Underwater electrosense is bio-inspired by weakly electric fishes that use an electric field to see the objects in the water. Current studies on engineering electrosense focus on designing sophisticated sensors and algorithms for emulating biological functions including localization and identification. This work aimed to develop a planar sensor equipped with a dense electrode array that is capable of providing accurate and dense data for identifying objects of similar volume and aspect ratio, which has been a challenge in underwater sensing. After sensor design and implementation were presented, convolutional neural networks (CNN), which are widely used in digital image recognition, was trained using both simulation and experimental data. In the simulation, the overall success rate on identifying the sphere, cube, and rod is 92.6% by a 28 × 28 electrode array. In the preliminary experimental tests, a sensor with 16 × 16 electrode array achieved an overall success rate of 90.4% on identifying a sphere and a rod.

Underwater sensing using electric field has been traditionally modeled by the computationally heavy finite element method (FEM) or boundary element method. In this paper, techniques in light scattering problems were for the first time expanded to underwater active electrosense. Numerical solutions of the volume integral equation were formulated, including a method of moment and discrete dipole approximation (DDA). We found that only DDA was suitable for conductive mediums and the connection between DDA, and a well-known sphere perturbation formula was further established. DDA was found to perform more than 100 times faster than FEM with only a 10% relative difference in underwater electrosense problems. A DDA-based unscented Kalman filter was proposed for inferring object size and location, and a one-dimensional sensor was made for further experimental validation. This paper lays a solid foundation for real-world underwater electrosensors.

Weakly electric fish use self-established electric field to sense the underwater environment that may be cluttered and turbid. Previous works on building artificial counterparts are limited to simplest cases, as no analytical solutions exist under complex boundary conditions. Universal numerical approaches like Finite Element Method (FEM) and Boundary Element Method (BEM) suffer from lengthy meshing process and heavily computational burden. In this paper, discrete dipole approximation (DDA), which is widely used in light scattering and absorption problems, was for the first time proposed to be applied for underwater electrosense. This approach is lightweight, flexible and computationally efficient compared with FEM. It was simulated in electric fields excited by parallel-plate electrodes and spherical electrodes of a simplified robotic model. A constrained unscented Kalman filter (CUKF) was further utilized to localize the position and identify the size of an invading cube. Results comparison with FEM indicate the differences of a cuboidal object in two orthogonal positions were 7.10% and 10.46% respectively, and the difference in size was 11.82%. These results were achieved at a cost of less than 1% of the computational effort of the FEM. The proposed approach proved effective from the simulation results and laid a solid foundation for real-time underwater active electrosense in a more general environment.

Artificial sensory system is promising to navigate in cluttered and turbid underwater environment by achieving similar functions of biological electrosense of weakly electric fish. In this paper we designed an electrosensory membrane that can operate in a full 3-dimensional mode. Algorithms on the object localization were also designed and tested based on numerical methods of electric field forward simulation. We combined the statistic learning method by training a multi-layer neural network and a probabilistic approach by applying a constrained unscented Kalman filter (CUKF). This exploits the merits of fast estimation and precise signal marching process. Experimental results showed that the detection and localization with the reported sensor were quick and accurate, with errors of around 10 mm using one-step neural network mapping and about 5 mm in close-range using CUKF. This work demonstrated the effectiveness of proposed electrosensory membrane and algorithms.

In existing flocking and coordination control systems of multiple agents, an agent is considered as a single point or a circular disk. For agents with a long and narrow shape, fitting them to circular disks results in a problem with the large conservative area. This paper presents a design of distributed controllers for flocking ofNmobile agents with an elliptical shape and with limited communication ranges. A separation condition for elliptical agents is first derived. Smooth orp-times differentiable step functions are then introduced. These functions and the separation condition between the elliptical agents are embedded in novel potential functions to design control algorithms for flocking. The controllers guarantee 1) no switchings in the controllers despite the agents' limited communication ranges; 2) no collisions between any agents; 3) asymptotic convergence of each agent's generalized velocity to a desired velocity; and 4) boundedness of the flock size, which is defined as the sum of all generalized distances between the agents, by a constant.

This paper presents a constructive method to design output-feedback cooperative controllers that force a group of N unicycle-type mobile robots with limited sensing ranges to perform desired formation tracking, and guarantee no collisions between the robots. For each robot an interlaced observer is designed to estimate the robot unmeasured velocities. p times differentiable jump functions are introduced and incorporated into novel potential functions to design a formation tracking control system.

This paper presents a boundary controller to reduce transverse motion of flexible marine risers driven by a hydraulic system at the top end of the risers under environmental disturbances. The boundary controller is designed based on Lyapunov's direct method and the backstepping technique. Proof of existence and uniqueness of the solutions of the closed loop control system is carried out by using the Galerkin approximation method.

A constructive method is presented to design cooperative controllers that force a group of N unicycle-type mobile robots with limited sensing ranges to perform desired formation tracking, and guarantee no collisions between the robots. Robot physical dimensions and dynamics are considered in the control design, p times differential bump functions are introduced and incorporated into novel potential functions for control design. Despite the robot limited sensing ranges, no switchings are needed to solve the collision avoidance problem.

A constructive method is presented to design a global robust and adaptive output feedback controller for dynamic positioning of surface ships under environmental disturbances. Measurements of the ship's velocities are not required for feedback. The ship's parameters are not required to be known. An adaptive observer is first designed to estimate the ship's velocities and parameters. The control is then designed based on Lyapunov's direct method to force the ship's position and orientation to globally asymptotically converge to the desired values.

In this paper, we investigate the operation of the queue-formation structure (or Q-structure) in multi-robot teams with limited communications. Information flow is divided into two time scales: (i) the fast time scale where the robots' reactive actions are determined based only on local communications, and (ii) the slow time scale, where information required is less demanding, can be collected over a longer time with intermittent information loss. Therefore, there is no need for global information at all times, reducing the overall communication load. In addition, a dynamic target determination algorithm, based on the Q-structure, is used to produce a series of targets that incrementally guide each robot into formation. It provides greater control over the distance between robots on the same queue for better formation scaling. An analysis of the convergence of the system of robots is provided. Simulation studies verify the effectiveness of the scheme.

In this paper, we investigate the operation of the queue-formation structure (or Q-structure) in multirobot teams with limited communication. Information flow can be divided into two time scales: (1) the fast-time scale where the robots' reactive actions are determined based only on local communication and (2) the slow-time scale, where information required is less demanding, can be collected over a longer time, and intermittent information loss can be afforded. Therefore, there is no need for global information at all times, reducing the overall communication load. In addition, a dynamic target determination algorithm, based on the Q-structure, is used to produce a series of targets that incrementally guide each robot into formation. It provides greater control over the distance between robots on the same queue, instead of relying on inter robot repulsive distance, and, allows better formation scaling. An analysis of the convergence of the system of robots and realistic simulation studies are provided.

We present a time-varying global output-feedback controller that solves both tracking and stabilization for unicycle-type mobile robots simultaneously at the torque level. The controller synthesis is based on a coordinate transformation, Lyapunov's direct method, and backstepping technique. Simulations demonstrate the result.