TY - GEN
T1 - A travel-time optimizing edge weighting scheme for dynamic re-planning
AU - Feit, Andrew
AU - Toval, Lenrik
AU - Hovagimian, Raffi
AU - Greenstadt, Rachel
PY - 2010
Y1 - 2010
N2 - The success of autonomous vehicles has made path planning in real, physically grounded environments an increasingly important problem. In environments where speed matters and vehicles must maneuver around obstructions, such as autonomous car navigation in hostile environments, the speed with which real vehicles can traverse a path is often dependent on the sharpness of the corners on the path as well as the length of path edges. We present an algorithm that incorporates the use of the turn angle through path nodes as a limiting factor for vehicle speed. Vehicle speed is then used in a time-weighting calculation for each edge. This allows the path planning algorithm to choose potentially longer paths, with less turns in order to minimize path traversal time. Results simulated in the Breve environment show that travel time can be reduced over the solution obtained using the Anytime D* Algorithm by approximately 10% for a vehicle that is speed limited based on turn rate.
AB - The success of autonomous vehicles has made path planning in real, physically grounded environments an increasingly important problem. In environments where speed matters and vehicles must maneuver around obstructions, such as autonomous car navigation in hostile environments, the speed with which real vehicles can traverse a path is often dependent on the sharpness of the corners on the path as well as the length of path edges. We present an algorithm that incorporates the use of the turn angle through path nodes as a limiting factor for vehicle speed. Vehicle speed is then used in a time-weighting calculation for each edge. This allows the path planning algorithm to choose potentially longer paths, with less turns in order to minimize path traversal time. Results simulated in the Breve environment show that travel time can be reduced over the solution obtained using the Anytime D* Algorithm by approximately 10% for a vehicle that is speed limited based on turn rate.
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M3 - Conference contribution
AN - SCOPUS:79959717816
SN - 9781577354673
T3 - AAAI Workshop - Technical Report
SP - 26
EP - 32
BT - Bridging the Gap Between Task and Motion Planning - Papers from the 2010 AAAI Workshop, Technical Report
T2 - 2010 AAAI Workshop
Y2 - 11 July 2010 through 11 July 2010
ER -