calisi motion

8/3/2019 Calisi Motion

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Motion planning, control andMotion planning, control and

obstacle avoidanceobstacle avoidance

D. Calisi


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Motion planning, control and obstacle avoidance - OpenRDK Workshop - March 2009 2

IntroductionIntroduction

Robot mobility is a keyfeature for any mobile robot

Robot motion is eminently

necessary since, bydefinition, a robot

accomplishes tasks by

moving in the real world

(J.C. Latombe)


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Problem formalization: configuration spaceProblem formalization: configuration space

U1

U2

U4

U3

U2

U1

c1

c2

Cfree

Cobst

Challenges Configuration space is difficult to compute

Complexity, uncertainty, dynamic environments

Kinematic and dynamic constraints

Time constraints


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Problem decompositionsProblem decompositions

The problem is hard to solve, mainly because of the time

constraint The problem is decomposed in two or more levels,

relaxing some constraints or reducing the search space

Examples First compute the geometric curve, then add a time-law on it

Relax some constraints or perform a dimensionality reduction ofthe search space, then use a complete planner with localinformation

Relax some constraints or perform a dimensionality reduction ofthe search space, then use a reactive method with localinformation

Latombes decomposition Global path-planner

Local obstacle avoidance algorithm (or a local planner)


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ComponentComponent--based subdivision and OpenRDK modulesbased subdivision and OpenRDK modules

Robot motion

algorithm

Environment representation

Robot model

Robot current configuration

Sensor readings

Target poses

Motion commands

Global planner

Local planner or

reactive method

Global mapper

Local obstacle

detector

Robot sensors Actuators

Global path

Local trajectory

Local target

Motion commands


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Motion planning, control and obstacle avoidance - OpenRDK Workshop - March 2009

Connecting the global planner and the obstacle avoidanceConnecting the global planner and the obstacle avoidance

The global planner

can provide different

kind of information

to the obstacle avoidance

algorithm

A local target is the

most general and can be

used straightforwardly

by most of the well-known

obstacle avoidancealgorithms


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PathPath--planner as a serviceplanner as a service

Path-plannerExplorer

Obstacle avoidance

Path requests

Path answers

Target pose

Motion commands

Local target


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World models for pathWorld models for path--planningplanning

The same environment can be

represented in very differentways

Grid-maps High memory requirements

Easy to build and maintain

Line-maps Line extraction and tracking

Topological maps Robus to noise

Difficult to build


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A*A*--based pathbased path--plannersplanners

Grid-based environment

representation

High resolution required

Geometric representation

Roadmap based

A* path search on a graph


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Topological path plannersTopological path planners

Topological

representations arecompact models

They use features of theenvironment, rather thandense metric information

From one hand they aremore robust to sensornoise and localizationerrors

On the other hand, it can

be difficult to build andmaintain a topologicalrepresentation


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Reactive methods: vector basedReactive methods: vector based

Potential field methods [Borenstein, 1989]

Vector field histogram [Borenstein, 1991, 1998, 2000]

Nearness diagram [Minguez & Montano, 2000]

In OpenRDK a vector based method has been implemented (YARN)


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Limits of vectorLimits of vector--based methodsbased methods

Nonholonomy and

dynamic constraints

are difficult to be

taken into account

Oscillations

(discontinuity in

control functions)


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Reactive methods: curveReactive methods: curve--basedbased

Curvature-Velocity [Simmons, 1996]

Dynamic Window Approach [Fox, Burgard, Thrun,

1997]


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A local reactive algorithm (1)A local reactive algorithm (1) Local obstacle map

Current laser points are integratedwith previous ones

Laser points are translated tosegments (recursive line fitting)

A set of trajectories is computed

Curves (e.g., clothoids)

Dynamic constraints Obstacles

An objective function is used tochoose the best trajectory tofollow

Straightness Target nearness/heading

Duration/length

Obstacle distance

Clothoid curve:

In car-like vehicles, control is on steering wheel (highways are

designed using clothoids)

In differential driven mobile robots, each wheel acceleration is

bounded


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A local reactive algorithm (2)A local reactive algorithm (2)


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Segway experimentSegway experiment

The user defines a path

The curve-based navigator follows the path


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Segway experimentSegway experiment

The user defines a path

The curve-based navigator follows the path


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Limits of curveLimits of curve--based methodsbased methods

Parameters in objective function are critical

Limited or no planning

[Stachniss, 2002]


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MoVeME frameworkMoVeME framework

Simulated experiments

From papers about

motion algorithms

From papers about

comparisons

Enriched with otherenvironments/situations

[Borenstein '91]

[Minguez '00][Fernandez '04]

[Stachniss '02]


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Comparisons of different world modelsComparisons of different world models

Grid-based map, A* path-planner, Dynamic Window ApproachTopological map, DWATopological map, Vector Field Histogram


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Thank you for your attentionThank you for your attention

Questions?


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Thank you for your attentionThank you for your attention

Questions?

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