Neurosurgery planning based on automated image recognition and optimal path design

2021 ◽  
Vol 69 (8) ◽  
pp. 708-721
Author(s):  
Annika Hackenberg ◽  
Karl Worthmann ◽  
Torben Pätz ◽  
Dörthe Keiner ◽  
Joachim Oertel ◽  
...  
Keyword(s):  
Optimal Path ◽  
Planning Problem ◽  
Simulation And Optimization ◽  
Ellipsoidal Approximations ◽  
Set Up ◽  
The Brain ◽  
Path Planning Problem ◽  
Automated Image Recognition ◽  
Modelling Simulation

Abstract Stereotactic neurosurgery requires a careful planning of cannulae paths to spare eloquent areas of the brain that, if damaged, will result in loss of essential neurological function such as sensory processing, linguistic ability, vision, or motor function. We present an approach based on modelling, simulation, and optimization to set up a computational assistant tool. Thereby, we focus on the modeling of the brain topology, where we construct ellipsoidal approximations of voxel clouds based on processed MRI data. The outcome is integrated in a path-planning problem either via constraints or by penalization terms in the objective function. The surgical planning problem with obstacle avoidance is solved for different types of stereotactic cannulae using numerical simulations. We illustrate our method with a case study using real MRI data.

scholarly journals Proposed Smooth-STC Algorithm for Enhanced Coverage Path Planning Performance in Mobile Robot Applications

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore ◽  
Dinh Xuan Truong
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Forest Monitoring ◽  
Planning Problem ◽  
Operational Environment ◽  
Uncertain Environments ◽  
Coverage Path Planning ◽  
Agricultural Robots ◽  
Cleaning Robots ◽  
Path Planning Problem

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

Path planning of a mobile robot using genetic heuristics

Robotica ◽  
1998 ◽  
Vol 16 (5) ◽  
pp. 575-588 ◽  
Author(s):  
Andreas C. Nearchou
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Hill Climbing ◽  
Planning Problem ◽  
Well Performance ◽  
Simulation Results ◽  
Task Oriented ◽  
Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

scholarly journals Procuring load curtailment from local customers under uncertainty

2017 ◽  
Vol 375 (2100) ◽  
pp. 20160311 ◽  
Author(s):  
Aleksandar Mijatović ◽  
John Moriarty ◽  
Jure Vogrinc
Keyword(s):  
Energy Management ◽  
Probabilistic Modelling ◽  
Demand Side ◽  
Power Network ◽  
Flexible Approach ◽  
Simulation And Optimization ◽  
Load Curtailment ◽  
Demand Side Response ◽  
Modelling Simulation

Demand side response (DSR) provides a flexible approach to managing constrained power network assets. This is valuable if future asset utilization is uncertain. However there may be uncertainty over the process of procurement of DSR from customers. In this context we combine probabilistic modelling, simulation and optimization to identify economically optimal procurement policies from heterogeneous customers local to the asset, under chance constraints on the adequacy of the procured DSR. Mathematically this gives rise to a search over permutations, and we provide an illustrative example implementation and case study. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

Modelling, Simulation and Optimization of a Packaging and Palletizing System

2015 ◽  
Vol 760 ◽  
pp. 205-211
Author(s):  
Cicerone Laurentiu Popa ◽  
Costel Emil Cotet ◽  
Stefan Gavrila ◽  
Valentin Ionita
Keyword(s):  
Material Flow ◽  
Flow System ◽  
Simulation Software ◽  
Single Type ◽  
Flow Management ◽  
Simulation And Optimization ◽  
Management Case Study ◽  
And Storage ◽  
Modelling Simulation

This paper presents a material flow management case study based on our research in modelling, simulation and optimization for a packaging and palletizing system. The studied system allows packaging, palletizing, wrapping and storage of several types of products within a food company. For each type of product to be packaged and stored a material flow system simulation is necessary in order to identify and eliminate the bottlenecks and to obtain an optimized system in terms of productivity. Because the optimization algorithm is the same for different kind of products we will illustrate our research using a single type of product. We will focus the case study on packaging coffee bags in boxes. The boxes are positioned on pallets in order to create a stack that will be wrapped for storage. We used Flexim 7 simulation software for modelling, simulation and optimization of the packaging and palletizing system.

Water wave optimization algorithm for autonomous underwater vehicle path planning problem

2021 ◽  
pp. 1-15
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jia Zeng ◽  
Jialing Tang
Keyword(s):  
Path Planning ◽  
Water Wave ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Wave Theory ◽  
Underwater Vehicle ◽  
Local Optimum ◽  
Planning Problem ◽  
Vehicle Path ◽  
Path Planning Problem

In this paper, a water wave optimization (WWO) algorithm is proposed to solve the autonomous underwater vehicle (AUV) path planning problem to obtain an optimal or near-optimal path in the marine environment. Path planning is a prerequisite for the realization of submarine reconnaissance, surveillance, combat and other underwater tasks. The WWO algorithm based on shallow wave theory is a novel evolutionary algorithm that mimics wave motions containing propagation, refraction and breaking to obtain the global optimization solution. The WWO algorithm not only avoids jumps out of the local optimum and premature convergence but also has a faster convergence speed and higher calculation accuracy. To verify the effectiveness and feasibility, the WWO algorithm is applied to solve the randomly generated threat areas and generated fixed threat areas. Compared with other algorithms, the WWO algorithm can effectively balance exploration and exploitation to avoid threat areas and reach the intended target with minimum fuel costs. The experimental results demonstrate that the WWO algorithm has better optimization performance and is robust.

scholarly journals An optimal path planning problem for heterogeneous multi-vehicle systems

2016 ◽  
Vol 26 (2) ◽  
pp. 297-308 ◽  
Author(s):  
Martin Klaučo ◽  
Slavomír Blažek ◽  
Michal Kvasnica
Keyword(s):  
Path Planning ◽  
A Priori ◽  
Optimal Path ◽  
Mixed Integer ◽  
Planning Problem ◽  
Optimal Path Planning ◽  
Second Order Cone ◽  
Vehicle Systems ◽  
Close Distance ◽  
Path Planning Problem

Abstract A path planning problem for a heterogeneous vehicle is considered. Such a vehicle consists of two parts which have the ability to move individually, but one of them has a shorter range and is therefore required to keep in a close distance to the main vehicle. The objective is to devise an optimal path of minimal length under the condition that at least one part of the heterogeneous system visits all desired waypoints exactly once. Two versions of the problem are considered. One assumes that the order in which the waypoints are visited is known a priori. In such a case we show that the optimal path can be found by solving a mixed-integer second-order cone problem. The second version assumes that the order in which the waypoints are visited is not known a priori, but can be optimized so as to shorten the length of the path. Two approaches to solve this problem are presented and evaluated with respect to computational complexity.

scholarly journals An Exact Geometry–Based Algorithm for Path Planning

2018 ◽  
Vol 28 (3) ◽  
pp. 493-504 ◽  
Author(s):  
Hassan Jafarzadeh ◽  
Cody H. Fleming
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Time Complexity ◽  
Optimal Path ◽  
Exact Algorithm ◽  
Planning Problem ◽  
Two Dimensional ◽  
Probabilistic Algorithms ◽  
Road Map ◽  
Path Planning Problem

Abstract A novel, exact algorithm is presented to solve the path planning problem that involves finding the shortest collision-free path from a start to a goal point in a two-dimensional environment containing convex and non-convex obstacles. The proposed algorithm, which is called the shortest possible path (SPP) algorithm, constructs a network of lines connecting the vertices of the obstacles and the locations of the start and goal points which is smaller than the network generated by the visibility graph. Then it finds the shortest path from start to goal point within this network. The SPP algorithm generates a safe, smooth and obstacle-free path that has a desired distance from each obstacle. This algorithm is designed for environments that are populated sparsely with convex and nonconvex polygonal obstacles. It has the capability of eliminating some of the polygons that do not play any role in constructing the optimal path. It is proven that the SPP algorithm can find the optimal path in O(nnr2) time, where n is the number of vertices of all polygons and n ̓ is the number of vertices that are considered in constructing the path network (n ̓ ≤ n). The performance of the algorithm is evaluated relative to three major classes of algorithms: heuristic, probabilistic, and classic. Different benchmark scenarios are used to evaluate the performance of the algorithm relative to the first two classes of algorithms: GAMOPP (genetic algorithm for multi-objective path planning), a representative heuristic algorithm, as well as RRT (rapidly-exploring random tree) and PRM (probabilistic road map), two well-known probabilistic algorithms. Time complexity is known for classic algorithms, so the presented algorithm is compared analytically.

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