Path Planning for Autonomous Vehicle Based on a Two-Layered Planning Model in Complex Environment

The autonomous vehicle consists of perception, decision-making, and control system. The study of path planning method has always been a core and difficult problem, especially in complex environment, due to the effect of dynamic environment, the safety, smoothness, and real-time requirement, and the nonholonomic constraints of vehicle. To address the problem of travelling in complex environments which consists of lots of obstacles, a two-layered path planning model is presented in this paper. This method includes a high-level model that produces a rough path and a low-level model that provides precise navigation. In the high-level model, the improved Bidirectional Rapidly-exploring Random Tree (Bi-RRT) based on the steering constraint is used to generate an obstacle-free path while satisfying the nonholonomic constraints of vehicle. In low-level model, a Vector Field Histogram- (VFH-) guided polynomial planning algorithm in Frenet coordinates is introduced. Based on the result of VFH, the aim point chosen from improved Bi-RRT path is moved to the most suitable location on the basis of evaluation function. By applying quintic polynomial in Frenet coordinates, a real-time local path that is safe and smooth is generated based on the improved Bi-RRT path. To verify the effectiveness of the proposed planning model, the real autonomous vehicle has been placed in several driving scenarios with different amounts of obstacles. The two-layered real-time planning model produces flexible, smooth, and safe paths that enable the vehicle to travel in complex environment.

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Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.e2880.0610521 ◽

2021 ◽

Vol 10 (5) ◽

pp. 439-448

Author(s):

Hrishikesh Dey ◽

Rithika Ranadive ◽

Abhishek Chaudhari

Keyword(s):

Path Planning ◽

Velocity Profile ◽

Real Time ◽

Autonomous Vehicle ◽

Optimal Path ◽

Autonomous Driving ◽

Navigation Algorithm ◽

Velocity Planning ◽

Planning Algorithm ◽

Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

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Semantic Similarity Analysis between High-Level Model Description Text and Low-Level Implementation Text for Network Survivability

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology ◽

10.1109/wi-iat.2012.132 ◽

2012 ◽

Author(s):

Zhao Wei ◽

Chunhe Xia ◽

Qing Wei ◽

Shan Yao ◽

Yujian Zhou

Keyword(s):

Semantic Similarity ◽

Network Survivability ◽

Model Description ◽

Similarity Analysis ◽

Low Level ◽

Level Model ◽

Semantic Similarity Analysis ◽

High Level

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Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Sensors ◽

10.3390/s21165547 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5547

Author(s):

Younes Al Younes ◽

Martin Barczyk

Keyword(s):

Path Planning ◽

Motion Planning ◽

Real Time ◽

Nonlinear Model ◽

Feedback Linearization ◽

Autonomous Vehicle ◽

Flight Test ◽

Local Path Planning ◽

Planning Approach ◽

Local Path

Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

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iQ-Check Listeria spp. Real-Time PCR Test Kit

Journal of AOAC International ◽

10.5740/jaoacint.govval06 ◽

2013 ◽

Vol 96 (3) ◽

pp. 508-515

Author(s):

Wendy F Lauer ◽

Jean-Philippe Tourniaire

Keyword(s):

Real Time ◽

Real Time Pcr ◽

Reference Method ◽

Sampling Area ◽

Fermented Sausage ◽

Low Level ◽

Test Kit ◽

Significant Difference ◽

High Level ◽

Health Canada

Abstract A comparative evaluation study of the Bio-Rad® iQ-Check™Listeria species Kit (Bio-Rad Laboratories, Hercules, CA) was conducted at Q Laboratories, Inc., Cincinnati, OH. iQ-Check is a rapid method based on real-time PCR amplification and detection of all species of Listeria, including L. grayi, in food and environmental samples. The iQ-Check method was compared to the Health Canada MFHPB-30 reference method for the analysis of five ready-to-eat meats—deli turkey, hot dogs, liver paté, raw fermented sausage, and deli ham—and one stainless steel surface. Each food matrix was analyzed at two contamination levels: a low level at 0.2–2 CFU/25 g and a high level at 2–5 CFU/25 g. The environmental surfaces were analyzed at a low level of 0.2–2 CFU/5 cm2 sampling area and a high level of 2–5 CFU/5 cm2 sampling area. There were 20 replicates per contamination level and five control replicates at 0 CFU/25 g or 0 CFU/5 cm2 sampling area (uninoculated). All samples that were detected by iQ-Check were subsequently confirmed by reference method protocol. There was no significant difference in the number of positive samples detected by the iQ-Check Listeria spp. Kit in comparison to the Health Canada MFHPB-30 method for all matrixes tested.

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Improving performance of Python code using rewriting rules technique

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2020.02-03.115 ◽

2020 ◽

pp. 115-125

Author(s):

K.A. Zhereb ◽

Keyword(s):

Algebraic Model ◽

Algebraic Models ◽

Data Types ◽

Low Level ◽

Additional Information ◽

Program Comparison ◽

Level Model ◽

Automatic Tool ◽

High Level ◽

Rewriting Rules

Python is a popular programming language used in many areas, but its performance is significantly lower than many compiled languages. We propose an approach to increasing performance of Python code by transforming fragments of code to more efficient languages such as Cython and C++. We use high-level algebraic models and rewriting rules technique for semi-automated code transformation. Performance-critical fragments of code are transformed into a low-level syntax model using Python parser. Then this low-level model is further transformed into a high-level algebraic model that is language-independent and easier to work with. The transformation is automated using rewriting rules implemented in Termware system. We also improve the constructed high-level model by deducing additional information such as data types and constraints. From this enhanced high-level model of code we generate equivalent fragments of code using code generators for Cython and C++ languages. Cython code is seamlessly integrated with Python code, and for C++ code we generate a small utility file in Cython that also integrates this code with Python. This way, the bulk of program code can stay in Python and benefit from its facilities, but performance-critical fragments of code are transformed into more efficient equivalents, improving the performance of resulting program. Comparison of execution times between initial version of Python code, different versions of transformed code and using automatic tools such as Cython compiler and PyPy demonstrates the benefits of our approach – we have achieved performance gains of over 50x compared to the initial version written in Python, and over 2x compared to the best automatic tool we have tested.

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Model Based Monitoring and Controlling for Platform-as-a-Service (PaaS)

International Journal of Cloud Applications and Computing ◽

10.4018/ijcac.2012010101 ◽

2012 ◽

Vol 2 (1) ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Jin Shao ◽

Qianxiang Wang ◽

Hong Mei

Keyword(s):

Cloud Service ◽

Quality Of Services ◽

Meta Model ◽

Platform As A Service ◽

Low Level ◽

Model Based ◽

Level Model ◽

High Level ◽

Service Paradigm

Platform as a Service (PaaS) is a typical cloud service paradigm that allows PaaS consumers to deploy and manage applications (usually services to SaaS consumers). To ensure the quality of services to both PaaS consumers and SaaS consumers, PaaS must be equipped with enough monitoring and controlling ability to make runtime adjustment actions. Although most of the components in PaaS have provided their own management interface, it is hard to perform adjustment actions based on raw runtime data collected from these low level management interfaces due to the diversity and dynamics of components in PaaS. This paper proposes a model based monitoring and controlling approach for PaaS. The proposed approach masks the underlying heterogeneity of components in PaaS and presents a high level model for monitoring and controlling. The model is instantiated automatically based on pre-defined meta-model, which effectively reduces the development efforts. A monitoring and controlling framework based on this approach is designed and implemented in a practical PaaS, which shows the feasibility of the proposed approach.

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