deviation problem occurring during a navigation process between QR codes. The QR code is used to get the current position and recognize the direction to neighbor QR codes. Finally, the D*Lite algorithm is applied to search for an optimal path from the robot position to a goal position on the grid map using QR codes. The proposed method is verified by the navigation experiments of the transportation robot in the real environment. The robot can follow the optimal path obtained from planning algorithm with high stability and accuracy.

Positioning and obstacle avoidance of automatic guided vehicle in partially known environment

This paper presents positioning and obstacle avoidance of Automatic Guidance Vehicle (AGV) in partially known environment. To do this task, the followings are done. Firstly, the system configuration of AGV is described. Secondly, mathematical kinematic modeling of the AGV is presented to understand its characteristics and behavior. Thirdly, the Simultaneous Localization and Mapping (SLAM) algorithm based on the laser measurement system and encoders is proposed. The encoders are used for detecting the motion state of the AGV. In a slippery environment and a high speed AGV condition, encoder positioning method generates big error. Therefore, Extended Kalman Filter (EKF) is used to get the best position estimation of AGV by combining the encoder positioning result and landmark positions obtained from the laser scanner. Fourthly, to achieve the desired coordinate, D* Lite algorithm is used to generate a path from the start point to the goal point for AGV and to avoid unknown obstacles using information obtained from laser scanner. A backstepping controller based on Lyapunov stability is proposed for tracking the desired path generated by D* Lite algorithm. Finally, the effectiveness of the proposed algorithms and controller are verified by using experiment. The experimental results show that the AGV successfully reaches the goal point with an acceptable small error.

Hybrid Intelligent Algorithm for Indoor Path Planning and Trajectory-Tracking Control of Wheeled Mobile Robot

This paper presents a hybrid intelligent algorithm for a wheeled mobile robot (WMR) to implement both trajectory-tracking and path-following navigation missions. The novel control scheme combining the kinematic with TSK fuzzy control is developed to track the desired position, linear, and angular velocities, even though the WMR suffers from system uncertainties and disturbances. The proposed TSK fuzzy controller deals with a general dynamic model and has a good ability of disturbance rejection. For the path-following issue, the improved D* lite algorithm determines an appropriate path between an initial position and a destination. The derived path is transformed into a tracking trajectory by a function of time. The asymptotic stability of the overall system is proven by Lyapunov theory. Finally, real-time experiments with the use of the proposed hybrid intelligent algorithm on an eight-shaped reference trajectory and long-distance movement demonstrate the feasibility of practical WMR maneuvers.

Reusing Previously Found A* Paths for Fast Goal-Directed Navigation in Dynamic Terrain

Generalized Adaptive A* (GAA*) is an incremental algorithm that replans using A* when solving goal-directed navigation problems in dynamic terrain. Immediately after each A* search, it runs an efficient procedure that updates the heuristic values of states that were just expanded by A*, making them more informed. Those updates allow GAA* to speed up subsequent A* searches. Being based on A*, it is simple to describe and communicate; however, it is outperformed by other incremental algorithms like the state-of-the-art D*Lite algorithm at goal-directed navigation. In this paper we show how GAA* can be modified to exploit more information from a previous search in addition to the updated heuristic function. Specifically, we show how GAA* can be modified to utilize the paths found by a previous A* search. Our algorithm — Multipath Generalized Adaptive A* (MPGAA*) — has the same theoretical properties of GAA* and differs from it by only a few lines of pseudocode. Arguably, MPGAA* is simpler to understand than D*Lite. We evaluate MPGAA* over various realistic dynamic terrain settings, and observed that it generally outperforms the state-of-the-art algorithm D*Lite in scenarios resembling outdoor and indoor navigation.

COStar: A D-star Lite-based dynamic search algorithm for codon optimization

Codon optimized genes have two major advantages: they simplify de novo gene synthesis and increase the expression level in target hosts. Often they achieve this by altering codon usage in a given gene. Codon optimization is complex because it usually needs to achieve multiple opposing goals. In practice, finding an optimal sequence from the massive number of possible combinations of synonymous codons that can code for the same amino acid sequence is a challenging task. In this article, we introduce COStar, a D-star Lite-based dynamic search algorithm for codon optimization. The algorithm first maps the codon optimization problem into a weighted directed acyclic graph using a sliding window approach. Then, the D-star Lite algorithm is used to compute the shortest path from the start site to the target site in the resulting graph. Optimizing a gene is thus converted to a search in real-time for a shortest path in a generated graph. Using in silico experiments, the performance of the algorithm was shown by optimizing the different genes including the human genome. The results suggest that COStar is a promising codon optimization tool for de novo gene synthesis and heterologous gene expression.

Automated Cell Transport in Optical Tweezers-Assisted Microfluidic Chambers

In this paper, we present a physics-aware, planning approach for automated transport of cells in an optical tweezers-assisted microfluidic chamber. The approach can be used for making a uniform distribution of cells inside the chamber to allow the study of a variety of biological processes, including cell signaling. Fluid forces inside the chamber, modeled using computational fluid dynamics, are incorporated into the widely used Langevin equation to simulate the motion of cells. The developed simulator was used for building a map that contains probabilities of a cell successfully reaching one of the outlets of the chamber from different locations under the influence of the fluid flow. The developed planner not only generates collision-free paths that exploit the fluid flow inside the chamber but also utilizes the offline generated simulation data to decide suitable locations for releasing the cells. This ensures fast and robust cell transport, while minimizing the required laser power and operational time. The planner is based on the heuristic D* Lite algorithm that employs a specific cost function for searching over a novel state-action space representation. The effectiveness of the planning algorithm is demonstrated using both simulation and physical experiments in a microfluidics-optical tweezers hybrid manipulation setup.

D* lite algorithm based path planning of mobile robot in static Environment

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost function.

이동 로봇을 위한 동적 실내 환경에서의 효율적인 온라인 경로 계획 알고리즘

An efficient modified <TEX>$D^*$</TEX> lite algorithm is suggested, which can perform online path planning for mobile robots in dynamic indoor environment. Online path planning should plan and execute alternately in a short time, and hence it enables the robot avoid unknown dynamic obstacles which suddenly appear on robot's path. Based on <TEX>$D^*$</TEX> Lite algorithm, we improved representation of edge cost, heuristic function, and priority queue management, to build a modified <TEX>$D^*$</TEX> Lite algorithm. Performance of the proposed algorithm is revealed via extensive simulation study.

Proteomic Analysis of Cellular Response to Osmotic Stress in Thick Ascending Limb of Henle’s Loop (TALH) Cells

Epithelial cells of the thick ascending limb of Henle's loop (TALH cells) play a major role in the urinary concentrating mechanism. They are normally exposed to variable and often very high osmotic stress, which is particularly due to high sodium and chloride reabsorption and very low water permeability of the luminal membrane. It is already established that elevation of the activity of aldose reductase and hence an increase in intracellular sorbitol are indispensable for the osmotic adaptation and stability of the TALH cells. To identify new molecular factors potentially associated with the osmotic stress-resistant phenotype in kidney cells, TALH cells exhibiting low or high levels of resistance to osmotic stress were characterized using proteomic tools. Two-dimensional gel analysis showed a total number of 40 proteins that were differentially expressed in TALH cells under osmotic stress. Twenty-five proteins were overexpressed, whereas 15 proteins showed a down-regulation. Besides the sorbitol pathway enzyme aldose reductase, whose expression was 15 times increased, many other metabolic enzymes like glutathione S-transferase, malate dehydrogenase, lactate dehydrogenase, alpha enolase, glyceraldehyde-3-phosphate dehydrogenase, and triose-phosphate isomerase were up-regulated. Among the cytoskeleton proteins and cytoskeleton-associated proteins vimentin, cytokeratin, tropomyosin 4, and annexins I, II, and V were up-regulated, whereas tubulin and tropomyosins 1, 2, and 3 were down-regulated. The heat shock proteins alpha-crystallin chain B, HSP70, and HSP90 were found to be overexpressed. In contrast to the results in oxidative stress the endoplasmic reticulum stress proteins like glucose-regulated proteins (GRP78, GRP94, and GRP96), calreticulin, and protein-disulfide isomerase were down-regulated under hypertonic stress.