Camera Collision Script Unity游戏中常见的几种Camera控制脚本

cadrl_ros（使用Deep RL避免冲突） 用Deep RL训练的动态避障算法的ROS实现 纸： M.Everett，Y.Chen和JP How，《具有深度强化学习的动态决策代理之间的运动计划》，IEEE / RSJ国际智能机器人和系统会议（IROS），2018年 论文： : 视频： : Bibtex： @inproceedings{Everett18_IROS, address = {Madrid, Spain}, author = {Everett, Michael and Chen, Yu Fan and How, Jonathan P.}, bookti

NULL 博文链接：https://goodscript.iteye.com/blog/1338973

This C library handles 2-dimensional bounding box collision detection. It is optimized to scale well from a few objects to many thousands of objects.

模糊集matlab代码Fuzzy_Collision_Avoidance 用于避免碰撞的 Matlab 代码 模糊逻辑通过考虑现实世界中通信的不精确性来执行计算。 与布尔逻辑（即 1 和 0）相反，取值是基于“真实程度”。 受人类认知和感知的生物过程的启发，模糊逻辑基于相对分级隶属函数的思想进行了理论化，隶属函数与归属的程度和程度相关。 模糊逻辑控制器 模糊逻辑控制器是一种基于模糊逻辑或模糊集的控制系统，它根据采用 0 到 1 之间连续值的逻辑变量来分析模拟输入值。因此，边界的模糊性和不精确性使其可用于近似模型. 传统控制器包括四个步骤，即模糊化、知识库、模糊推理和去模糊化。 模糊控制器的第一步是定义模糊控制器的输入和输出变量。 模糊逻辑控制器使用一组非常灵活的 if-then 规则，并且控制器规则通常用语言术语表述。 因此，语言变量和模糊集的使用意味着模糊化过程，即将输入变量映射到合适的语言学值。 最后一步是去模糊化，它将基于模糊的语言术语转换为标量输出值。 方法 在 VREP 软件中创建了机器人导航环境，用于模拟和机器人在物体填充环境中的性能。 用于此目的的机器人是 Pioneer

1 Introduction 1.1 Contents Overview 1.2 About the Code 2 Collision Detection Design Issues 2.1 Collision Algorithm Design Factors 2.2 Application Domain Representation 2.2.1 Object Representations 2.2.2 Collision versus Rendering Geometry 2.2.3 Collision Algorithm Specialization 2.3 Different Types of Queries 2.4 Environment Simulation Parameters 2.4.1 Number of Objects 2.4.2 Sequential versus Simultaneous Motion 2.4.3 Discrete versus Continuous Motion 2.5 Performance 2.5.1 Optimization Overview 2.6 Robustness 2.7 Ease of Implementation and Use 2.7.1 Debugging a Collision Detection System 2.8 Summary 3 A Math and Geometry Primer 3.1 Matrices 3.1.1 Matrix Arithmetic 3.1.2 Algebraic Identities Involving Matrices 3.1.3 Determinants 3.1.4 Solving Small Systems of Linear Equation using Cramer's Rule 3.1.5 Matrix Inverses for 2x2 and 3x3 Matrices 3.1.6 Determinant Predicates 3.1.6.1 ORIENT2D(A, B, C) 3.1.6.2 ORIENT3D(A, B, C, D) 3.1.6.3 INCIRCLE2D(A, B, C, D) 3.1.6.4 INSPHERE(A, B, C, D, E) 3.2 Coordinate Systems and Points 3.3 Vectors 3.3.1 Vector Arithmetic 3.3.2 Algebraic Identities Involving Vectors 3.3.3 The Dot Product 3.3.4 Algebraic Identities Involving Dot Products 3.3.5 The Cross Product 3.3.6 Algebraic Identities Involving Cross Products 3.3.7 The Scalar Triple Product 3.3.8 Algebraic Identities Involving Scalar Triple Products 3.4 Barycentric Coordinates 3.5 Lines, Rays, and Segments 3.6 Planes and Halfspaces 3.7 Polygons 3.7.1 Testing Polygonal Convexity 3.8 Polyhedra 3.8.1 Testing Polyhedral Convexity 3.9 Computing Convex Hulls 3.9.1 Andrew's Algorithm 3.9.2 The Quickhull Algorithm 3.10 Voronoi Regions 3.11 Minkowski Sum and Difference 3.12 Summary 4 Bounding Volumes 4.1 Desired BV Characteristics 4.2 Axis-Aligned Bounding Boxes (AABBs) 4.2.1 AABB-AABB Intersection 4.2.2 Computing and Updating AABBs 4.2.3 AABB from the Object Bounding Sphere 4.2.4 AABB Reconstructed from Original Point Set 4.2.5 AABB from Hill-Climbing Vertices of the Object Representation 4.2.6 AABB Recomputed from Rotated AABB 4.3 Spheres 4.3.1 Sphere-Sphere Intersection 4.3.2 Computing a Bounding Sphere 4.3.3 Bounding Sphere from Direction of Maximum Spread 4.3.4 Bounding Sphere Through Iterative Refinement 4.3.5 The Minimum Bounding Sphere 4.4 Oriented Bounding Boxes (OBBs) 4.4.1 OBB-OBB Intersection 4.4.2 Making the Separating-Axis Test Robust 4.4.3 Computing a Tight OBB 4.4.4 Optimizing PCA-Based OBBs 4.4.5 Brute-Force OBB Fitting 4.5 Sphere-Swept Volumes 4.5.1 Sphere-Swept Volume Intersection 4.5.2 Computing Sphere-Swept Bounding Volumes 4.6 Halfspace Intersection Volumes 4.6.1 Kay-Kajiya Slab-Based Volumes 4.6.2 Discrete-Orientation Polytopes (k-DOPs) 4.6.3 k-DOP-k-DOP Overlap Test 4.6.4 Computing and Realigning k-DOPs 4.6.5 Approximate Convex Hull Intersection Tests 4.7 Other Bounding Volumes 4.8 Summary 5 Basic Primitive Tests 5.1 Closest Point Computations 5.1.1 Closest Point on Plane to Point 5.1.2 Closest Point on Line Segment to Point 5.1.2.1 Distance of Point to Segment 5.1.3 Closest Point on AABB to Point 5.1.3.1 Distance of Point to AABB 5.1.4 Closest Point on OBB to Point 5.1.4.1 Distance of Point to OBB 5.1.4.2 Closest Point on 3D Rectangle to Point 5.1.5 Closest Point on Triangle to Point 5.1.6 Closest Point on Tetrahedron to Point 5.1.7 Closest Point on Convex Polyhedron to Point 5.1.8 Closest Points of Two Lines 5.1.9 Closest Points of Two Line Segments 5.1.9.1 2D Segment Intersection 5.1.10 Closest Points of a Line Segment and a Triangle 5.1.11 Closest Points of Two Triangles 5.2 Testing primitives 5.2.1 Separating Axis Test 5.2.1.1 Robustness of the Separating Axis Test 5.2.2 Testing Sphere against Plane 5.2.3 Testing Box against Plane 5.2.4 Testing Cone against Plane 5.2.5 Testing Sphere against AABB 5.2.6 Testing Sphere against OBB 5.2.7 Testing Sphere against Triangle 5.2.8 Testing Sphere against Polygon 5.2.9 Testing AABB against Triangle 5.2.10 Testing Triangle against Triangle 5.3 Intersecting Lines, Rays, and (Directed) Segments 5.3.1 Intersecting Segment against Plane 5.3.2 Intersecting Ray or Segment against Sphere 5.3.3 Intersecting Ray or Segment against Box 5.3.4 Intersecting Line against Triangle 5.3.5 Intersecting Line against Quadrilateral 5.3.6 Intersecting Ray or Segment against Triangle 5.3.7 Intersecting Ray or Segment against Cylinder 5.3.8 Intersecting Ray or Segment against Convex Polyhedron 5.4 Additional Tests 5.4.1 Testing Point in Polygon 5.4.2 Testing Point in Triangle 5.4.3 Testing Point in Polyhedron 5.4.4 Intersection of Two Planes 5.4.5 Intersection of Three Planes 5.5 Dynamic Intersection Tests 5.5.1 Interval Halving for Intersecting Moving Objects 5.5.2 Separating Axis Test for Moving Convex Objects 5.5.3 Intersecting Moving Sphere against Plane 5.5.4 Intersecting Moving AABB against Plane 5.5.5 Intersecting Moving Sphere against Sphere 5.5.6 Intersecting Moving Sphere against Triangle (and Polygon) 5.5.7 Intersecting Moving Sphere against AABB 5.5.8 Intersecting Moving AABB against AABB 5.6 Summary 6 Bounding Volume Hierarchies 6.1 Hierarchy Design Issues 6.1.1 Desired BVH Characteristics 6.1.2 Cost Functions 6.1.3 Tree Degree 6.2 Building Strategies for Hierarchy Construction 6.2.1 Top-Down Construction 6.2.1.1 Partitioning Strategies 6.2.1.2 Choice of Partitioning Axis 6.2.1.3 Choice of Split Point 6.2.2 Bottom-Up Construction 6.2.2.1 Improved Bottom-Up Construction 6.2.2.2 Other Bottom-Up Construction Strategies 6.2.2.3 Bottom-Up N-Ary Clustering Trees 6.2.3 Incremental (Insertion) Construction 6.2.3.1 The Goldsmith-Salmon Incremental Construction Method 6.3 Hierarchy Traversal 6.3.1 Descent Rules 6.3.2 Generic Informed Depth-First Traversal 6.3.3 Simultaneous Depth-First Traversal 6.3.4 Optimized Leaf-Direct Depth-First Traversal 6.4 Example Bounding Volume Hierarchies 6.4.1 OBB-Trees 6.4.2 AABB-Trees and BoxTrees 6.4.3 Sphere-Tree through Octree Subdivision 6.4.4 Sphere-Tree from Sphere-Covered Surfaces 6.4.5 Generate-and-Prune Sphere Covering 6.4.6 k-DOP Trees 6.5 Merging Bounding Volumes 6.5.1 Merging Two AABBs 6.5.2 Merging Two Spheres 6.5.3 Merging Two OBBs 6.5.4 Merging Two k-DOPs 6.6 Efficient Tree Representation and Traversal 6.6.1 Array Representation 6.6.2 Preorder Traversal Order 6.6.3 Offsets Instead of Pointers 6.6.4 Cache-Friendlier Structures (Non-Binary Trees) 6.6.5 Tree Node and Primitive Ordering 6.6.6 On Recursion 6.6.7 Grouping Queries 6.7 Improved Queries through Caching 6.7.1 Surface Caching: Caching Intersecting Primitives 6.7.2 Front Tracking 6.8 Summary 7 Spatial Partitioning 7.1 Uniform Grids 7.1.1 Cell Size Issues 7.1.2 Grids as Arrays of Linked Lists 7.1.3 Hashed Storage and Infinite Grids 7.1.4 Storing Static Data 7.1.5 Implicit Grids 7.1.6 Uniform Grid Object-Object Test 7.1.6.1 One Test at a Time 7.1.6.2 All Tests at a Time 7.1.7 Additional Grid Considerations 7.2 Hierarchical Grids 7.2.1 Basic Hgrid Implementation 7.2.2 Alternative Hierarchical Grid Representations 7.2.3 Other Hierarchical Grids 7.3 Trees 7.3.1 Octrees (and Quadtrees) 7.3.2 Octree Object Assignment 7.3.3 Locational Codes and Finding the Octant for a Point 7.3.4 Linear Octrees (Hash-Based) 7.3.5 Computing the Morton Key 7.3.6 Loose Octrees 7.3.7 k-d Trees 7.3.8 Hybrid Schemes 7.4 Ray and Directed Line Segment Traversals 7.4.1 k-d Tree Intersection Test 7.4.2 Uniform Grid Intersection Test 7.5 Sort and Sweep Methods 7.5.1 Sorted Linked List Implementation 7.5.2 Array-Based Sorting 7.6 Cells and Portals 7.7 Avoiding Retesting 7.7.1 Bit Flags 7.7.2 Time Stamping 7.7.3 Amortized Time Stamp Clearing 7.8 Summary 8 BSP Tree Hierarchies 8.1 BSP Trees 8.2 Types of BSP Trees 8.2.1 Node-Storing BSP Trees 8.2.2 Leaf-Storing BSP Trees 8.2.3 Solid-Leaf BSP Trees 8.3 Building the BSP Tree 8.3.1 Selecting Dividing Planes 8.3.2 Evaluating Dividing Planes 8.3.3 Classifying Polygons with Respect to a Plane 8.3.4 Splitting Polygons against a Plane 8.3.5 More on Polygon splitting Robustness 8.3.6 Tuning BSP Tree Performance 8.4 using the BSP Tree 8.4.1 Testing Point against a Solid-Leaf BSP Tree 8.4.2 Intersecting Ray against a Solid-Leaf BSP Tree 8.4.3 Polytope Queries on Solid-Leaf BSP Trees 8.5 Summary 9 Convexity-Based Methods 9.1 Boundary-Based Collision Detection 9.2 Closest Features Algorithms 9.2.1 The V-Clip Algorithm 9.3 Hierarchical Polyhedron Representations 9.3.1 The Dobkin-Kirkpatrick Hierarchy 9.4 Linear and Quadratic Programming 9.4.1 Linear Programming 9.4.1.1 Fourier-Motzkin Elimination 9.4.1.2 Seidel's Algorithm 9.4.2 Quadratic Programming 9.5 The Gilbert-Johnson-Keerthi Algorithm 9.5.1 The Gilbert-Johnson-Keerthi Algorithm 9.5.2 Finding the Point of Minimum Norm in a Simplex 9.5.3 GJK, Closest Points and Contact Manifolds 9.5.4 Hill-Climbing for Extreme Vertices 9.5.5 Exploiting Coherence by Vertex Caching 9.5.6 Rotated Objects Optimization 9.5.7 GJK for Moving Objects 9.6 The Chung-Wang Separating Vector Algorithm 9.7 Summary 10 GPU-Assisted Collision Detection 10.1 Interfacing with the GPU 10.1.1 Buffer Readbacks 10.1.2 Occlusion Queries 10.2 Testing Convex Objects 10.3 Testing Concave Objects 10.4 GPU-Based Collision Filtering 10.5 Summary 11 Numerical Robustness 11.1 Robustness Problem Types 11.2 Representing Real Numbers 11.2.1 The IEEE-754 Floating-Point Formats 11.2.2 Infinity Arithmetic 11.2.3 Floating-Point Error Sources 11.3 Robust Floating-Point Usage 11.3.1 Tolerances Comparisons for Floating-Point Values 11.3.2 Robustness through Thick Planes 11.3.3 Robustness through Sharing of Calculations 11.3.4 Robustness of Fat Objects 11.4 Interval Arithmetic 11.4.1 Interval Arithmetic Examples 11.4.2 Interval Arithmetic in Collision Detection 11.5 Exact and Semi-Exact Computation 11.5.1 Exact Arithmetic using Integers 11.5.2 On Integer Division 11.5.3 Segment Intersection using Integer Arithmetic 11.6 Further Suggestions for Improving Robustness 11.7 Summary 12 Geometrical Robustness 12.1 Vertex Welding 12.2 Computing Adjacency Information 12.2.1 Computing a Vertex-to-Face Table 12.2.2 Computing an Edge-to-Face Table 12.2.3 Testing Connectedness 12.3 Holes, Cracks, Gaps, and T-Junctions 12.4 Merging Coplanar Faces 12.4.1 Testing Coplanarity of Two Polygons 12.4.2 Testing Polygon Planarity 12.5 Triangulation and Convex Partitioning 12.5.1 Triangulation by Ear Cutting 12.5.1.1 Triangulating Polygons with Holes 12.5.2 Convex Decomposition of Polygons 12.5.3 Convex Decomposition of Polyhedra 12.5.4 Dealing with "Nondecomposable" Concave Geometry 12.6 Consistency Testing using Euler's Formula 12.7 Summary 13 Optimization 13.1 CPU Caches 13.2 Instruction Cache Optimizations 13.3 Data Cache Optimizations 13.3.1 Structure Optimizations 13.3.2 Quantized and Compressed Vertex Data 13.3.3 Prefetching and Preloading 13.4 Cache-Aware Data Structures and Algorithms 13.4.1 A Compact Static k-d Tree 13.4.2 A Compact AABB Tree 13.4.3 Cache-Obliviousness 13.5 Software Caching 13.5.1 Cached Linearization Example 13.5.2 Amortized Predictive Linearization Caching 13.6 Aliasing 13.6.1 Type-Based Alias Analysis 13.6.2 Restricted Pointers 13.6.3 Avoiding Aliasing 13.7 Parallelism through SIMD Optimizations 13.7.1 4 Spheres versus 4 Spheres SIMD Test 13.7.2 4 Spheres versus 4 AABBs SIMD Test 13.7.3 4 AABBs versus 4 AABBs SIMD Test 13.8 Branching 13.9 Summary References Index

unity小游戏源码超级闪躲小游戏模板Collision HIT 1.0C#语言开发。完整的源拿来学习研究很不错也可以可直接运营。

碰撞检测仪 UCLA MAE M20：MatLab中的计算机编程 结果 概要 用MatLab编写的基本碰撞检测模拟器。 该程序模拟了N个粒子和矩形边界之间的碰撞。 基本模拟器分为4部分： 通过UNIFORM GRID算法的碰撞检测功能 确定碰撞时间的功能 时间步长和解决冲突的功能 将模拟渲染为电影文件 奖励功能 为了获得更多荣誉，已实现以下功能： 引力作用 非弹性碰撞 布朗动力学 异构磁盘质量 异构磁盘半径 周期性边界条件 颗粒汇/源 报告中提供了更多详细信息和详细说明。 上次编辑时间：2014年秋季 想要查询更多的信息：

布里萨帕蒂 :pushpin: 介绍 这是各种机器学习算法和实验的集合，通过遵循各种教程，文章博客等内容，这些知识已经在我这边实现了。 这些机器学习算法已在来自 ， 等的各种数据集上实现。 :check_mark: 资源 :collision: 笔记本和数据集 姓名 数据集 笔记本 亚马逊情绪分析 使用转移学习进行COVID-19检测 猫狗分类器 使用LSTM的聊天机器人 决策树 假新闻分类 性别预测 印地语字符识别 鸢尾花预测 K均值聚类 线性回归I 线性回归II 线性回归III 逻辑回归 MNIST时尚数据集 朴素贝叶斯 强化学习 葡萄酒数据集 时间序列分析 垃圾邮件检测 IMDB情绪分类 卫星影像分析

避免加速度和速度障碍的相互碰撞 我们提出了一种考虑到加速约束的移动机器人避碰方法。 我们讨论了在移动障碍物中导航单个机器人的情况，以及在导航公共工作空间时相互避免碰撞的多个机器人的情况。 受速度障碍概念的启发，我们引入了加速度－速度障碍（AVO），以使机器人在遵守加速度约束的同时避免与移动障碍物发生碰撞。 AVO表征了机器人可以安全地达到并采用的比例控制加速度所采用的新速度。 通过让每个机器人承担避免成对碰撞的责任的一半，我们将此概念扩展为针对多机器人设置的相互避免碰撞。 我们的设计可确保无冲突导航，即使机器人独立且同时行动而无需协调。 我们的方法是为完整的机器人设计的，但也可以应用于运动约束非完整的机器人，例如汽车。 我们已经实现了我们的方法，并且在具有大量机器人和障碍物的具有挑战性的环境中显示了仿真结果。 版权所有2010北卡罗莱纳大学教堂山分校 根据Apache许可版本2.0（“