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, Volume 10, Issue 3, pp 48–53 | Cite as

Algorithm Toolbox for Autonomous Mobile Robotic Systems

  • Thomas Emter
  • Christian Frese
  • Angelika Zube
  • Janko Petereit
Research Autonomous Vehicles
  • 25 Downloads

Heavy machinery is often used in environments that pose significant human health risks. The objective of current research activities at Fraunhofer IOSB is to equip construction machinery with autonomous capabilities, thereby enabling it to act independently in danger areas. To this end, an algorithm toolbox for autonomous robotic systems was developed. The toolbox includes components ranging from environment detection, through task and motion planning, all the way up to concrete task execution. To be able to evaluate the research findings, a technology demonstrator has been established, which is capable of removing contaminated layers of earth autonomously.

1 Autonomous Mobile Robots

Autonomous robotic systems can offer many different ways of support. They operate in dangerous or inaccessible environments and reduce human workloads by assuming monotonous tasks. Current applications range from assistance robots in industrial environments, through autonomous heavy machinery in...

Notes

Thanks

Parts of the described research have been supported within the project “AKIT — Autonomy KIT for conventional work vehicles to facilitate networked and assisted removal of hazards” (funding code 13N14099), which is being promoted in the course of the announcement “Innovative rescue and security systems” by the Federal Ministry of Education and Research (BMBF) within the scope of the Federal Government’s Research for Civil Security Framework Programme. The authors are grateful for the support.

References

  1. [1]
    Thrun, S.; Burgard, W.; Fox, D.: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press, 2005zbMATHGoogle Scholar
  2. [2]
    Petereit, J.; Emter, T.; Frey, C. W.: Mobile robot motion planning in multi-resolution lattices with hybrid dimensionality. In: IFAC Intelligent Autonomous Vehicles Symposium (2013), pp. 158–163Google Scholar
  3. [3]
    Seyboldt, R.; Frese, C.; Zube, A.: Sampling-based Path Planning to Cartesian Goal Positions for a Mobile Manipulator Exploiting Kinematic Redundancy. International Symposium on Robotics (ISR), 2016, pp. 1–9Google Scholar
  4. [4]
    Emter, T.; Petereit, J.: Integrated multi-sensor fusion for mapping and localization in outdoor environments for mobile robots. In: Proceedings SPIE 9121, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications (2014)Google Scholar
  5. [5]
    De Luca, A.; Oriolo, G.; Samson, C.: Feedback control of a nonholonomic car-like robot. In: Robot Motion Planning and Control (Lecture Notes in Control and Information Sciences 229). Berlin: Springer, 1998, pp. 171–253Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden 2017

Authors and Affiliations

  • Thomas Emter
    • 1
  • Christian Frese
    • 1
  • Angelika Zube
    • 1
  • Janko Petereit
    • 1
  1. 1.Department Systems of Measurement, Control and Diagnosis of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSBKarlsruheGermany

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