3 papers
cs.RO2026
Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners
Mitchell E. C. Sabbadini, Andrew H. Liu, Joseph Ruan +3
Robots operating in changing environments either predict obstacle changes and/or plan quickly enough to react to them. Predictive approaches require a strong prior about the positi…
cs.RO2025
AORRTC: Almost-Surely Asymptotically Optimal Planning with RRT-Connect
Tyler Wilson, Wil Thomason, Zachary Kingston +1
Finding high-quality solutions quickly is an important objective in motion planning. This is especially true for high-degree-of-freedom robots. Satisficing planners have traditiona…
cs.RO2025
Nearest-Neighbourless Asymptotically Optimal Motion Planning with Fully Connected Informed Trees (FCIT*)
Tyler S. Wilson, Wil Thomason, Zachary Kingston +2
Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. T…