Robots can be used to sample and monitor our environment. Measurement samples are collected from different locations so that a "distribution map" that describes certain environmental attribute (e.g., pollutant concentration) can be reconstructed.
There is an urgent need to improve the operational mobility of autonomous ground vehicles to navigate in off-road environments that are tremendously unstructured including complex terrains such as rock fields, ditches, ridges, cliffs, etc.
When uncertainty (e.g., robot's imperfect motion) is considered, stochastic methods are required to cope with the system stochasticity especially if it is time-varying due to spatiotemporal enviromental disturbances. Recent work also includes efficient reinforcement learning using minimalist training trials.
Mapping with onboard sensors is an essential function for robots. We develop exploration and mapping components that allow a vehicle to construct navigable space from perception inputs (e.g., cameras) and also plan feasible motion in unstructured outdoor environments.
When the number of robots is large, the robots need to explore 3D space and interact among themselves and also with surroundings including static obstacles and dynamic objects such as humans.
A common constraint for multi-robot system is communication, where each robot has a limited communication range and is able to exchange information only with neighbors in its vicinity. We work on designing decentralized coordination methods such as distributed task allocation mechanisms.
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