AUTONOMOUS ROVER PATH PLANNING AND ... - European Space...

1AEVO GmbH ‐ All rights reserved 13.4.2011

AUTONOMOUS ROVER PATH PLANNING AND RECONFIGURATION

J. Graciano, E. Chester

11th Symposium on Advanced Space Technologies in Robotics and AutomationESTEC, 11‐15 April 2011


AEVO GmbH

R&D company with HQ in Gilching/Oberpfaffenhofen (Munich)

Founded in March 2010 in the framework of the ESA BIC Initiative

Focus on technology transfer of computer‐based optimisation

Projects in the areas of: Component design

Communication scheduling

Mission design

Logistics

Member of bavAIRia

COMPANY


DESIGNTools that explore the system

design space to findthe most robust options.

SCHEDULETools that optimise

task or team schedulesto improve resource usage.

OPTIMISATION ENGINES

DEVELOPTools that find the best trade‐off

between conflicting objectives without violating the constraints.

OPTIMISETools that fine‐tune a system

calibration to increaseefficiency and representation quality.

OPTIMISATIONENGINES

AEVO's Optimisation Engines are tools that optimise the process ofdesigning, developing, testing and fine‐tuning systems.


TECHNOLOGIES

Algorithms

Interpolation, Least Squares

Gradient Methods

Monte‐Carlo Algorithms

Stochastic Search

Game Theory

Evolutionary Methods

Neural Networks

(...)

Software

Matlab/Simulink

SciLab

Fortran

C/C++

Perl

XML

(...)

OPTIMISATIONENGINES

AEVO's Optimisation Enginesuse different methods and algorithms for each specific problem


Multi‐user, multi‐objective, multi‐mission spacecraft and ground segment scheduling tool

MOSATS

Objective:Optimisation of the satellite communication windows for multiple ground stations and multiple satellites. This is a highly constrained problem, where the objectives incide on the constraint satisfiability and on the maximisation of scientific return.

Key advantages:

Easy to use, web‐based front‐end

Versatile integration support with existing tools through use of XSLT/XPath transformations

Support for ESA‐ESOC, NASA‐JPL, and AGI’s STK data formats

Rapid scenario definition and disruption assessment


Scheduling Infrastructure for Resource Optimisation

SIROCCO

Objective:In the framework of a validation project with support of the DLR‐GSOC, AEVO is developing a software for the resource optimisation of spacecraft on‐board tasks. The objective is to plan and schedule all necessary tasks in a defined time period, arising from 2 sources: payload operations requests and operating rules and procedures related to the operations.

Some tasks considered are: Uplinks

Payload Operations

Downlinks

File deletion

Sleep phases

Attitude manoeuvres


Parametric Entry, Descent and Landing Synthesis

PEDALS

Objective:Demonstration of a design‐by‐evolution approach for developing mission architectures for entry vehicles.

Aspects considered: Aerodynamic shape optimisation

Atmospheric flight simulation

Multiple decelerator concepts

Parachute/Ballute/Thruster sizing

Sleep phases

Attitude changed

Integration of MOLA reference topology

Integration of Mars‐GRAM engineeringatmosphere model of Mars

Demonstrate concept validity without use ofITAR‐restricted codes


RATIONALE & SCOPERationaleEfficient and confident route planning for rover operationsAdaptive re‐planning (reacts to introduction of other actors, changes in mission, changing priorities, terrain hazards, etc.)Continuous trajectory design

Scope

Development of minima‐free artificial potential (AP) method

Validation for realistic scenarios

Simulation work to examine flexibility and suitability of the method in dealing with:

‐ Static conflicts (obstacle avoidance)‐ Dynamic conflicts (re‐planning)‐ Targeting


MODELLING (1)ARTIFICIAL POTENTIALS

Obstacles are represented by repulsive potentials

Goals represented by attractive potentials

Total potential field is built through weighted sum of all individual APs

Gradient of total field is used to navigate, from

high potential states to low potential states

Drawback: unless functions are carefullychosen, the potential field suffers fromlocal minima, causing undesired stops

Schematic potential field withconvergence and avoidance sets [2]


MODELLING (2)GAMES

Mathematical modeling of situations of interaction involving two or more agents

Usually comprises [3]:‐ set of players

‐ set of possible moves

‐ payoffs awarded to each player in function of all moves combinations

‐ rules of the game (may include move precedence, restrictions on memory)

Nash equilibrium: no incentive to unilateral departure


MODELLING (3)Orthogonality between "players" Target gradient field

Obstacle gradient field Total gradient field


MODELLING (4)Functions


RESULTSFIELD INTENSITY (AVOIDANCE STRENGTH)

F = 0.2 F = 2.0


RESULTSFIELD TYPE

(Regions to avoid

are red, to prefer

are green)


RESULTSFIELD FUNCTION



R = 1


RESULTSFIELD INTENSITY

R = 0.5 R = 1.5

(demo)


RESULTS


FURTHER ACTIVITIESDevelopment

Implementation (demonstration project)

Improve modelling (3D, realistic dynamics and constraints)

Additional features (shadow of the future)

Applications

Planetary rovers

On‐Orbiting servicing / docking

UAV (single, formation)

General robot routing


SUMMARY Demonstration of path planning and path optimisation for platforms in

the presence of multiple obstacles and terrain hazards.

Main advantages: No "local‐minima" problem

Rapid, robust algorithm suitable for embedded navigation executive

Broader use of potential functions

Aspect analysed: Static conflicts

Dynamic conflicts

Path re‐planning in dynamic environment

Selective target activation (for autonomous science planning)

Also:

Chase/escape situations

Formation flying / team motion


João Graciano

AEVO GmbHFriedrichshafenerstrasse 182205 GilchingTel: +49 8105 772 7757Fax: +49 8105 772 7755

QUESTIONS?

Thank you for your attention


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