About the Computational Robotics Group

The Computational Robotics Group, led by Prof. Ron Alterovitz, addresses fundamental algorithmic challenges to enable robots to safely and autonomously complete tasks in clinical and home environments. We apply our algorithms to emerging robots that have the potential to enhance physician performance, improve patient care, and autonomously assist people in their homes.

The objective of motion planning in robotics is to compute actions that will guide a robot around obstacles to accomplish a task, such as reaching a tumor inside the body or cleaning a table. Creating motion planning algorithms for robots that operate inside human bodies or within people’s homes requires addressing difficult challenges: the algorithms must simultaneously consider deformations of soft tissues, compensate for uncertainty in robot motion and sensing, guarantee safety, and integrate human expertise into the planning process. To address these challenges, we develop and integrate a variety of computational methods, including sampling-based motion planners, physically-based simulations, optimal controllers, learning approaches, and medical image analysis methods.

We are currently applying our new algorithms to emerging, minimally-invasive medical devices such as steerable needles and tentacle-like robots that can bend around anatomical obstacles and provide physicians with access to clinical targets that previously were unreachable without open surgery. Applications include new procedures in neurosurgery and cardiothoracic surgery. We are also applying these algorithms to personal robots that can assist people with tasks of daily living. Applications include helping elderly and disabled individuals who may not otherwise be able to live on their own.

This research is supported by the National Science Foundation (NSF) and the National Institutes of Health (NIH).

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