For future systems that require one or a small team of operators to supervise a network of automated agents, automated planners are critical since they are faster than humans for path planning and resource allocation in multivariate, dynamic, time-pressured environments. However, such planners can be brittle and unable to respond to emergent events.Human operators can aid such systems by bringing their knowledge-based reasoning and experience to bear. Given a decentralized task planner and a goal-based operator interface for a network of unmanned vehicles in a search, track, and neutralize mission, we demonstrate with a human-on-the-loop experiment that humans guiding these decentralized planners improved system performance by up to 50%. However, those tasks that required precise and rapid calculations were not significantly improved with human aid. Thus, there is a shared space in such complex missions for human-automation collaboration.
Abstract-This paper presents a decentralized algorithm that can create feasible assignments for a network of autonomous agents in the presence of coupled constraints. The coupled constraints are introduced to address complex mission characteristics that include assignment relationships, where the value of a task is conditioned on whether or not another task has been assigned, and temporal relationships, where the value of a task is conditioned on when it is performed relative to other tasks. The new algorithm is developed as an extension to the consensus-based bundle algorithm (CBBA), introducing the notion of pessimistic or optimistic bidding strategies and the relative timing constraints between tasks. This extension, called Coupled-constraint CBBA (CCBBA), is compared to the baseline in a complex mission simulation and is found to outperform the baseline, particularly for task-rich scenarios.
Advances in autonomy have made it possible to invert the typical operator-to-unmanned-vehicle ratio so that a single operator can now control multiple heterogeneous unmanned vehicles. Algorithms used in unmanned-vehicle path planning and task allocation typically have an objective function that only takes into account variables initially identified by designers with set weightings. This can make the algorithm seemingly opaque to an operator and brittle under changing mission priorities. To address these issues, it is proposed that allowing operators to dynamically modify objective function weightings of an automated planner during a mission can have performance benefits. A multiple-unmanned-vehicle simulation test bed was modified so that operators could either choose one variable or choose any combination of equally weighted variables for the automated planner to use in evaluating mission plans. Results from a human-participant experiment showed that operators rated their performance and confidence highest when using the dynamic objective function with multiple objectives. Allowing operators to adjust multiple objectives resulted in enhanced situational awareness, increased spare mental capacity, fewer interventions to modify the objective function, and no significant differences in mission performance. Adding this form of flexibility and transparency to automation in future unmanned vehicle systems could improve performance, engender operator trust, and reduce errors.
This paper presents decentralized methods for allocating heterogeneous tasks to a network of agents with different capabilities, when the rules of engagement dictate various cooperation constraints. The new methods are built upon the consensus-based bundle algorithm (CBBA), and the key extensions to the baseline CBBA are: (a) task decomposition and associated scoring modification to allow for softconstrained cooperation preference, and (b) a decentralized task elimination protocol to ensure satisfaction of the hardconstrained cooperation requirements. The new extensions are shown to preserve the robust convergence property of the baseline CBBA, and numerical examples on a cooperative track & strike mission verify the performance improvement by these extended capabilities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.