Paper Info
Title
Monte-Carlo Search For Prize-Collecting Robot Motion Planning With Time Windows, Capacities, Pickups, And Deliveries
Abstract
Logistics operations often require a robot to pickup and deliver objects from multiple locations within certain time frames. This is a challenging task-and-motion planning problem as it intertwines logical and temporal constraints about the operations with geometric and differential constraints related to obstacle avoidance and robot dynamics. To address these challenges, this paper couples vehicle routing over a discrete abstraction with sampling-based motion planning. On the one hand, vehicle routing provides plans to effectively guide sampling-based motion planning as it explores the vast space of feasible motions. On the other hand, motion planning provides feasibility estimates which vehicle routing uses to refine its plans. This coupling makes it possible to extend the state-of-the-art in multi-goal motion planning by also incorporating capacities, pickups, and deliveries in addition to time windows. When not all pickups and deliveries can be completed in time, the approach seeks to minimize the violations and maximize the profit.
Year
DOI
Venue
2019
10.1007/978-3-030-30179-8_13
ADVANCES IN ARTIFICIAL INTELLIGENCE, KI 2019
DocType
Volume
ISSN
Conference
11793
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Stefan Edelkamp100.68
Erion Plaku200.34
Yassin Warsame300.34