scholarly journals Real-time Approximate Routing for Smart Transit Systems

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
Noémie Périvier ◽  
Chamsi Hssaine ◽  
Samitha Samaranayake ◽  
Siddhartha Banerjee
Keyword(s):  
Real Time ◽  
Time Window ◽  
Time Budget ◽  
High Capacity ◽  
Constant Factor ◽  
Planning Problem ◽  
Randomized Rounding ◽  
Exact Methods ◽  
Line Planning ◽  
Transit Systems

We study real-time routing policies in smart transit systems, where the platform has a combination of cars and high-capacity vehicles (e.g., buses or shuttles) and seeks to serve a set of incoming trip requests. The platform can use its fleet of cars as a feeder to connect passengers to its high-capacity fleet, which operates on fixed routes. Our goal is to find the optimal set of (bus) routes and corresponding frequencies to maximize the social welfare of the system in a given time window. This generalizes the Line Planning Problem, a widely studied topic in the transportation literature, for which existing solutions are either heuristic (with no performance guarantees), or require extensive computation time (and hence are impractical for real-time use). To this end, we develop a 1-1/e-ε approximation algorithm for the Real-Time Line Planning Problem, using ideas from randomized rounding and the Generalized Assignment Problem. Our guarantee holds under two assumptions: (i) no inter-bus transfers and (ii) access to a pre-specified set of feasible bus lines. We moreover show that these two assumptions are crucial by proving that, if either assumption is relaxed, the łineplanningproblem does not admit any constant-factor approximation. Finally, we demonstrate the practicality of our algorithm via numerical experiments on real-world and synthetic datasets, in which we show that, given a fixed time budget, our algorithm outperforms Integer Linear Programming-based exact methods.

scholarly journals A Quadratic Programming Approach to Manipulation in Real-Time Using Modular Robots

2021 ◽  
Vol 3 (1) ◽  
pp. 121-145
Author(s):  
Chao Liu ◽  
Mark Yim
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Dimensional Space ◽  
Quadratic Program ◽  
Modular Robots ◽  
Programming Approach ◽  
Planning Problem ◽  
Real Time Control ◽  
Line Planning

Motion planning in high-dimensional space is a challenging task. In order to perform dexterous manipulation in an unstructured environment, a robot with many degrees of freedom is usually necessary, which also complicates its motion planning problem. Real-time control brings about more difficulties in which robots have to maintain the stability while moving towards the target. Redundant systems are common in modular robots that consist of multiple modules and are able to transform into different configurations with respect to different needs. Different from robots with fixed geometry or configurations, the kinematics model of a modular robotic system can alter as the robot reconfigures itself, and developing a generic control and motion planning approach for such systems is difficult, especially when multiple motion goals are coupled. A new manipulation planning framework is developed in this paper. The problem is formulated as a sequential linearly constrained quadratic program (QP) that can be solved efficiently. Some constraints can be incorporated into this QP, including a novel way to approximate environment obstacles. This solution can be used directly for real-time applications or as an off-line planning tool, and it is validated and demonstrated on the CKBot and the SMORES-EP modular robot platforms.

Research on pull-type multi-AGV system dynamic path optimization based on time window

2021 ◽  
pp. 095440702098271
Author(s):  
Hongying Shan ◽  
Chuang Wang ◽  
Cungang Zou ◽  
Mengyao Qin
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Planning Problem ◽  
Dijkstra Algorithm ◽  
Before And After ◽  
Common Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Set Up ◽  
Path Planning Algorithm

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

scholarly journals Modeling and Simulation of Mission Planning Problem for Remote Sensing Satellite Imaging

2018 ◽  
Vol 179 ◽  
pp. 03024 ◽  
Author(s):  
Yao Pan ◽  
Zhong Ming Chi ◽  
Qi Long Rao ◽  
Kai Peng Sun ◽  
Yi Nan Liu
Keyword(s):  
Remote Sensing ◽  
Time Window ◽  
Constraint Satisfaction Problem ◽  
Optimal Path ◽  
Time Constraint ◽  
Mission Planning ◽  
Planning Problem ◽  
Satellite Imaging ◽  
Problem Model ◽  
Remote Sensing Satellite

Mission planning problem for remote sensing satellite imaging is studied. Firstly, the time constraint satisfaction problem model is presented after analyzing the characteristic of time constraint. Then, An optimal path searching algorithm based on the discrete time window is proposed according to the non-uniqueness for satellite to mission in the visible time window. Simulation results verify the efficiency of the model and algorithm.

scholarly journals Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 37 ◽  
Author(s):  
Zhigang Hu ◽  
Hui Kang ◽  
Meiguang Zheng
Keyword(s):  
Support Vector Regression ◽  
Real Time ◽  
Virtual Machines ◽  
Time Window ◽  
Performance Model ◽  
Streaming Data ◽  
Support Vector ◽  
Load Prediction ◽  
Stream Data ◽  
Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

Dynamic Measurement of Spatial Attitude at Bottom Rotating Drillstring: Simulation, Experimental, and Field Test

2015 ◽  
Vol 138 (2) ◽  
Author(s):  
Qilong Xue ◽  
Ruihe Wang ◽  
Baolin Liu ◽  
Leilei Huang
Keyword(s):  
Real Time ◽  
Measurement Errors ◽  
Time Window ◽  
Measurement Data ◽  
Movement Patterns ◽  
Dynamic Measurement ◽  
Drilling Process ◽  
Stick Slip ◽  
Actual Measurement ◽  
Gas Drilling

In the oil and gas drilling engineering, measurement-while-drilling (MWD) system is usually used to provide real-time monitoring of the position and orientation of the bottom hole. Particularly in the rotary steerable drilling technology and application, it is a challenge to measure the spatial attitude of the bottom drillstring accurately in real time while the drillstring is rotating. A set of “strap-down” measurement system was developed in this paper. The triaxial accelerometer and triaxial fluxgate were installed near the bit, and real-time inclination and azimuth can be measured while the drillstring is rotating. Furthermore, the mathematical model of the continuous measurement was established during drilling. The real-time signals of the accelerometer and the fluxgate sensors are processed and analyzed in a time window, and the movement patterns of the drilling bit will be observed, such as stationary, uniform rotation, and stick–slip. Different signal processing methods will be used for different movement patterns. Additionally, a scientific approach was put forward to improve the solver accuracy benefit from the use of stick–slip vibration phenomenon. We also developed the Kalman filter (KF) to improve the solver accuracy. The actual measurement data through drilling process verify that the algorithm proposed in this paper is reliable and effective and the dynamic measurement errors of inclination and azimuth are effectively reduced.

scholarly journals Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Li Ni ◽  
Jianting Cao ◽  
Rubin Wang
Keyword(s):  
Brain Death ◽  
Real Time ◽  
Time Window ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
Well Performance ◽  
Entropy Measures ◽  
Brain Death Determination ◽  
Death Determination ◽  
The Brain

To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.

Determination of the base position and working area for mobile manipulators

2016 ◽  
Vol 36 (1) ◽  
pp. 80-88 ◽  
Author(s):  
Shunan Ren ◽  
Xiangdong Yang ◽  
Jing Xu ◽  
Guolei Wang ◽  
Ying Xie ◽  
...  
Keyword(s):  
Operation Mode ◽  
Mobile Manipulator ◽  
Planning Problem ◽  
Mobile Manipulators ◽  
Line Planning ◽  
Content Type ◽  
Practical Applications ◽  
Base Position ◽  
Intersection Area ◽  
Intersection Line

Purpose – The purpose of this paper is to determine the base position and the largest working area for mobile manipulators. The base position determines the workspace of the mobile manipulator, particularly when the operation mode is intermittent (i.e. the mobile platform stops when the manipulator conducts the task). When the base of the manipulator is in the intersection area of the Base’s Workable Location Spaces (BWLSes), the end effector (EE) can reach all path points. In this study, the intersection line of BWLSes is calculated numerically, and the largest working area is determined using the BWLS concept. The performance of this method is validated with simulations on specific surface segments, such as plane, cylinder and conical surface segments. Design/methodology/approach – The BWLS is used to determine the largest working area and the base position in which the mobile manipulator can reach all path points with the objective of reducing off-line planning time. Findings – Without considering the orientation of the EE, the base position and the working area for the mobile manipulator are determined using the BWLS. Compared to other methods, the proposed algorithm is beneficial when the planning problem has six dimensions, ensuring the reachability and stability of the EE. Originality/value – The algorithm needs no manual configuration, and its performance is investigated for typical surfaces in practical applications.

A New Formulation for the Dial-a-Ride Problem

2021 ◽  
Author(s):  
Yannik Rist ◽  
Michael A. Forbes
Keyword(s):  
Time Window ◽  
Valid Inequalities ◽  
Route Planning ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Planning Problem ◽  
Current State ◽  
New Formulation ◽  
First Time ◽  
Delivery Location

This paper proposes a new mixed integer programming formulation and branch and cut (BC) algorithm to solve the dial-a-ride problem (DARP). The DARP is a route-planning problem where several vehicles must serve a set of customers, each of which has a pickup and delivery location, and includes time window and ride time constraints. We develop “restricted fragments,” which are select segments of routes that can represent any DARP route. We show how to enumerate these restricted fragments and prove results on domination between them. The formulation we propose is solved with a BC algorithm, which includes new valid inequalities specific to our restricted fragment formulation. The algorithm is benchmarked on existing and new instances, solving nine existing instances to optimality for the first time. In comparison with current state-of-the-art methods, run times are reduced between one and two orders of magnitude on large instances.

Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

2020 ◽  
Author(s):  
Jing Huang ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Quadratic Programming ◽  
Real Time ◽  
Trajectory Planning ◽  
Autonomous Driving ◽  
Curse Of Dimensionality ◽  
Planning Problem ◽  
Feasible Set ◽  
Planning Algorithm ◽  
Vehicle Trajectory

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.

Mixed Integer Linear Programming for UAV Trajectory Planning Problem

2014 ◽  
Vol 541-542 ◽  
pp. 1473-1477 ◽  
Author(s):  
Lei Zhang ◽  
Zhou Zhou ◽  
Fu Ming Zhang
Keyword(s):  
Real Time ◽  
Predictive Control ◽  
Trajectory Planning ◽  
Arrival Time ◽  
Linear Constraints ◽  
Mixed Integer ◽  
Planning Problem ◽  
Convex Constraints ◽  
3D Environments ◽  
On Line

This paper describes a method for vehicles flying Trajectory Planning Problem in 3D environments. These requirements lead to non-convex constraints and difficult optimizations. It is shown that this problem can be rewritten as a linear program with mixed integer linear constraints that account for the collision avoidance used in model predictive control, running in real-time to incorporate feedback and compensate for uncertainty. An example is worked out in a real-time scheme, solved on-line to compensate for the effect of uncertainty as the maneuver progresses. In particular, we compare receding horizon control with arrival time approaches.

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