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Shaurya Shriyam



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Shaurya Shriyam
, and Satyandra K. Gupta (2019). "Incorporation of Contingency Tasks in Task Allocation for Multirobot Teams." In IEEE Transactions on Automation Science and Engineering (TASE).

Complex logistics support missions require the execution of spatially separated information gathering and situational awareness tasks. Mobile robot teams can play an important role in the automated execution of these tasks to reduce mission completion time. Planning strategies for such missions must take into account the formation of effective coalitions among available robots and assignment of tasks to robots with the goal of minimizing the expected mission completion time. The occurrence of unexpected situations that adversely interfere with the execution of the mission may require the execution of contingency tasks so that the originally planned tasks may proceed with minimal disruption. Initially reported potential contingency tasks may not always affect mission tasks due to the uncertainty in the mission environment. When potential contingency tasks are reported, the planner updates its existing plan to minimize the expected mission completion time based on the probability of these contingency tasks impacting the mission, their impact on the mission, and other task characteristics. We describe various heuristic-based strategies to compute task allocations for robots for mission execution. We perform simulation experiments to compare them and analyze the computational performance of the best performing strategy. We show that the proactive approach to contingency task management outperforms both the conservative and reactive approaches.

Shaurya Shriyam
, Brual C. Shah, and Satyandra K. Gupta (2018). "Decomposition of collaborative surveillance tasks for execution in marine environments by a team of unmanned surface vehicles." In Journal of Mechanisms and Robotics Vol. 10, no. 2.

This paper introduces an approach for decomposing exploration tasks among multiple unmanned surface vehicles (USVs) in congested regions. In order to ensure effective distribution of the workload, the algorithm has to consider the effects of the environmental constraints on the USVs. The performance of a USV is influenced by the surface currents, risk of collision with the civilian traffic, and varying depths due to tides and weather. The team of USVs needs to explore a certain region of the harbor and we need to develop an algorithm to decompose the region of interest into multiple subregions. The algorithm overlays a two-dimensional grid upon a given map to convert it to an occupancy grid, and then proceeds to partition the region of interest among the multiple USVs assigned to explore the region. During partitioning, the rate at which each USV is able to travel varies with the applicable speed limits at the location. The objective is to minimize the time taken for the last USV to finish exploring the assigned area. We use the particle swarm optimization (PSO) method to compute the optimal region partitions. The method is verified by running simulations in different test environments. We also analyze the performance of the developed method in environments where speed restrictions are not known in advance.


Shaurya Shriyam
, and Satyandra K. Gupta (2018). "Incorporating potential contingency tasks in multi-robot mission planning." In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 1-7.

In most complex missions, unexpected situations arise that may interfere with the planned execution of mission tasks. These situations result in the generation of contingency tasks that need to be executed before the originally planned tasks are completed. Potential contingency tasks may not always affect mission tasks due to the inherent uncertainty in the environment. Deferring action on a potential contingency task may incur a penalty in terms of wasted time due to idle robots if the contingency task becomes a bottleneck in the future. On the other hand, immediate action on a potential contingency task may incur a penalty in terms of wasted time if the contingency task did not actually impact the mission. When a contingency task is reported, the planner generates an updated plan that minimizes expected mission completion time by taking into account the probability of the contingency task impacting mission tasks, its effect on the mission, and its spatial location. We have characterized the performance of the algorithm through simulation experiments. We show that the proactive approach to contingency task management outperforms a conservative approach.