@incollection{Ferrein:etAl_INTECH2023_Controlling-a-Fleet,
  author       = {Alexander Ferrein and Gjorgji Nikolovski and Nicolas Limpert
                  and Michael Reke and Stefan Schiffer and Ingrid Scholl},
  title        = {{Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation}},
  booktitle    = {Multi-Robot Systems - New Advances},
  publisher    = {IntechOpen},
  address      = {Rijeka},
  year         = {2023},
  editor       = {Serdar K{\"u}{\c{c}}{\"u}k},
  chapter      = {4},
  doi          = {10.5772/intechopen.113044},
  url          = {https://doi.org/10.5772/intechopen.113044},
  url_intech   = {https://www.intechopen.com/chapters/88580},
  abstract     = {In this chapter, we report on our activities to
                  create and maintain a fleet of autonomous load haul
                  dump (LHD) vehicles for mining operations. The ever
                  increasing demand for sustainable solutions and
                  economic pressure causes innovation in the mining
                  industry just like in any other branch. In this
                  chapter, we present our approach to create a fleet
                  of autonomous special purpose vehicles and to
                  control these vehicles in mining operations. After
                  an initial exploration of the site we deploy the
                  fleet. Every vehicle is running an instance of our
                  ROS 2-based architecture. The fleet is then
                  controlled with a dedicated planning module. We also
                  use continuous environment monitoring to implement a
                  life-long mapping approach. In our experiments, we
                  show that a combination of synthetic, augmented and
                  real training data improves our classifier based on
                  the deep learning network Yolo v5 to detect our
                  vehicles, persons and navigation beacons. The
                  classifier was successfully installed on the NVidia
                  AGX-Drive platform, so that the abovementioned
                  objects can be recognised during the dumper
                  drive. The 3D poses of the detected beacons are
                  assigned to lanelets and transferred to an existing
                  map.},
}

{"_id":"qS2iNzqbGah5qt6H8","bibbaseid":"ferrein-nikolovski-limpert-reke-schiffer-scholl-controllingafleetofautonomouslhdvehiclesinminingoperation-2023","author_short":["Ferrein, A.","Nikolovski, G.","Limpert, N.","Reke, M.","Schiffer, S.","Scholl, I."],"bibdata":{"bibtype":"incollection","type":"incollection","author":[{"firstnames":["Alexander"],"propositions":[],"lastnames":["Ferrein"],"suffixes":[]},{"firstnames":["Gjorgji"],"propositions":[],"lastnames":["Nikolovski"],"suffixes":[]},{"firstnames":["Nicolas"],"propositions":[],"lastnames":["Limpert"],"suffixes":[]},{"firstnames":["Michael"],"propositions":[],"lastnames":["Reke"],"suffixes":[]},{"firstnames":["Stefan"],"propositions":[],"lastnames":["Schiffer"],"suffixes":[]},{"firstnames":["Ingrid"],"propositions":[],"lastnames":["Scholl"],"suffixes":[]}],"title":"Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation","booktitle":"Multi-Robot Systems - New Advances","publisher":"IntechOpen","address":"Rijeka","year":"2023","editor":[{"firstnames":["Serdar"],"propositions":[],"lastnames":["Küçük"],"suffixes":[]}],"chapter":"4","doi":"10.5772/intechopen.113044","url":"https://doi.org/10.5772/intechopen.113044","url_intech":"https://www.intechopen.com/chapters/88580","abstract":"In this chapter, we report on our activities to create and maintain a fleet of autonomous load haul dump (LHD) vehicles for mining operations. The ever increasing demand for sustainable solutions and economic pressure causes innovation in the mining industry just like in any other branch. In this chapter, we present our approach to create a fleet of autonomous special purpose vehicles and to control these vehicles in mining operations. After an initial exploration of the site we deploy the fleet. Every vehicle is running an instance of our ROS 2-based architecture. The fleet is then controlled with a dedicated planning module. We also use continuous environment monitoring to implement a life-long mapping approach. In our experiments, we show that a combination of synthetic, augmented and real training data improves our classifier based on the deep learning network Yolo v5 to detect our vehicles, persons and navigation beacons. The classifier was successfully installed on the NVidia AGX-Drive platform, so that the abovementioned objects can be recognised during the dumper drive. The 3D poses of the detected beacons are assigned to lanelets and transferred to an existing map.","bibtex":"@incollection{Ferrein:etAl_INTECH2023_Controlling-a-Fleet,\n author = {Alexander Ferrein and Gjorgji Nikolovski and Nicolas Limpert\n and Michael Reke and Stefan Schiffer and Ingrid Scholl},\n title = {{Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation}},\n booktitle = {Multi-Robot Systems - New Advances},\n publisher = {IntechOpen},\n address = {Rijeka},\n year = {2023},\n editor = {Serdar K{\\\"u}{\\c{c}}{\\\"u}k},\n chapter = {4},\n doi = {10.5772/intechopen.113044},\n url = {https://doi.org/10.5772/intechopen.113044},\n url_intech = {https://www.intechopen.com/chapters/88580},\n abstract = {In this chapter, we report on our activities to\n create and maintain a fleet of autonomous load haul\n dump (LHD) vehicles for mining operations. The ever\n increasing demand for sustainable solutions and\n economic pressure causes innovation in the mining\n industry just like in any other branch. In this\n chapter, we present our approach to create a fleet\n of autonomous special purpose vehicles and to\n control these vehicles in mining operations. After\n an initial exploration of the site we deploy the\n fleet. Every vehicle is running an instance of our\n ROS 2-based architecture. The fleet is then\n controlled with a dedicated planning module. We also\n use continuous environment monitoring to implement a\n life-long mapping approach. In our experiments, we\n show that a combination of synthetic, augmented and\n real training data improves our classifier based on\n the deep learning network Yolo v5 to detect our\n vehicles, persons and navigation beacons. The\n classifier was successfully installed on the NVidia\n AGX-Drive platform, so that the abovementioned\n objects can be recognised during the dumper\n drive. The 3D poses of the detected beacons are\n assigned to lanelets and transferred to an existing\n map.},\n}\n","author_short":["Ferrein, A.","Nikolovski, G.","Limpert, N.","Reke, M.","Schiffer, S.","Scholl, I."],"editor_short":["Küçük, S."],"key":"Ferrein:etAl_INTECH2023_Controlling-a-Fleet","id":"Ferrein:etAl_INTECH2023_Controlling-a-Fleet","bibbaseid":"ferrein-nikolovski-limpert-reke-schiffer-scholl-controllingafleetofautonomouslhdvehiclesinminingoperation-2023","role":"author","urls":{"Paper":"https://doi.org/10.5772/intechopen.113044"," intech":"https://www.intechopen.com/chapters/88580"},"metadata":{"authorlinks":{}}},"bibtype":"incollection","biburl":"http://maskor.fh-aachen.de/biblio/MASKOR.bib","dataSources":["h8Pz4jiwoLbb3hmG2","i8ftsMK5wMbiNqmtB","pBDNm3knLemYTNMHw"],"keywords":[],"search_terms":["controlling","fleet","autonomous","lhd","vehicles","mining","operation","ferrein","nikolovski","limpert","reke","schiffer","scholl"],"title":"Controlling a Fleet of Autonomous LHD Vehicles in Mining Operation","year":2023}