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.

We describe the software components of a robotics system designed to autonomously grasp objects and perform dexterous manipulation tasks with only high-level supervision. The system is centered on the tight integration of several core functionalities, including perception, planning and control, with the logical structuring of tasks driven by a Behavior Tree architecture. The advantage of the implementation is to reduce the execution time while integrating advanced algorithms for autonomous manipulation. We describe our approach to 3-D perception, real-time planning, force compliant motions, and audio processing. Performance results for object grasping and complex manipulation tasks of in-house tests and of an independent evaluation team are presented.

This paper investigates the kinematics of spin-rolling motion of rigid objects. This paper does not consider slipping but applies a Darboux frame to develop kinematics of spin-rolling motion, which occurs in a nonholonomic system. A new formulation of spin-rolling motion of the moving object is derived in terms of contravariant vectors, rolling velocity, and geometric invariants, including normal curvature, geodesic curvature, and geodesic torsion of the respective contact curve. The equation is represented with geometric invariants. It can be readily generalized to suit both arbitrary parametric surface and contact trajectory and can be differentiated to any order. Effect of the relative curvatures and torsion on spin-rolling kinematics is explicitly presented. The translation velocity of an arbitrary point on the moving object is also derived based on the Darboux frame.

This paper adopts a coordinate-free approach to investigate the kinematics of rigid bodies with rolling contact. A new equation of angular velocity of the moving body is derived in terms of the magnitude of rolling velocity and two sets of geometric invariants belonging to the respective contact curves. This new formulation can be differentiated up to any order. Furthermore, qualitative information about trajectory planning can be deduced from this equation if the characteristics of rolling objects and the motion are taken into consideration.

This paper introduces for the first time a metamorphic palm and presents a novel multifingered hand, known as Matahand, with a foldable and flexible palm that makes the hand adaptable and reconfigurable. The orientation and pose of the new robotic hand are enhanced by additional motion of the palm, and workspace of the robotic fingers is complemented with the palm motion. To analyze this enhanced workspace, this paper introduces finger-orientation planes to relate the finger orientation to palm various configurations. Normals of these orientation planes are used to construct a Gauss map. Adding an additional dimension, a 4-D ruled surface is generated to illustrate orientation and pose change of the hand, and an orientation-pose manifold is developed from the orientation-pose ruled surface. The orientation and workspace analysis are further developed by introducing a triangular palm workspace that evolves into a helical surface and is further developed into a 4-D representation. Simulations are presented to illustrate the characteristics of this new dexterous hand.