Revisiting Replanning from Scratch: Real-Time Incremental Planning with Fast Almost-Surely Asymptotically Optimal Planners
Mitchell Edris Crisante Sabbadini, Andrew H. Liu, Joseph Ruan, Tyler S. Wilson, Zachary Kingston, Jonathan Gammell
AI summary
Problem
Traditional reactive incremental planners rely on computationally expensive plan reuse and explicit obstacle change detection, which hinders real-time performance in dynamic environments.
Approach
The authors propose bypassing plan reuse entirely by solving independent optimal planning problems from scratch using fast almost-surely asymptotically optimal (ASAO) planners like EIT* and AORRTC.
Key results
- EIT* achieves higher success rates and shorter median global paths than RRTX, RRT*, and RRT-Connect
- Real-time replanning capability down to 50 ms without plan reuse overhead
- Successful real-world validation on a Franka Research 3 arm for dynamic obstacle avoidance
- Consistent global path optimality through independent ASAO planning cycles
Why it matters
Offers a simpler, more efficient paradigm for real-time robotic navigation that eliminates the computational bottlenecks of traditional incremental planners.
Abstract
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 position and motion of obstacles. Reactive approaches require no assumptions about their environment but must replan quickly and find high-quality paths to navigate effectively. Reactive approaches often reuse information between queries to reduce planning cost. These techniques are conceptually sound but updating dense planning graphs when information changes can be computationally prohibitive. It can also require significant effort to detect the changes in some applications. This paper revisits the long-held assumption that reactive replanning requires updating existing plans. It shows that the incremental planning problem can alternatively be solved more efficiently as a series of independent problems using fast almost-surely asymptotically optimal (ASAO) planning algorithms. These ASAO algorithms quickly find an initial solution and converge towards an optimal solution which allows them to find consistent global plans in the presence of changing obstacles without requiring explicit plan reuse. This is demonstrated with simulated experiments where Effort Informed Trees (EIT*) finds shorter median solution paths than the tested reactive planning algorithms and is further validated using Asymptotically Optimal RRT-Connect (AORRTC) on a real-world planning problem on a robot arm.