This thesis provides strategies for multiple vehicles to explore unknown environments in a cooperative and systematic manner. These strategies are called Simultaneous Cooperative Exploration and Networking (SCENT) strategies. As the basis for development of SCENT strategies, we first tackle the motion control and planning for one vehicle with range sensors. In particular, we develop the curve-tracking controllers for autonomous vehicles with rigidly mounted range sensors, and a provably complete exploration strategy is proposed so that one vehicle with range sensors builds a topological map of an environment. The SCENT algorithms introduced in this thesis extend the exploration strategy for one vehicle to multiple vehicles. The enabling idea of the SCENT algorithms is to construct a topological map of the environment, which is considered completely explored if the map corresponds to a complete Voronoi diagram of the environment. To achieve this, each vehicle explores its local area by incrementally expanding the already visited areas of the environment. At the same time, every vehicle deploys communication devices at selected locations and, as a result, a communication network is created concurrently with a topological map. This additional network allows the vehicles to share information in a distributed manner resulting in an efficient exploration of the workspace. The efficiency of the proposed SCENT algorithms is verified through theoretical investigations as well as experiments using mobile robots. Moreover, the resulting networks and the topological maps are used to solve coordinated multi-robot tasks, such as capturing intruders.

Cooperative control enables combinations of sensor data from multiple autonomous underwater vehicles (AUVs) so that multiple AUVs can perform smarter behaviors than a single AUV. In addition, in some situations, a human-driven underwater vehicle (HUV) and a group of AUVs need to collaborate and preform formation behaviors. However, the collective dynamics of a fleet of heterogeneous underwater vehicles are more complex than the non-trivial single vehicle dynamics, resulting in challenges in analyzing the formation behaviors of a fleet of heterogeneous underwater vehicles. The research addressed in this dissertation investigates the collective dynamics and control of a fleet of heterogeneous underwater vehicles, including multi-AUV systems and systems comprised of an HUV and a group of AUVs (human-AUV systems). This investigation requires a mathematical motion model of an underwater vehicle. This dissertation presents a review of a six-degree-of-freedom (6DOF) motion model of a single AUV and proposes a method of identifying all parameters in the model based on computational fluid dynamics (CFD) calculations. Using the method, we build a 6DOF model of the EcoMapper and validate the model by field experiments. Based upon a generic 6DOF AUV model, we study the collective dynamics of a multi-AUV system and develop a method of decomposing the collective dynamics. After the collective dynamics decomposition, we propose a method of achieving orientation control for each AUV and formation control for the multi-AUV system. We extend the results and propose a cooperative control for a human-AUV system so that an HUV and a group of AUVs will form a desired formation while moving along a desired trajectory as a team. For the post-mission stage, we present a method of analyzing AUV survey data and apply this method to AUV measurement data collected from our field experiments carried out in Grand Isle, Louisiana in 2011, where AUVs were used to survey a lagoon, acquire bathymetric data, and measure the concentration of reminiscent crude oil in the water of the lagoon after the BP Deepwater Horizon oil spill in the Gulf of Mexico in 2010.