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This paper presents a nonlinear, backstepping depth and pitch controller for a dual-bladder buoyancy engine actuated by gear pumps. Flow-rate feedback is obtained using a custom flow sensor comprised of a differential pressure sensor and a small, 3D-printed attachment. The controller is simulated using a model of the CephaloBot, our in-house developed autonomous underwater vehicle (AUV). Its depth control capability is also experimentally validated using a single-bladder buoyancy engine on-board a smaller-scale test cylinder. Lyapunov stability analysis shows global, asymptotic stability, which is exhibited in our simulation. Our experiments verify that this buoyancy engine is a feasible and effective depth controller for AUVs. 

This paper presents a nonlinear, backstepping depth and pitch controller for a dual-bladder buoyancy engine actuated by gear pumps. Flow-rate feedback is obtained using a custom flow sensor comprised of a differential pressure sensor and a small, 3D-printed attachment. The controller is simulated using a model of the CephaloBot, our in-house developed autonomous underwater vehicle (AUV). Its depth control capability is also experimentally validated using a single-bladder buoyancy engine on-board a smaller-scale test cylinder. Lyapunov stability analysis shows global, asymptotic stability, which is exhibited in our simulation. Our experiments verify that this buoyancy engine is a feasible and effective depth controller for AUVs. 

A control methodology for aerial or aquatic vehicles is presented that leverages intelligent distributed sensing inspired by the lateral line found in fish to directly measure the fluid forces acting on the vehicle. As a result, the complex robot control problem is effectively simplified to that of a rigid body in a vacuum. Furthermore, by sensing these forces, they can be compensated for immediately, rather than after they have displaced the vehicle. We have created a sensory shell around a prototype autonomous underwater vehicle, derived algorithms to remove static pressure and calculate total force from the discrete measurements using a fitting technique that filters sensor error, and validated the control methodology on a vehicle in the presence of multiple fluid disturbances. This sensing control scheme reduces position tracking errors by as much as 72% compared to a standard position error feedback controller. 

This paper presents a multi-agent flocking scheme for real-time control of homogeneous unmanned aerial vehicles (UAVs) based on smoothed particle hydrodynamics. Swarm cohe- sion, collision avoidance, and velocity consensus are concurrently satisfied by characterizing the emerging macroscopic flock as a continuous fluid. Two vital implementation issues are addressed in particular including latency in information fusion and directionality of com- munication due to antenna patterns. Symmetric control forces are achieved by meticulous scheduling of inter-vehicle communication to sustain the motion stability of the flock. A gener- alized, anisotropic smoothing kernel that takes into account the relative position and attitude between agents is adopted to address potential flocking instability introduced by communi- cation anisotropy due to the antenna radiation pattern. The feasibility of the technique is demonstrated experimentally using a single UAV avoiding a virtual obstacle.