Path Planning for Multiple Marine Vehicles Andreas J. Häusler¹, Reza Ghabcheloo², Isaac Kaminer³ António M. Pascoal¹, A. Pedro Aguiar¹ ¹Instituto Superior.

Slides:



Advertisements
Similar presentations
Optimal Path planning for (Unmanned) Autonomous Vehicles, UAVs Objective: The main aim of the project is to find out the optimal path or trajectory including.
Advertisements

INSTITUTO DE SISTEMAS E ROBÓTICA Online Model Identification for Set-valued State Estimators With Discrete-Time Measurements João V. Messias Institute.
ROBOT RENDEZVOUS: 3 OR MORE ROBOTS USING 1-DIMENSIONAL SEARCH !!!!!!! !
Dynamic Object Tracking in Wireless Sensor Networks Tzung-Shi Chen 1, Wen-Hwa Liao 2, Ming-De Huang 3, and Hua-Wen Tsai 4 1 National University of Tainan,
Active Contours, Level Sets, and Image Segmentation
Synchronous Maneuvering of Uninhabited Air Vehicles
S ystems Analysis Laboratory Helsinki University of Technology Near-Optimal Missile Avoidance Trajectories via Receding Horizon Control Janne Karelahti,
Mission Planning Multiple vehicle missions require the vehicles to be in formation An initial formation has to be established before the mission starts.
Carnegie Mellon University TRAJECTORY MODIFICATION TECHNIQUES IN COVERAGE PLANNING By - Sanjiban Choudhury (Indian Institute of Technology, Kharagpur,
Florian Klein Flocking Cooperation with Limited Communication in Mobile Networks.
Robust and Efficient Control of an Induction Machine for an Electric Vehicle Arbin Ebrahim and Dr. Gregory Murphy University of Alabama.
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Reciprocal Velocity Obstacles for Real-Time Multi-Agent Navigation Jur van den Berg Ming Lin Dinesh Manocha.
INTRODUCTION TO DYNAMICS ANALYSIS OF ROBOTS (Part 6)
Early Research Presentation Optimal and Feasible Attitude Motions for Microspacecraft January 2013 Albert Caubet.
Oklahoma State University Generative Graphical Models for Maneuvering Object Tracking and Dynamics Analysis Xin Fan and Guoliang Fan Visual Computing and.
Centre for Autonomous Systems Petter ÖgrenCAS talk1 A Control Lyapunov Function Approach to Multi Agent Coordination P. Ögren, M. Egerstedt * and X. Hu.
Local or Global Minima: Flexible Dual-Front Active Contours Hua Li Anthony Yezzi.
EE631 Cooperating Autonomous Mobile Robots Lecture 5: Collision Avoidance in Dynamic Environments Prof. Yi Guo ECE Dept.
Temporally and Spatially Deconflicted Path Planning for Multiple Marine Vehicles A. Häusler 1, R. Ghabcheloo 2, A. Pascoal 1, A. Aguiar 1 I. Kaminer 3,
Multiple Marine Vehile Deconflicted Path Planning with Currents and Communication Constraints A. Häusler 1, R. Ghabcheloo 2, A. Pascoal 1, A. Aguiar 1.
1 Mobile Sensor Network Deployment using Potential Fields : A Distributed, Scalable Solution to the Area Coverage Problem Andrew Howard, Maja J Mataric´,
CS 326 A: Motion Planning Coordination of Multiple Robots.
San Diego 7/11/01 VIRTUAL SHELLS FOR AVOIDING COLLISIONS Yale University A. S. Morse.
Implementation of RRT based Path planner and conversion into Temporal Plan Network By: Aisha Walcott Final Project Presentation Dec. 10, J.
CS 326 A: Motion Planning 2 Dynamic Constraints and Optimal Planning.
P. Ögren (KTH) N. Leonard (Princeton University)
Empirical Virtual Sliding Target Guidance law Presented by: Jonathan Hexner Itay Kroul Supervisor: Dr. Mark Moulin.
1 Optimization of ALINEA Ramp-metering Control Using Genetic Algorithm with Micro-simulation Lianyu Chu and Xu Yang California PATH ATMS Center University.
Steering Behaviors For Autonomous Characters
CS 326 A: Motion Planning Coordination of Multiple Robots.
DAMN : A Distributed Architecture for Mobile Navigation Julio K. Rosenblatt Presented By: Chris Miles.
Performance Guarantees for Hazard Based Lateral Vehicle Control
Multi-vehicle Cooperative Control Raffaello D’Andrea Mechanical & Aerospace Engineering Cornell University u Progress on RoboFlag Test-bed u MLD approach.
Chuang-Hue Moh Spring Embodied Intelligence: Final Project.
Aeronautics & Astronautics Autonomous Flight Systems Laboratory All slides and material copyright of University of Washington Autonomous Flight Systems.
INTEGRATED PROGRAMME IN AERONAUTICAL ENGINEERING Coordinated Control, Integrated Control and Condition Monitoring in Uninhabited Air-Vehicles Ian Postlethwaite,
Constraints-based Motion Planning for an Automatic, Flexible Laser Scanning Robotized Platform Th. Borangiu, A. Dogar, A. Dumitrache University Politehnica.
Robot Crowd Navigation using Predictive Position Fields in the Potential Function Framework Ninad Pradhan, Timothy Burg, and Stan Birchfield Electrical.
Vectors Readings: Chapter 3. Vectors Vectors are the objects which are characterized by two parameters: magnitude (length) direction These vectors are.
Flow Fields Hao Li and Howard Hamilton. Motivation for Flow Fields Multiple AI algorithms in a computer game can produce conflicting results. The AI must.
Richard Patrick Samples Ph.D. Student, ECE Department 1.
Collaborative Mobile Robots for High-Risk Urban Missions Report on Timeline, Activities, and Milestones P. I.s: Leonidas J. Guibas and Jean-Claude Latombe.
Mobile Robot Navigation Using Fuzzy logic Controller
1 Distributed and Optimal Motion Planning for Multiple Mobile Robots Yi Guo and Lynne Parker Center for Engineering Science Advanced Research Computer.
Dynamic Self-Organization & Computation by Natural and Artificial Potential Fields John H Reif Duke University Download:
Chapter 7: Trajectory Generation Faculty of Engineering - Mechanical Engineering Department ROBOTICS Outline: 1.
AS-RIGID-AS-POSSIBLE SHAPE MANIPULATION
Advanced Control of Marine Power System
S ystems Analysis Laboratory Helsinki University of Technology Automated Solution of Realistic Near-Optimal Aircraft Trajectories Using Computational Optimal.
Design, Optimization, and Control for Multiscale Systems
S& EDG: Scalable and Efficient Data Gathering Routing Protocol for Underwater Wireless Sensor Networks 1 Prepared by: Naveed Ilyas MS(EE), CIIT, Islamabad,
Real-time motion planning for Manipulator based on Configuration Space Chen Keming Cis Peking University.
1 Motion Fuzzy Controller Structure(1/7) In this part, we start design the fuzzy logic controller aimed at producing the velocities of the robot right.
NATIONAL AVIATION UNIVERSITY Institute of Air navigation Air Navigation Systems Department Graduate work MASTER’S DEGREE THESIS «Modeling of flight using.
School of Systems, Engineering, University of Reading rkala.99k.org April, 2013 Motion Planning for Multiple Autonomous Vehicles Rahul Kala Rapidly-exploring.
City College of New York 1 John (Jizhong) Xiao Department of Electrical Engineering City College of New York Mobile Robot Control G3300:
Optimal Path Planning Using the Minimum-Time Criterion by James Bobrow Guha Jayachandran April 29, 2002.
Mobile Sensor Network Deployment Using Potential Fields: A Distributed, Scalable Solution to the Area Coverage Problem Andrew Howard, Maja J Matari´c,
Antigone Engine. Introduction Antigone = “Counter Generation” Library of functions for simplifying 3D application development Written in C for speed (compatible.
Optimal Trajectory for Network Establishment of Remote UAVs –1–1 Prachya Panyakeow, Ran Dai, and Mehran Mesbahi American Control Conference June 2013.
Unpredictable Software-based Attestation Solution for Node Compromise Detection in Mobile WSN Xinyu Jin 1 Pasd Putthapipat 1 Deng Pan 1 Niki Pissinou 1.
4/22/20031/28. 4/22/20031/28 Presentation Outline  Multiple Agents – An Introduction  How to build an ant robot  Self-Organization of Multiple Agents.
Mid Term Review Andreas J. Häusler FREEsubNET MCRTN-CT
Optimal Planning for Vehicles with Bounded Curvature: Coordinated Vehicles and Obstacle Avoidance Andy Perrin.
Yueshi Shen Dept. of Information Engineering
Vesa Klumpp, Knowtion Applications of Intelligent Control in Industry and Adaption to Space Missions Vesa Klumpp, Knowtion
EE631 Cooperating Autonomous Mobile Robots Lecture: Collision Avoidance in Dynamic Environments Prof. Yi Guo ECE Dept.
TOWARDS A DESIRED TRANSPORT FUTURE: SAFE, SUFFICIENT AND AFFORDABLE
Market-based Dynamic Task Allocation in Mobile Surveillance Systems
Chapter 4 . Trajectory planning and Inverse kinematics
Presentation transcript:

Path Planning for Multiple Marine Vehicles Andreas J. Häusler¹, Reza Ghabcheloo², Isaac Kaminer³ António M. Pascoal¹, A. Pedro Aguiar¹ ¹Instituto Superior Técnico, Lisbon, Portugal ²Tampere University of Technology, Tampere, Finland ³Naval Postgraduate School, Monterey, USA

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Motivation & Problem Statement Widening fields of application Increasing demand for autonomous robots Trend towards networked systems Presence of stringent limitations (dynamical constraints, energy, external disturbances) Robust path planning methods required

Mother Ship Current Introduction

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Introduction Multiple vehicle missions require the vehicles to be in formation An initial formation pattern must be established before the mission starts Lack of hovering capabilities  vehicles cannot be deployed and brought to formation separately Need to drive the vehicles to an initial formation pattern in a concerted manner.

Go-To-Formation Maneouvre

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Go-To-Formation Maneouvre Launch of multiple marine vehicles Formation to be reached before mission starts Simultaneous arrival time and equal speeds Collision avoidance & deconfliction clearance

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Spatial Deconfliction Vehicle 1 Vehicle 2 Initial positions Final positions (target formation)

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Temporal Deconfliction Final positions (target formation) Vehicle 1 Vehicle 2Initial positions Intermediate positions

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles MULTIPLE VEHICLE PATH PLANNING SYSTEM Initial Positions Initial Velocities Final Positions Final Velocities Vehicle dynamical constraints External constraints (e.g., obstacles) Cost criterion (e.g.weighted sum of energies, maneuvering time) Nominal Paths and Speed Profiles Vehicle collision avoidance constraints Path Planning System

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Path Planning: an overview Core methodology: based on the work on single vehicle path planning using direct methods (Yakimenko) Extension to multiple air vehicle path planning with spatial deconfliction ( Yakimenko, Kaminer, Pascoal) Extension to multiple marine vehicle path planning with temporal deconfliction (Aguiar, Ghabcheloo, Häusler, Kaminer, Pascoal)

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Decoupling of Space and Time Reduces number of opt. parameters  suitable for real-time implementation Single vehicle path Parameterized by Polynomial for each coordinate Degree determined by no. of boundary conditions Original work by Yakimenko and Kaminer

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Decoupling of Space and Time Optimization produces paths without time constraints, but with timing laws Evolution of with time is Temporal speedand acceleration

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Flexibility of Generated Paths Path shape can be changed by varying Simple choice Path geometry is “shaped” simply by varying

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Multiple Vehicle Path Generation Cost (Energy consumption) Constraints Optimize using zero order methods Spatial deconfliction: subject to

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Simulation Results Spatial Deconfliction in 2D

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Simulation Results Spatial Deconfliction in 3D

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Temporal Deconfliction Deconfliction constraint changes to Simultaneous arrival at time Time-coordinated path following using virtual time Cooperation to adjust vehicle motions in reaction to deviations from original plan (Ghabcheloo)

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Temporal Deconfliction Common path parametrization variable

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Simulation Results Temporal Deconfliction in 2D

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Conclusions Use of direct optimization methods (Yakimenko) Efficient and fast techniques for path generation first used for UAVs (Kaminer et al.) Extension to temporal deconfliction allows for equal times of arrival Decoupling of space and time, resulting in great flexibility for time-coordinated path following

May 13th, Oceans '09 IEEE Bremen Häusler et al. - Path Planning for Multiple Marine Vehicles Future Trends Incorporate effects of current fields Add obstacle avoidance Improve optimization techniques towards online re-planning Sea tests with multiple marine vehicles planned for the Fall of 2009

Thank you for your attention! Delfim (IST/ISR) Infante (IST/ISR) ASTER x (IFR) Seawolf (ATL) Arquipélago (IMAR) Delfim X (IST/ISR)