scholarly journals Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance

2017 ◽  
pp. 507-525
Author(s):  
Lucas Janson ◽  
Brian Ichter ◽  
Marco Pavone
Keyword(s):  
Motion Planning ◽  
Deterministic Sampling ◽  
And Performance

Integrated motion planning and trajectory control system for unmanned air vehicles

2012 ◽  
Vol 227 (1) ◽  
pp. 3-18 ◽  
Author(s):  
J Karimi ◽  
Seid H Pourtakdoust
Keyword(s):  
Control System ◽  
Motion Planning ◽  
Integrated Approach ◽  
Unmanned Vehicles ◽  
Trajectory Control ◽  
Planning Problem ◽  
Nonlinear Dynamic Model ◽  
Contour Matching ◽  
And Performance ◽  
Enhanced Particle Swarm Optimization

Motion planning and trajectory control are two basic challenges of unmanned vehicles. In motion planning problem, feasible trajectories are developed while nonlinear dynamic model and performance constraints of the vehicle under utility are considered. In this study, motion planning is performed via an enhanced particle swarm optimization algorithm. The resulting offline generated trajectories are tracked using a nonlinear trajectory control system methodology. The Lyapunov-based constrained backstepping approach and command filters are utilized in designing the trajectory control system. Command filters smoothen the input signals and provide their derivatives. Evaluation of the proposed integrated approach in several simulated scenarios has effectively demonstrated the potential of both algorithms in generating optimal contour matching trajectories as well as excellent tracking capability of the trajectory control system.

scholarly journals Water Sink Model for Robot Motion Planning

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1269 ◽  
Author(s):  
Gi-Yoon Jeon ◽  
Jin-Woo Jung
Keyword(s):  
Motion Planning ◽  
Water Flow ◽  
Potential Field ◽  
Cell Decomposition ◽  
Performance Criteria ◽  
Local Minima ◽  
Probabilistic Algorithms ◽  
Planning Techniques ◽  
Field Methods ◽  
And Performance

There are various motion planning techniques for robots or agents, such as bug algorithm, visibility graph, Voronoi diagram, cell decomposition, potential field, and other probabilistic algorithms. Each technique has its own advantages and drawbacks, depending on the number and shape of obstacles and performance criteria. Especially, a potential field has vector values for movement guidance to the goal, and the method can be used to make an instantaneous and smooth robot movement path without an additional controller. However, there may be some positions with zero force value, called local minima, where the robot or agent stops and cannot move any further. There are some solutions for local minima, such as random walk or backtracking, but these are not yet good enough to solve the local minima problem. In this paper, we propose a novel movement guidance method that is based on the water sink model to overcome the previous local minima problem of potential field methods. The concept of the water sink model is to mimic the water flow, where there is a sink or bathtub with a plughole and floating piece on the water. The plughole represents the goal position and the floating piece represents robot. In this model, when the plug is removed, water starts to drain out via the plughole and the robot can always reach the goal by the water flow. The water sink model simulator is implemented and a comparison of experimental results is done between the water sink model and potential field.

Optimal Motion Planning for Differential Drive Mobile Robots based on Multiple-Interval Chebyshev Pseudospectral Methods

Robotica ◽  
2020 ◽  
pp. 1-20
Author(s):  
Run Mao ◽  
Hongli Gao ◽  
Liang Guo
Keyword(s):  
Motion Planning ◽  
Mobile Robots ◽  
Planning Problem ◽  
Nonholonomic Mobile Robots ◽  
Dynamic Constraints ◽  
Optimal Motion Planning ◽  
Single Interval ◽  
And Performance ◽  
And Control ◽  
Chebyshev Pseudospectral

SUMMARY This paper presents a Chebyshev Pseudospectral (PS) method for solving the motion planning problem of nonholonomic mobile robots with kinematic and dynamic constraints. The state and control variables are expanded in the Chebyshev polynomial of order N, and Chebyshev–Gauss–Lobatto (CGL) nodes are provided for approximating the system dynamics, boundary conditions, and performance index. For the lack of enough nodes nearby the obstacles, the interpolation of trajectory may violate the obstacles and the multiple-interval strategy is proposed to deal with the violation. Numerical examples demonstrate that multiple-interval strategy yields more accurate results than the single-interval Chebyshev PS method.

Motion Planning and Performance Analysis of a Novel Six-Tracked Robot

2021 ◽  
Author(s):  
Rengui Bai ◽  
Runxin Niu ◽  
Huawei Liang ◽  
Zhaosheng Xu ◽  
Yi Ding ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Motion Planning ◽  
And Performance

scholarly journals Deterministic sampling-based motion planning: Optimality, complexity, and performance

2017 ◽  
Vol 37 (1) ◽  
pp. 46-61 ◽  
Author(s):  
Lucas Janson ◽  
Brian Ichter ◽  
Marco Pavone
Keyword(s):  
Motion Planning ◽  
Random Sampling ◽  
Superior Performance ◽  
Asymptotically Optimal ◽  
Deterministic Sampling ◽  
Sampling Sequence ◽  
Practical Performance ◽  
Sampling Sequences ◽  
Low Dispersion ◽  
Theoretical Results

Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly exploring random tree (RRT) algorithms, represent one of the most successful approaches to robotic motion planning, due to their strong theoretical properties (in terms of probabilistic completeness or even asymptotic optimality) and remarkable practical performance. Such algorithms are probabilistic in that they compute a path by connecting independently and identically distributed (i.i.d.) random points in the configuration space. Their randomization aspect, however, makes several tasks challenging, including certification for safety-critical applications and use of offline computation to improve real-time execution. Hence, an important open question is whether similar (or better) theoretical guarantees and practical performance could be obtained by considering deterministic, as opposed to random, sampling sequences. The objective of this paper is to provide a rigorous answer to this question. Specifically, we first show that PRM, for a certain selection of tuning parameters and deterministic low-dispersion sampling sequences, is deterministically asymptotically optimal, in other words, it returns a path whose cost converges deterministically to the optimal one as the number of points goes to infinity. Second, we characterize the convergence rate, and we find that the factor of sub-optimality can be very explicitly upper-bounded in terms of the[Formula: see text] -dispersion of the sampling sequence and the connection radius of PRM. Third, we show that an asymptotically optimal version of PRM exists with computational and space complexity arbitrarily close to [Formula: see text] (the theoretical lower bound), where n is the number of points in the sequence. This is in contrast to the [Formula: see text] complexity results for existing asymptotically optimal probabilistic planners. Fourth, we discuss extending our theoretical results and insights to other batch-processing algorithms such as FMT*, to non-uniform sampling strategies, to k-nearest-neighbor implementations, and to differentially constrained problems. Importantly, our main theoretical tool is the [Formula: see text]-dispersion, an interesting consequence of which is that all our theoretical results also hold for low-[Formula: see text]-dispersion random sampling (which i.i.d. sampling does not satisfy). In other words, achieving deterministic guarantees is really a matter of i.i.d. sampling versus non-i.i.d. low-dispersion sampling (with deterministic sampling as a prominent case), as opposed to random versus deterministic. Finally, through numerical experiments, we show that planning with deterministic (or random) low-dispersion sampling generally provides superior performance in terms of path cost and success rate.

Transmission Scanning Electron Microscopy and Energy Analysis with the Siemens ELMISKOP 101

1972 ◽  
Vol 30 ◽  
pp. 450-451
Author(s):  
H. M. Thieringer
Keyword(s):  
Objective Lens ◽  
Transmission Microscopy ◽  
Image Recording ◽  
Mode Of Operation ◽  
Scanning Transmission ◽  
Electron Microscopes ◽  
And Performance ◽  
Convergent Beam ◽  
Ray Path ◽  
Beam Diffraction

It has repeatedly been show that with conventional electron microscopes very fine electron probes can be produced, therefore allowing various micro-techniques such as micro recording, X-ray microanalysis and convergent beam diffraction. In this paper the function and performance of an SIEMENS ELMISKOP 101 used as a scanning transmission microscope (STEM) is described. This mode of operation has some advantages over the conventional transmission microscopy (CTEM) especially for the observation of thick specimen, in spite of somewhat longer image recording times.Fig.1 shows schematically the ray path and the additional electronics of an ELMISKOP 101 working as a STEM. With a point-cathode, and using condensor I and the objective lens as a demagnifying system, an electron probe with a half-width ob about 25 Å and a typical current of 5.10-11 amp at 100 kV can be obtained in the back focal plane of the objective lens.

Angular Resolved Electron Spectroscopy with Parallel Recording

1990 ◽  
Vol 48 (2) ◽  
pp. 370-371
Author(s):  
Huang Min ◽  
P.S. Flora ◽  
C.J. Harland ◽  
J.A. Venables
Keyword(s):  
Electron Spectroscopy ◽  
Low Voltage ◽  
Solid Angle ◽  
Detection System ◽  
Voltage Electron ◽  
Cylindrical Mirror ◽  
Inner Radius ◽  
Channel Plate ◽  
And Performance ◽  
Type Detector

A cylindrical mirror analyser (CMA) has been built with a parallel recording detection system. It is being used for angular resolved electron spectroscopy (ARES) within a SEM. The CMA has been optimised for imaging applications; the inner cylinder contains a magnetically focused and scanned, 30kV, SEM electron-optical column. The CMA has a large inner radius (50.8mm) and a large collection solid angle (Ω > 1sterad). An energy resolution (ΔE/E) of 1-2% has been achieved. The design and performance of the combination SEM/CMA instrument has been described previously and the CMA and detector system has been used for low voltage electron spectroscopy. Here we discuss the use of the CMA for ARES and present some preliminary results.The CMA has been designed for an axis-to-ring focus and uses an annular type detector. This detector consists of a channel-plate/YAG/mirror assembly which is optically coupled to either a photomultiplier for spectroscopy or a TV camera for parallel detection.

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