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Towards Context-Aware Navigation for Long-Term Autonomy in - - PowerPoint PPT Presentation

Towards Context-Aware Navigation for Long-Term Autonomy in Agricultural Environments A U TH ORS : M A R K H L L M A NN , BENJ A MI N K I S L IUK , J A N C H R I STOP H K R A U SE , C H R I S TOPH TI EB EN , A L EX A NDER M


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Towards Context-Aware Navigation for Long-Term Autonomy in Agricultural Environments

A U TH ORS : M A R K H ÖL L M A NN ∗, BENJ A MI N K I S L IUK∗, J A N C H R I STOP H K R A U SE∗, C H R I S TOPH TI EB EN∗, A L EX A NDER M OCK †, S EBA STIAN P Ü TZ †, F EL I X I G EL B R IN K†, TH OM A S W I EM A NN ∗†, S A NTIAG O F OCK E M A RTI NEZ ∗, S TEFA N S TI EN E∗, J OA CH I M H ER TZ BERG ∗† S P EA KER : BENJ A M IN K I S LIU K ∗ D F K I L A B L OW ER S A X ONY, P L A N B A S ED R OB OT C ON TR OL † U NI V ERSI TY OF OS NA BRÜCK, K NOW L EDG E BA S ED S Y S TEMS

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Motivation

The future of agriculture is already a central driver for robotic innovation

  • Integrated long term autonomy benefits

robotic applications

  • Farms are non-standardized work

environments with large, heterogeneous areas

  • Robots need to work in and switch

between different contexts

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Prior and Related Work

Navigation:

  • Eband, DWA, Global Planner, Move Base Flex,

Localization:

  • Robot localization, AMCL

Control Architecture:

  • SMACH

Environment Modelling & Representation:

  • Waypoint Server, Costmap 2D, QGis

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Experimental Setup

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Experimental Setup

Autonomous Robotic Experimental Platform:

  • RTK Multi-GNSS
  • WIFI & 4G Data uplink
  • Multiple 2D and 3D LIDAR sensors

Base Station Container:

  • WIFI & 4G Data Uplink
  • Inductive Charging Station
  • Monitoring and Data Server

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Context Aware Navigation

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Different contexts in different areas:

  • Area usage / crop type
  • Time of day
  • Season
  • State of an agricultural process

Topological planning:

  • Abstract Path Planning
  • Each zone represents a context
  • Different set of parameters for each

context

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Context Aware Navigation

Different contexts in different areas:

  • Area usage / crop type
  • Time of day
  • Season
  • State of an agricultural process

Topological planning:

  • Abstract Path Planning
  • Each zone represents a context
  • Different set of parameters for each

context

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Context Aware Navigation

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Different contexts in different areas:

  • Area usage / crop type
  • Time of day
  • Season
  • State of an agricultural process

Topological planning:

  • Abstract Path Planning
  • Each zone represents a context
  • Different set of parameters for each

context

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Context Aware Navigation

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Context Aware Navigation

  • Goal and robot pose are added into the graph as

temporary vertices

  • These vertices are connected with all waypoints

in the same zone

  • Dijkstra's algorithm to compute path in the graph
  • Each edge traversed is a path segment
  • Move base flex is called to execute the segment

with parameters given from the zone model

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Context Aware Navigation

  • Goal and robot pose are added into the graph as

temporary vertices

  • These vertices are connected with all waypoints

in the same zone

  • Dijkstra's algorithm to compute path in the graph
  • Each edge traversed is a path segment
  • Move base flex is called to execute the segment

with parameters given from the zone model

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Context Aware Navigation

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Video

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Future Work – System Enhancements

Further extend Move Base Flex:

  • Adapt costmap representation
  • More specialized controllers
  • Automated zone generation

From Contexts to Semantics:

  • Enrich Zones with semantically inferable information
  • Generate Zones with logical reasoning
  • Adapt behaviour based on semantic knowledge

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Conclusion

  • Integration of multiple heterogenous contexts is required to achieve long term

autonomy

  • Hierarchical abstraction with flexible behaviour implementation facilitates the adaption
  • f robotic behaviour according to semantic inference
  • The flexible architecture allows different state-of-the-art software stacks to be applied

where most beneficial

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Acknowledgements

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Sponsored by the

Work by the University of Osnabrück and DFKI is supported by the German Federal Ministry of Education and Research in the project SoilAssist2 (Grant No. 031B0684D) and by the German Federal Ministry of Food and Agriculture within the experimental field Agro-Nordwest project (Grant No. 28DE103E18) and by the Ministry of Science and Culture of Lower Saxony and within the Zukunftslabor Agrar (11-76251-14-3/19 (ZN3490)