Distributed and optimal motion planning for multiple mobile robots
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Distributed and Optimal Motion Planning for Multiple Mobile Robots Yi Guo and Lynne Parker Center for Engineering Science Advanced Research Computer Science and Mathematics Division Oak Ridge National Laboratory ICRA’02 Presentation on May 14, TP-11 OUTLINE Introduction

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Distributed and optimal motion planning for multiple mobile robots l.jpg

Distributed and Optimal Motion Planning for Multiple Mobile Robots

Yi Guo and Lynne Parker

Center for Engineering Science Advanced Research

Computer Science and Mathematics Division

Oak Ridge National Laboratory

ICRA’02 Presentation on May 14, TP-11


Outline l.jpg
OUTLINE Robots

  • Introduction

  • Premises and Problem Statement

  • Multi-Robot Motion Planning Algorithm

  • Implementation Examples

    • 3D Simulations

    • Nomad 200 Indoor Robot Experiments

    • ATRV-Mini All-Terrain Mobile Robot Experiments (underway)

  • Conclusions


Introduction l.jpg
Introduction Robots

  • Motion planning in dynamic environments with moving obstacles is NP-hard.

  • Simple reactive motion planning strategies cannot guarantee deadlock free and convergence.

  • Previous results either obtain optimal solutions through centralized and exhaustive computing, or achieve distributed implementations without considering optimization issues.

    • Distributed solutions (e.g., [Azarm & Schmidt, Carpin & Pagello]) use negotiation or insert random time delays to resolve conflicts;

    • Recent results (e.g., [LaValle & Hutchinson]) consider performance through centralized computing, not capable of real time re-planning.


Introduction4 l.jpg
Introduction Robots

  • Outdoor environment is more challenging with 3D terrain features and the requirement for online re-planning.

  • Need to deal with the constraint of computation expenses, the requirements of real time control and robust solutions.

  • Our new multi-robot motion planning algorithm:

    • Distributed;

    • Optimal (a global performance measurement defined and minimized);

    • Capable of operation in outdoor environments and real time re-planning.


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Assumptions Robots

  • Each robot has an assigned goal, and knows its start and goal locations.

  • Pre-defined map available

    • Indoor: static polygonal obstacles;

    • Outdoor: terrain elevation and traversability based on grid representation.

  • Onboard sensors detect discrepancy and revise map online

  • Communication devices broadcast messages

  • Robots move at constant fixed speeds

  • Robots switch instantaneously between fixed speed and halting.


Problem statement l.jpg
Problem Statement Robots

  • Multi-robot motion planning problem:

    Find collision-free sequence of traverse states for each robot from its start to its goal, minimizing:


Multi robot motion planning algorithm l.jpg
Multi-Robot Motion Planning Algorithm Robots

  • The computationally expensive problem by decomposing it into two modules: path planning and velocity planning.

  • D* search method is applied in both modules, based on either geometric or schedule formulations.

  • Optimization is achieved at the individual robot level by defining cost functions to minimize, and also at the team level by a global measurement function reflecting performance indices of interest as a team.

  • Robustness design is incorporated by defining safety margins in both modules.

Flow chart diagram of algorithm


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Multi-Robot Motion Planning Algorithm Robots

Step 1: Path planning:

  • D* search in free space produces optimal path Pi for each robot from the start to the goal minimizing cost function:

    Step 2: Path is broadcast across robots; collision (time-space) check produces a set of collision regions;

    Step 3: Coordination diagram constructed:

  • Each path Pi is a continuous mapping ;

  • denotes the set of points that place robot along path Pi;

  • Coordination space is defined

  • Collision regions marked as obstacles in coordination diagram.


Multi robot motion planning algorithm9 l.jpg
Multi-Robot Motion Planning Algorithm Robots

Step 4: Velocity planning:

  • D* search in coordination diagram,

    minimizing cost function:

    and producing velocity profile VPi and performance index Ki (each

    robot evaluates a set of costs);

    Step 5: Broadcasting VPi and Kiacross robots;

    Step 6: Global performance evaluation

  • Find the minimal Kl, select corresponding VPl as the optimal solution for velocity.

robot 3

robot 2

robot 1

Coordination Diagram


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Implementation: 3D Simulation Robots

Start locations

Robot 1

Robot 2

Robot 3

Velocity profile

Goal locations

Multiple paths in Mars-like terrain environment


Nomad 200 experiments l.jpg
Nomad 200 Experiments Robots

Robots at start positions

Environmental map

Left: Pre-defined map;

Middle:Robots at run;

Right:Encoder

trajectories.

Encoder trajectory records

Velocity profile

Robots in motion


Issues in design robustness l.jpg
Issues in Design Robustness Robots

  • Observations:

    • Localization errors;

    • Motion uncertainties:

      • Robot does not take equal unit time to track a unit distance;

      • Robot does not switch instantaneously between moving and stopping.

  • Robustness design:

    • Safety margin defined in path searching for localization errors;

    • Safety margin defined in velocity planning for motion uncertainties.


Atrv experiments l.jpg

Distributed Robots

Positioning and Mapping

Multi

Robot

Motion Planning

Motion

Control

ATRV Experiments

  • System integration issues:

  • Sensor selection and fusion

  • Software platform

  • Communications

  • GUI/simulator

Inter-Robot

Communication

Sensing

Robot Poses,

3D Map

Paths

Inter-Robot

Communication

Actuation


Conclusions l.jpg
Conclusions Robots

  • We designed a 3D multi-robot motion planning algorithm that is distributed, optimal, and capable of real time re-planning in outdoor environment.

  • The computationally expensive problem is decomposed into two modules: path planning and velocity planning.

  • D* search method is applied in both modules, based on either geometric or schedule formulations.

  • The algorithm is implemented and validated in a 3D simulator, and experiment validation on groups of Nomad 200 indoor robots was done.

  • Robustness design is incorporated in the algorithm to overcome motion and sensor uncertainties.

  • Experiments on ATRV-mini robots in outdoor natural environments are underway.


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