Author name cluster

Ellen A. Cappo

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

IROS Conference 2020 Conference Paper

Data Driven Online Multi-Robot Formation Planning

Ellen A. Cappo
Arjav Desai
Nathan Michael

This work addresses planning for multi-robot formations online in cluttered environments via a data-driven search approach. The user-specified objective function governing formation shape and rotation is expressed in terms of offline demonstrations of robot motions (performed in an obstacle free environment). We leverage the offline demonstration to inform online planning for coordinated motions in the presence of obstacles. We formulate planning as a discrete search over demonstrated multi-robot actions, and select actions using a best-first approach to minimize edge expansions for fast online operation. Actions are selected using a heuristic based on their probability distribution exhibited in the demonstration, and we show that this approach is able to recreate coordinated motions exhibited in the demonstration when navigating in the obstructed conditions of the cluttered test environments. We demonstrate results in simulation over environments with increasing numbers of obstacles, and show that resulting plans are collision free and obey dynamic constraints.

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IROS Conference 2016 Conference Paper

Dynamically feasible and safe shape transitions for teams of aerial robots

Arjav Desai
Ellen A. Cappo
Nathan Michael

We consider the problem of generating dynamically feasible and safe plans for teams of aerial robots (quadrotors) while holding a fixed relative formation as well as transitioning between a sequence of formations. We extend the existing assignment and planning approaches for quadrotor teams to find minimal-time trajectories to enable team transition between non-rest initial and ending states while ensuring dynamic feasibility with respect to predefined kinematic, dynamic, and collision constraints. This work also presents a method for safe splitting and merging of robot formations according to input specification. The proposed methodology is capable of generating dynamically feasible and safe plans for teams of quadrotors in real time. We validate the performance of the proposed approach through various trials and scenarios conducted in simulation.

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IROS Conference 2016 Conference Paper

Persistent robot formation flight via online substitution

Derek Mitchell
Ellen A. Cappo
Nathan Michael

This paper presents an online optimization-based approach to compute trajectories to enable substitution of robots in formation-based deployments with durations that exceed the energy capacity of individual systems. The proposed algorithm computes trajectories in a multi-robot context to ensure a collision-free exchange, even where congestion is a concern. The quality of the resulting trajectories is determined by the amount of time spent deviating from the original plan while maintaining collision-free, speed-limited polynomial splines. The algorithm is shown through simulation and experiments to be viable with average deviation time gaps of less than 16 seconds and average computation times of under 3 minutes for the presented scenarios with varying numbers of robots and deployment specifications.

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ICRA Conference 2015 Conference Paper

Locomotive reduction for snake robots

Xuesu Xiao
Ellen A. Cappo
Weikun Zhen
Jin Dai
Ke Sun 0002
Chaohui Gong
Matthew J. Travers
Howie Choset

Limbless locomotion, evidenced by both biological and robotic snakes, capitalizes on these systems' redundant degrees of freedom to negotiate complicated environments. While the versatility of locomotion methods provided by a snake-like form is of great advantage, the difficulties in both representing the high dimensional workspace configuration and implementing the desired translations and orientations makes difficult further development of autonomous behaviors for snake robots. Based on a previously defined average body frame and set of motion primitives, this work proposes locomotive reduction, a simplifying methodology which reduces the complexity of controlling a redundant snake robot to that of navigating a differential-drive vehicle. We verify this technique by controlling a 16-DOF snake robot using locomotive reduction combined with a visual tracking system. The simplicity resulting from the proposed locomotive reduction method allows users to apply established autonomous navigation techniques previously developed for differential-drive cars to snake robots. Best of all, locomotive reduction preserves the advantages of a snake robot's ability to perform a variety of locomotion modes when facing complicated mobility challenges.

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