Swarm search and service (SSS) missions require large swarms to simultaneously search an area while servicing jobs as they are encountered. Jobs must be immediately serviced and can be one of several different job types-each requiring a different service time and number of vehicles to complete its service successfully. After jobs are serviced, vehicles are returned to the swarm and become available for reallocation. As part of SSS mission planning, human operators must determine the number of vehicles needed to achieve this balance. The complexities associated with balancing vehicle allocation to multiple as yet unknown tasks with returning vehicles makes this extremely difficult for humans. Previous work assumes that all system jobs are known ahead of time or that vehicles move independently of each other in a multi-agent framework. We present a dynamic vehicle routing (DVR) framework whose policies optimally allocate vehicles as jobs arrive. By incorporating time constraints into the DVR framework, an M/M/k/k queuing model can be used to evaluate overall steady state system performance for a given swarm size. Using these estimates, operators can rapidly compare system performance across different configurations, leading to more effective choices for swarm size. A sensitivity analysis is performed and its results are compared with the model, illustrating the appropriateness of our method to problems of plausible scale and complexity.
As the number of viable applications for unmanned aerial vehicle (UAV) systems increases at an exponential rate, interfaces that reduce the reliance on highly skilled engineers and pilots must be developed. Recent work aims to make use of common human communication modalities such as speech and gesture. This paper explores a multimodal natural language interface that uses a combination of speech and gesture input modalities to build complex UAV flight paths by defining trajectory segment primitives. Gesture inputs are used to define the general shape of a segment while speech inputs provide additional geometric information needed to fully characterize a trajectory segment. A user study is conducted in order to evaluate the efficacy of the multimodal interface.
As part of swarm search and service (SSS) missions, robots are tasked with servicing jobs as they are sensed. This requires small sub-swarm teams to leave the swarm for a specified amount of time to service the jobs. In doing so, fewer robots are required to change motion than if the whole swarm were diverted, thereby minimizing the job's overall effect on the swarm's main goal. We explore the problem of removing the required number of robots from the swarm, while maintaining overall swarm connectivity. By preserving connectivity, robots are able to successfully rejoin the swarm upon completion of their assigned job. These robots are then made available for reallocation. We propose a decentralized and asynchronous method for breaking off sub-swarm groups and rejoining them with the main swarm using the swarm's communication graph topology. Both single and multiple job site cases are explored. The results are compared against a full swarm movement method. Simulation results show that the proposed method outperforms a full swarm method in the average number of messages sent per robot in each step, as well as, the distance traveled by the swarm.
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