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Multi-objective or multi-destination path planning is crucial for mobile robotics applications such as mobility as a service, robotics inspection, and electric vehicle charging for long trips. This work proposes an anytime iterative system to concurrently solve the multi-objective path planning problem and determine the visiting order of destinations. The system is comprised of an anytime informable multi-objective and multi-directional RRT * algorithm to form a simple connected graph, and a solver that consists of an enhanced cheapest insertion algorithm and a genetic algorithm to solve approximately the relaxed traveling salesman problem in polynomial time. Moreover, a list of waypoints is often provided for robotics inspection and vehicle routing so that the robot can preferentially visit certain equipment or areas of interest. We show that the proposed system can inherently incorporate such knowledge to navigate challenging topology. The proposed anytime system is evaluated on large and complex graphs built for real-world driving applications. C++ implementations are available at: https://github.com/UMich-BipedLab/IMOMD-RRTStar.
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Effect of Routing Constraints\! on Learning Efficiency of Destination Recommender Systems in Mobility-on-Demand Services
With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a ``physical internet search engine''. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators.
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- Award ID(s):
- 1652735
- PAR ID:
- 10213710
- Date Published:
- Journal Name:
- IEEE Transactions on Intelligent Transportation Systems
- ISSN:
- 1524-9050
- Page Range / eLocation ID:
- 1 to 16
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TITS.2020.3038675
