Mac Schwager
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mac Schwager has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Daniela Rus
Massachusetts Institute of Technology
2 shared publications - Emilio Frazzoli
Massachusetts Institute of Technology
1 shared publication - Jingjin Yu
Massachusetts Institute of Technology
1 shared publication - Marco Pavone
Stanford University
1 shared publication - Stephen L. Smith
Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada
1 shared publication - Zijian Wang
Boston University
1 shared publication
Latest Publications
- Open Access
Correlated Orienteering Problem and it Application to Persistent Monitoring Tasks
We propose a novel non-linear extension to the Orienteering Problem (OP), called the Correlated Orienteering Problem (COP). We use COP to model the planning of informative tours for the persistent monitoring of a spatiotemporal field with time-invariant spatial correlations, in which the tours are constrained to have limited length. Our focus in this paper is QCOP a quadratic COP formulation that only looks at correlations between neighboring nodes in a node network. The main feature of QCOP is a quadratic utility function capturing the said spatial correlation. QCOP may be solved using mixed integer quadratic programming (MIQP), with the resulting anytime algorithm capable of planning multiple disjoint tours that maximize the quadratic utility. In particular, our algorithm can quickly plan a near-optimal tour over a network with up to $150$ nodes. Besides performing extensive simulation studies to verify the algorithm's correctness and characterize its performance, we also successfully applied it to two realistic persistent monitoring tasks: (i) estimation over a synthetic spatiotemporal field, and (ii) estimating the temperature distribution in the state of Massachusetts.Open AccessCorrelated Orienteering Problem and it Application to Persistent Monitoring Tasks
We propose a novel non-linear extension to the Orienteering Problem (OP), called the Correlated Orienteering Problem (COP). We use COP to model the planning of informative tours for the persistent monitoring of a spatiotemporal field with time-invariant spatial correlations, in which the tours are constrained to have limited length. Our focus in this paper is QCOP a quadratic COP formulation that only looks at correlations between neighboring nodes in a node network. The main feature of QCOP is a quadratic utility function capturing the said spatial correlation. QCOP may be solved using mixed integer quadratic programming (MIQP), with the resulting anytime algorithm capable of planning multiple disjoint tours that maximize the quadratic utility. In particular, our algorithm can quickly plan a near-optimal tour over a network with up to $150$ nodes. Besides performing extensive simulation studies to verify the algorithm's correctness and characterize its performance, we also successfully applied it to two realistic persistent monitoring tasks: (i) estimation over a synthetic spatiotemporal field, and (ii) estimating the temperature distribution in the state of Massachusetts. - Open Access
Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems
In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. The system allows for one-way trips, so that customers do not have to return to their origin. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to "rebalance the rebalancers" by letting them travel back to the popular destinations with a customer. Accordingly, in this paper we study how to optimally route the rebalancing vehicles and drivers so that stability (in terms of boundedness of the number of waiting customers) is ensured while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. Leveraging our analysis, we determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Interestingly, our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.
Open AccessRebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems
In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. The system allows for one-way trips, so that customers do not have to return to their origin. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to "rebalance the rebalancers" by letting them travel back to the popular destinations with a customer. Accordingly, in this paper we study how to optimally route the rebalancing vehicles and drivers so that stability (in terms of boundedness of the number of waiting customers) is ensured while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. Leveraging our analysis, we determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Interestingly, our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.