On-demand planning of a school of autonomous mobile robots for prioritized task completion




Bakshi, Soovadeep

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Using autonomous mobile robots (AMRs) to collaboratively complete tasks has been intensively pursued by both industry and academia. Despite advancements in the field of AMR operation, there are still many challenges associated with this domain. For instance, to optimally operate numerous AMRs to collaboratively complete a large number of tasks in real-time is highly challenging. First, prioritized tasks need to be continuously and optimally assigned to the entire school of AMRs. When the number of AMRs is larger than 20 and the number of tasks is greater than 500, the computational cost of optimally assigning tasks to each AMR and scheduling these AMRs to complete the assigned tasks is significant, which renders most operations of such an AMR system infeasible in real time.

Secondly, for a typical field equipped with AMRs, the tasks involve going from one location to another, making the search space asymmetric. The AMR needs to travel from its starting location to its ending location to complete a task. Thus, this AMR problem is operating in the ‘task space’ instead of the actual ‘point space’, i.e., the tasks are assigned to individual AMRs without repetitions instead of the points/locations in the field. This aspect will dramatically increase the complexity of the optimization process and traditional ‘point space’ methods might not apply.

Thirdly, the tasks might have different priority levels. The fact that completing tasks with higher priorities earlier is preferred during clustering and scheduling adds another dimension to the optimization process, which can be defined as an asymmetric ‘priority space’. The asymmetricity of the ‘priority space’ also affects the clustering and scheduling of the AMRs. There has been extensive research on scheduling of AMRs to complete tasks by traveling to specific locations. However, most algorithms consider that the tasks do not have priorities. The ‘priority space’ necessitates exploring new methods for optimal scheduling in an asymmetric space.

Finally, the clustering and scheduling of the school of AMRs should be adapted to operational and environmental changes. For instance, a battery-powered AMR will need to efficiently utilize its energy, and therefore, energy conscientious trajectory generation as well as optimal recharge scheduling of AMRs under such circumstances is also of great interest. The overall planning process should be completed in a timely manner such that the operation of the school of AMRs can be implemented in real time, i.e., on-demand.

In order to address the above challenges, this research will develop algorithms for real-time control of a school of AMRs that can optimally cluster the AMRs with tasks having various levels of priorities, schedule each AMR to complete the tasks with the highest efficiency, generate energy-efficient trajectories for each AMR, and perform energy-based recharge scheduling.


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