PPT-GPU-based Parallel Collision Detection for Real-time Motion Planning

Author : marina-yarberry | Published Date : 2018-11-13

Jia Pan and Dinesh Manocha University of North Carolina Chapel Hill USA httpgammacsuncedugplanner Presenter Liangjun Zhang Stanford University Realtime Motion

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GPU-based Parallel Collision Detection for Real-time Motion Planning: Transcript


Jia Pan and Dinesh Manocha University of North Carolina Chapel Hill USA httpgammacsuncedugplanner Presenter Liangjun Zhang Stanford University Realtime Motion Planning Dynamicuncertaindeformable environments. Scalable Avoidance and Group Behavior (2008). Authors. : . Yersin. , . Maïm. , . Morini. , . Thalman. Presented. . by. : Jessica . Siewert. Content of presentation. The Method:. Previous work. The architecture. Collision Avoidance. Inefficient airspace utilization. Increased air traffic workload. Use of obsolete technologies. 2. Requirements. Increase flexibility of vehicle operations. Fly higher number of vehicles in smaller airspace. Space-Time Footsteps for . Agent-Based Steering. By . Glen Berseth. 1. , . Mubbasir. Kapadia. 2. , . Petros. Faloutsos. 3. University of British Columbia. 1. , Rutgers University. 2. , York University. Peter Brook. Matei. . Ciocarlie. Kaijen. Hsiao. Uncertainty. in Object Perception. Sensed Scene. Real Scene. Collaborative Grasp Planning with Multiple Object Representations. 2. Uncertainty. in Object . Our Focus. Victim. Attacker. Malicious. Web. Exploit. browser. Phishing. Exploit. vulnerability. Code Repo. Database. Malware . propagation. Initial Compromise. Gaining Foothold. Lateral Movement. High Value Asset Acquisition. Collision Detection. Probabilistic Roadmaps. How to test for. collision?. Collision Detection Methods. Many different methods. In particular:. Grid method. : good for many simple moving objects of about the same size (e.g., many moving discs with similar radii). . motion planning. J. . Pettré. , J.P. Laumond,. A motion capture . based. . control-space. . approach. for . walking. mannequins. Computer Animation and Virtual . Worlds. , Vol. 16, 2006.. C. . real-time, . parallel and distributed adaptive object detection and retraining framework based on . adaboost. algorithm. 1. Munther Abualkibash. University of Bridgeport, CT. Outline. 2. Introduction . Kris Hauser. ECE 383 / ME 442. Fall 2015. 3D models in robotics. Design. Simulation. Robot collision detection (i.e. prediction). Proximity calculation. Map building. Object recognition. Grasp planning. Rahul Kala. Literature Review. Organization. rkala.99k.org. Trajectory Planning. Current . Intelligent Vehicles . algorithms cannot be used as:. Lane prone. Simple obstacle frameworks. Non-cooperative. By: Matt . Boggus. Outline. Collision tests. Pixel-pixel. Square-square. Axis-aligned bounding box (rectangle) testing. Collision detection loops. Exhaustive comparison. Static objects vs. dynamic objects. Kris Hauser. I400/B659: Intelligent Robotics. Spring 2014. 3D models in robotics. Design. Simulation. Robot collision detection (i.e. prediction). Proximity calculation. Map building. Object recognition. Team Member : Zhen Hu, . Hanqing Duan. Mentor: Dr. Russell Taylor, Dr. Deepa . Galaiya. Seminar Presentation. Project Summary. Goal: . Accurate Measurement of Neck Flexion Angle during Thyroid and Ear Surgery by using IMUs. TH TH H E R E R EAL EAL O ON N E S E S T EP R Pathogen and product description species are gram-negative, nonspore forming, spiral, or curved-shaped bacteria. Among the more than 26 species currentl

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