{'fraction_non_alphanumeric': 0.02360248447204969, 'fraction_numerical': 0.0, 'mean_word_length': 5.522267206477733, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.