A Tuned Approach to Feedback Motion Planning with RRTs under Model Uncertainty. Maeda, G. J., Singh, S. P. N., & Durrant-Whyte, H. 2011. doi abstract bibtex Model uncertainty complicates most kinodynamic motion planning and control approaches due to their reliance on accurate forward prediction. If the model uncertainty is significant, a generated path or control strategy based on forward simulation of this model is potentially invalid and expensive to track (if possible). This paper explores the use of system identification/estimation to tune model parameters. Framed as an extension to rapidly exploring random tree (RRT) methods, it updates the model so that reachable actions added to the tree have more fidelity. This can be viewed as a mixture of a model predictive control (MPC) for local planning with an approximate-model global planner providing sub-goals and thus overcoming the limited lookahead caused by model uncertainty. The benefits of this approach are illustrated for a 3 DOF serial manipulator controlled by computed torque control operating under large external disturbances. In this case, the approach provides operation under intermittent feedback and disturbance observation. Tracking and actuator utilization are also improved over solutions found via conventional methods.
@CONFERENCE{icra11.gjm,
author = {Guilherme J. Maeda and Surya P. N. Singh and Hugh Durrant-Whyte},
title = {A Tuned Approach to Feedback Motion Planning with RRTs under Model
Uncertainty},
booktitle = {International Conference on Robotics and Automation},
year = {2011},
pages = {2288-2294},
abstract = {Model uncertainty complicates most kinodynamic motion planning and
control approaches due to their reliance on accurate forward prediction.
If the model uncertainty is significant, a generated path or control
strategy based on forward simulation of this model is potentially
invalid and expensive to track (if possible). This paper explores
the use of system identification/estimation to tune model parameters.
Framed as an extension to rapidly exploring random tree (RRT) methods,
it updates the model so that reachable actions added to the tree
have more fidelity. This can be viewed as a mixture of a model predictive
control (MPC) for local planning with an approximate-model global
planner providing sub-goals and thus overcoming the limited lookahead
caused by model uncertainty. The benefits of this approach are illustrated
for a 3 DOF serial manipulator controlled by computed torque control
operating under large external disturbances. In this case, the approach
provides operation under intermittent feedback and disturbance observation.
Tracking and actuator utilization are also improved over solutions
found via conventional methods.},
doi = {10.1109/ICRA.2011.5979834},
pdf = {ICRA2011.0649.pdf}
}
Downloads: 0
{"_id":{"_str":"51fd4b4ac5b22c38760016d9"},"__v":34,"authorIDs":["25JLGLMgHDQdFcbEd","2pNN7j4TWs2sdPq2b","34dFKNX3cFW2RkuZi","35Xv7osbm3zXk62F8","3SKHrAB8wMRysGFy8","47GNnFb6EPKma3Srt","48H54WhqNYjnD95ui","4SntEAiXntKARkNgS","5462d4748a9aab071c0005c7","5T75juAmsAB4z6bD6","5WvQsNxqBRyW2RSQi","5e34214741f782de01000022","5e3426cb41f782de01000084","5e3429b441f782de010000b9","5e34312341f782de0100013e","5e34390b41f782de010001e6","5e34392441f782de010001eb","5e6b0e1e86bf9cde0100000e","5sfq2WJ4W5g8mskFe","6a8JHmpJfRqSEQkqb","6hGsP8HLkwinYXigD","6zbapqaBfYNicQRXv","7CkZJwcgcj75uyjun","7DA6BoSFi7tnffK8H","7WRSvEmwiLfubqe8f","8HnjdunXqJG4QDLAd","8ntKbqqhqJyvrfK6r","9zqTiopbnCKBBJEHw","B7m7NjLbfonZHkc6g","BifZh9Ad6N9fnehRi","CL6tPXijZ3hpfr78G","CmEkRJvYX5Tr4Pnfk","DExNFWbRJaceGfGum","DY3kZm8P2JayQgduy","EyvXqoGdM2qS93CN7","Fdd27HCHQMmQXfGLs","GqK42ji4y7PtPXZ3F","Gy3fvTPGQkczs42ez","HLcHzprYn3r3Xe84b","HdmdEWKvJektnYR7y","Hj7KYhXTskqMzhg7u","HzDPAyHXF2wTA7zGu","JHBPkDMYpmfwpcT8A","JMwRZPisoDhWDzWhm","K37JaQLteDSHdqyjx","K7AXubjaKLhA3pNTA","KqPDgQxE6zxpxX67e","LRjJSNBWHuWanhqNM","LjXJLGxM3QELAoaNP","Mc9S7iNhTFJWsRkeb","Mww7bu4yZkujgW5ns","NAGpXYkKmHQ29Mvjb","NKMyNZac6Y89jH6Qr","PZGS54s4e74ZKchZy","PcR3YBj9m6B3nvjG4","Q7eQ2ycHNYkfAF6nP","QR4SjtXbj8siaJg4K","QReRjpmwvPkeefcfK","QkREvvTHfouEvisjF","Rg9JW68PMYmedw2jv","RxHhvDjoid9t5F7uJ","Sju6B3BiCKw3CtwpM","TYZYfD6XShucnrAkf","XviLSraASaqofFM4Z","XwzuwxPG3rMWe5q7S","XzKRCGm3o924pvPcu","YHiJHk6hwmzPyR4nL","ZezE89A6WSniSjNvd","ZtbB3GZNz96JBnnL6","aFQJQukES2Lw58sS5","aPafrdEuKAitYSfiA","afAXjw75m99Km4Fgj","afwmjw9TTkZfveNzE","bNN2E3KLq5WFdzKTF","cfwHoA5cnQ6eGD4bg","dDWF4crzWnW8RyG9p","dFqdXn6WnDTzTnEqQ","dLkDhYcDM2PSSEZk3","dmJYK8FNXWAAu5G7T","dvJ37AfDNYzAuD9PK","eiYowsdjQ9Mcw2Thz","evxzRHu7G7fbeoibY","fid77jGJy9yzcHJr2","fuEkxmax5jFbtxCYy","gAmxy4SvWarbPBeTX","gFrSDrvd4JrFP9kTP","gXJZBJPrZddgZ7YoH","ggsiWBRuBugZgRE8f","gqQQ75hZq9ros28RR","h3u9YL2Ae2DNrQCqi","hiXcvvFucmwxJEZ9y","hth6XiDc9jGYzRe5H","i2DexC5F5rxsC5PHg","iELGk3WWqbGkdYzPc","ijX6hDPyFBq7xgoma","ip7PgT6GfbdZreojv","is7DZGXrEJDXNvotf","j6o4kAC79ZjvEeijw","jJbSbk2LoomLaPG7h","kTAMCAkfLFo4f89Gp","kXmyRRzRyEi5cz83e","kz4FwssxjbAeByCEF","nRtwjECYR2WAxoeFJ","oDxo3ZMAB4phpqr5o","p64zTgE7XqWDjvB7v","pHCFNdiRdm4dHD4JE","pJd6uJtiZnuhNGMpH","paBtjfzxNGmPkJgFy","pu6XsJacuRdqyidu3","qLFpKhtC29Rk9jeS8","rBLmXAceeFPWAntch","sHdkaG8cdJP4GriPy","tsgNGCAcyhTm8BeQB","u3sfcQ8RovYYpKjLb","uGrPMtPgNJq2bBX47","uHkfdT8WqEWJyDjRg","ucoBD8M4z8ezYE8bc","upFqY33vPBba6wxh8","v9z9AicS8PCAymgiR","vAQtv8TCRg4oNqGSn","vDknP8BNi6WY35SDp","vQekzHzAimvgtFT3G","vg9Go2zuYX2Xq8HJf","vve7zKuHuKZmDZ6FB","w4erytKzHZZj3bnbZ","wmuqnoGDizWJe8P2H","xGz64z5uDaXxM3ZHF","xSFFddhsMR36EEcYu","y52p2nvza87XinHws","yBp5Pq56pbsjD6f8H","z6nH83SNs79e3uH2u","zgA7uYAasRk8ezQ9e","zj2qr55FtWLxKhX9E","zsFBk8a7A7dEouo2u"],"author_short":["Maeda, G. J.","Singh, S. P. N.","Durrant-Whyte, H."],"bibbaseid":"maeda-singh-durrantwhyte-atunedapproachtofeedbackmotionplanningwithrrtsundermodeluncertainty-2011","bibdata":{"bibtype":"conference","type":"conference","author":[{"firstnames":["Guilherme","J."],"propositions":[],"lastnames":["Maeda"],"suffixes":[]},{"firstnames":["Surya","P.","N."],"propositions":[],"lastnames":["Singh"],"suffixes":[]},{"firstnames":["Hugh"],"propositions":[],"lastnames":["Durrant-Whyte"],"suffixes":[]}],"title":"A Tuned Approach to Feedback Motion Planning with RRTs under Model Uncertainty","booktitle":"International Conference on Robotics and Automation","year":"2011","pages":"2288-2294","abstract":"Model uncertainty complicates most kinodynamic motion planning and control approaches due to their reliance on accurate forward prediction. If the model uncertainty is significant, a generated path or control strategy based on forward simulation of this model is potentially invalid and expensive to track (if possible). This paper explores the use of system identification/estimation to tune model parameters. Framed as an extension to rapidly exploring random tree (RRT) methods, it updates the model so that reachable actions added to the tree have more fidelity. This can be viewed as a mixture of a model predictive control (MPC) for local planning with an approximate-model global planner providing sub-goals and thus overcoming the limited lookahead caused by model uncertainty. The benefits of this approach are illustrated for a 3 DOF serial manipulator controlled by computed torque control operating under large external disturbances. In this case, the approach provides operation under intermittent feedback and disturbance observation. Tracking and actuator utilization are also improved over solutions found via conventional methods.","doi":"10.1109/ICRA.2011.5979834","pdf":"ICRA2011.0649.pdf","bibtex":"@CONFERENCE{icra11.gjm,\r\n author = {Guilherme J. Maeda and Surya P. N. Singh and Hugh Durrant-Whyte},\r\n title = {A Tuned Approach to Feedback Motion Planning with RRTs under Model\r\n\tUncertainty},\r\n booktitle = {International Conference on Robotics and Automation},\r\n year = {2011},\r\n pages = {2288-2294},\r\n abstract = {Model uncertainty complicates most kinodynamic motion planning and\r\n\tcontrol approaches due to their reliance on accurate forward prediction.\r\n\tIf the model uncertainty is significant, a generated path or control\r\n\tstrategy based on forward simulation of this model is potentially\r\n\tinvalid and expensive to track (if possible). This paper explores\r\n\tthe use of system identification/estimation to tune model parameters.\r\n\tFramed as an extension to rapidly exploring random tree (RRT) methods,\r\n\tit updates the model so that reachable actions added to the tree\r\n\thave more fidelity. This can be viewed as a mixture of a model predictive\r\n\tcontrol (MPC) for local planning with an approximate-model global\r\n\tplanner providing sub-goals and thus overcoming the limited lookahead\r\n\tcaused by model uncertainty. The benefits of this approach are illustrated\r\n\tfor a 3 DOF serial manipulator controlled by computed torque control\r\n\toperating under large external disturbances. In this case, the approach\r\n\tprovides operation under intermittent feedback and disturbance observation.\r\n\tTracking and actuator utilization are also improved over solutions\r\n\tfound via conventional methods.},\r\n doi = {10.1109/ICRA.2011.5979834},\r\n pdf = {ICRA2011.0649.pdf}\r\n}\r\n\r\n","author_short":["Maeda, G. J.","Singh, S. P. N.","Durrant-Whyte, H."],"key":"icra11.gjm","id":"icra11.gjm","bibbaseid":"maeda-singh-durrantwhyte-atunedapproachtofeedbackmotionplanningwithrrtsundermodeluncertainty-2011","role":"author","urls":{},"downloads":0,"html":""},"bibtype":"conference","biburl":"http://robotics.itee.uq.edu.au/~spns/pubcache/SpnS_PubList.bib","downloads":0,"keywords":[],"search_terms":["tuned","approach","feedback","motion","planning","rrts","under","model","uncertainty","maeda","singh","durrant-whyte"],"title":"A Tuned Approach to Feedback Motion Planning with RRTs under Model Uncertainty","title_words":["tuned","approach","feedback","motion","planning","rrts","under","model","uncertainty"],"year":2011,"dataSources":["zNCf6MTxXnpkNN9Zz"]}