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Vehicle Controls & Behaviors

Annual Plan

Novel Algorithms for Multi-Agent Autonomous Telerobotic Surveillance and Reconnaissance System

Project Team

Principal Investigator

Manish Bansal, Virginia Tech


Jonathon Smereka, Sam Kassoumeh, U.S. Army GVSC


Scott Corey, Spatial Integrated Systems (SIS)


Parshin Shojaee, Kartik Kulkarni, Virginia Tech

Project Summary

Project starts in 2020.

The advent of network-based telerobotic cameras, installed on aerial vehicles or mini unmanned ground vehicles enable multiple autonomous agents in the battlefield to interact with a remote physical environment using shared resources. It provides large streams of information to decision makers to conduct military operations such as intelligence, surveillance and reconnaissance, in harsh and hostile environment where it is tedious for humans to collect information. However, the telerobotic cameras system requires huge amount of data processing and storage units because of redundant (or overlapping) and unimportant information (videos or images) provided by the cameras.

The objective of this project is to develop novel algorithms for the multi-agent autonomous telerobotic surveillance and reconnaissance system by solving a set of stochastic combinatorial optimization problems. More specifically, our goals are as follows: (a) To efficiently manage this information using limited resources such that a subset of the information with maximum priority is captured; and (b) To utilize this information for identifying vulnerabilities in the logistics network, whose disruption will impose a substantial damage to the rescue missions, against enemy’s unforeseen attacks using knowledge of the enemy’s capabilities.


  • M. Bansal and P. Shojaee, “Planar Maximum Coverage Location Problem with Partial Coverage and Adjustable Quality of Service”, under review, INFORMS Journal on Computing, 2020.
  • K. Kulkarni and M. Bansal, “Discrete Multi-Capacitated Lot-Sizing Problems without and with Backlogging and Multiple Items”, under review, Operations Research Letters (Journal), 2020.

Publications from Prior Work closely related to the project:

  • Bansal, M., Kuo-Ling Huang, and Sanjay Mehrotra. “Decomposition algorithms for two-stage distributionally robust mixed binary programs.” SIAM Journal on Optimization 28.3, 2360-2383, 2018.
  • Bansal, M. and Kianfar, K., “Planar Maximum Coverage Location Problem with Partial Coverage and Rectangular Demand and Service Zones,” INFORMS Journal on Computing, vol. 29, no. 1, pp. 152–169, Jan. 2017.
  • Bansal, M., Kuo-Ling Huang, and Sanjay Mehrotra. “Tight second stage formulations in two-stage stochastic mixed integer programs.” SIAM Journal on Optimization 28.1, 788-819, 2018.
  • Bansal, M., S. Mehrotra, “On solving two-stage distributionally robust disjunctive programs with a general ambiguity set,” European Journal of Operational Research 279 (2), 296-307, 2019.
  • Bansal, M., K. Kianfar, Y. Ding, and E. Moreno-Centeno, “Hybridization of Bound-and-Decompose and Mixed Integer Feasibility Checking to Measure Redundancy in Structured Linear Systems,” IEEE Transactions on Automation Science and Engg. vol. 10, no. 4, pp. 1151–1157, Oct. 2013.
  • Venkatachalam, S., Bansal, M., and Smereka, JM. Stochastic Programming Models for Autonomous Ground Vehicles, Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium, 13-15, 2019.
  • Bansal, M., “Facets for single module and multi-module capacitated lot-sizing problems without backlogging,” Discrete Applied Mathematics, 255, 117-141, 2019.
  • Bansal, M., and Kianfar, K., “Facets for continuous multi-mixing set with general coefficients and bounded integer variables,” Discrete Optimization, vol. 26, pp. 1–25, Nov. 2017.
  • Kulkarni, K., Bansal, M., “Exact algorithms for lot-sizing problems with multiple capacities, piecewise concave production costs, and subcontracting,” under review, Operations Research, 2019.