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Christopher Banks

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3 papers

2 author rows

ICRA Conference 2020 Conference Paper

Multi-Agent Task Allocation using Cross-Entropy Temporal Logic Optimization

Christopher Banks
Sean Wilson
Samuel Coogan 0001
Magnus Egerstedt

In this paper, we propose a graph-based search method to optimally allocate tasks to a team of robots given a global task specification. In particular, we define these agents as discrete transition systems. In order to allocate tasks to the team of robots, we decompose finite linear temporal logic (LTL) specifications and consider agent specific cost functions. We propose to use the stochastic optimization technique, cross entropy, to optimize over this cost function. The multi-agent task allocation cross-entropy (MTAC-E) algorithm is developed to determine both when it is optimal to switch to a new agent to complete a task and minimize the costs associated with individual agent trajectories. The proposed algorithm is verified in simulation and experimental results are included.

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IROS Conference 2019 Conference Paper

Specification-Based Maneuvering of Quadcopters Through Hoops

Christopher Banks
Kyle Slovak
Samuel Coogan 0001
Magnus Egerstedt

In this paper, we study the problem of navigating quadcopters through a sequence of hoops. The specification may be given directly or indirectly via a linear temporal logic (LTL) formula. We approach this problem in three phases. First, we introduce a planner that generates a path through a given sequence of hoops. Second, we augment our planner to leverage a given specification in linear temporal logic (LTL) and generate a sequence that satisfies this specification. Third, we implement cross-entropy optimization on this planner to enhance trajectory performance where quadcopter trajectories are modified within the solution space to optimize over a cost function. We implement this planner as a novel interaction modality between users and quadcopters on the Robotarium. Simulation and experimental results are provided.

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AAAI Conference 2017 Conference Paper

Collaborative Planning with Encoding of Users’ High-Level Strategies

Joseph Kim
Christopher Banks
Julie Shah

The generation of near-optimal plans for multi-agent systems with numerical states and temporal actions is computationally challenging. Current off-the-shelf planners can take a very long time before generating a near-optimal solution. In an effort to reduce plan computation time, increase the quality of the resulting plans, and make them more interpretable by humans, we explore collaborative planning techniques that actively involve human users in plan generation. Speciﬁcally, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner. Through human subject experimentation, we empirically demonstrate that this approach results in statistically signiﬁcant improvements to plan quality, without substantially increasing computation time. We also show that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions, as compared to plans that do not incorporate userprovided strategies.

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