MOTIVATION: Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters. RESULTS: We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users. AVAILABILITY AND IMPLEMENTATION: ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The hashing method maps similar data of various types to binary hashcodes with smaller hamming distance, and it has received broad attention due to its low-storage cost and fast retrieval speed. However, the existing limitations make the present algorithms difficult to deal with for large-scale data sets: 1) discrete constraints are involved in the learning of the hash function and 2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting in both time and space complexity greater than O(n2). To address these issues, we propose a novel discrete supervised hash learning framework that can be scalable to large-scale data sets of various types. First, the discrete learning procedure is decomposed into a binary classifier learning scheme and binary codes learning scheme, which makes the learning procedure more efficient. Second, by adopting the asymmetric low-rank matrix factorization, we propose the fast clustering-based batch coordinate descent method, such that the time and space complexity are reduced to O(n). The proposed framework also provides a flexible paradigm to incorporate with arbitrary hash function, including deep neural networks and kernel methods, as well as any types of data to hash, including images and videos. Experiments on large-scale data sets demonstrate that the proposed method is superior or comparable with the state-of-the-art hashing algorithms.

This paper deals with the ofï-line route planning of Automated Guided Vehicles (AGVs). We propose a conflict-free route planning approach for the AGVs' control system in a warehouse system. The candidate routes are determined by using the improved Dijkstra's algorithm. The potential conflicts are detected and classified upon the comparison of node coordinates and occupancy times. A self-adaptive strategy is implemented to resolve the potential conflicts based on the conflict and mission classes. Presented simulation results demonstrate the efficiency of the proposed conflict-free route planning approach.

In this paper, a sensory-motor fusion-based manipulation and grasping control strategy has been developed for a robotic hand-eye system. The proposed hierarchical control architecture has three modules: 1) vision servoing; 2) surface electromyography (sEMG)-based movement recognition; and 3) hybrid force and motion optimization for manipulation and grasping. A stereo camera is used to obtain the 3-D point cloud of a target object and provides the desired operational position. The AdaBoost-based motion recognition is employed to discriminate different movements based on sEMG of human upper limbs. The operational space motion planning for bionic arm and force planning for multifingered robotic hand can be both transformed as a convex optimization problem with various constraints. A neural dynamics optimization solution is proposed and implemented online. The proposed formulation can achieve a substantial reduction of computational load. The actual implementation includes a bionic arm with dextrous hand, high-speed active vision, and an EMG sensors. A series of manipulation tasks consisting of tracking/recogniting/grasping of an object are implemented, and experiment results exhibit the responsiveness and flexibility of the proposed sensory motion fusion approach.

The quality of holes has important influence on the mechanical strength, assembly quality and life of aircraft, and inferior quality holes may even cause airplane crash and casualties. The perpendicular accuracy of holes during drilling is crucial one of all the factors influencing the quality of holes. In order to improve the perpendicular accuracy during drilling, this paper presented an attitude adjustment mechanism and attitude adjustment algorithm for the end effector. For the mechanism, it adjusts the drill bit attitude by using double eccentric discs and a spherical pair. After the surface normal vector at drilling point is obtained, double eccentric discs will rotate define angels calculated by using attitude adjustment algorithm based on the shortest time principle to achieve the coincidence of the surface normal vector and the drill bit axis vector if the angle between the normal vector and drill bit axis vector is greater than 0.5°. To demonstrate the method mentioned above, simulation and experiment are carried out on the aeronautical drilling robot. The results show that the attitude adjustment mechanism and attitude adjustment algorithm can increase the perpendicular accuracy of holes during drilling and satisfy the demand in automatic aircraft assembly.

This paper investigates dynamic balance optimization and control of quadruped robots with compliant/flexible joints under perturbing external forces. First, we formulate a constrained dynamic model of compliant/flexible joints for quadruped robots and a reduced-order dynamic model is developed considering the robot interaction with the environment through multiple contacts. A dynamic force distribution approach based on quadratic objective function is proposed for evaluating the optimal contact forces to cope with the external wrench, and fuzzy-based adaptive control of compliant/flexible joints for quadruped robots is proposed to suppress uncertainties in the dynamics of the robot and actuators. The dynamic surface control approaches and fuzzy learning algorithms are combined in the proposed framework. All the signals of the closed-loop system have proven to be uniformly ultimately bounded through Lyapunov synthesis. Simulation experiments were performed for a quadruped robot with compliant/flexible joints. The benefits of its tracking accuracy and robustness indicate that the proposed framework is promising for the robots with payload uncertainties and external disturbances.

Aiming at the robot drilling perpendicularity in aircraft assembly, a surface normal measurement method is presented and a drilling end effector is designed. Four laser ranging sensors are uniformly distributed around the drill to measure the surface normal as four non-coplanar points define a unique circumscribed sphere and the sphere center can be calculated. By this principle, the method measures four points coordinates on the curved surface in the drilling area using four laser ranging sensors. If the four laser ranging sensors are close enough to each other, it is reasonable to assume that the drilling point is on the sphere surface. Therefore, the connection line of the sphere center and the drilling point is the surface normal at the drilling point. The angle θ between the normal and the axis of the drill is calculated and if θ is larger than 0.5 degree, the drilling end effector will adjust the attitude of the drill to make sure θ is smaller than 0.5 degree to meet the requirement in aircraft assembly. A novel adjusting mechanism is designed in the end effector which can keep drill vertex immobile when adjusting drill position. Thus, it is not required to remove the drill vertex to the mark point. Simulation results of three kinds curved surfaces show that the surface normal measurement method is accurate and efficient. Experiments on aviation drilling system results also demonstrate the adjusting mechanism is effective with high accuracy.

This paper describe a design of aircraft drilling end-effector (ADEE) based on flexible railway which is mainly used for drilling on the surface of fuselage and wing of aircraft. The whole system is composed of several different modules. Flexible railway module (FRM) make ADEE move to any places where need to be drilled. Framework and Z axis module (FZM) uses high-rigidity framework and linear slide guide to guarantee the precision and stability of the drilling action. Compression and absorbing chipping module (CACM) adopts symmetric double-cylinder structure and generates 1000N compressing forces that can compress three layers aircraft skin and is increased by demand. Vacuous absorbing chipping mechanism uses high flow vacuum generator as vacuum air source and filters metal shavings by filter. Visual inspection module (VIM) can precisely calculate the distance between three points around drill bit and finished surface by calculating, and then achieves verticality deviation value through normal detection algorithm. Binary angular adjustment module (BAAM) revises angle deviation that is caused by the differences of adsorption position diameter of two rows of rack. ADEE is more flexible and better adaptable; meanwhile, it can greatly improve the efficiency and precision of the holes. It does not use drilling fluids in the whole manufacturing process and fundamentally eliminates the pollution problem of the drilling fluid. In conclusion, experiments show that ADEE can meet the requirements of automatic drilling of departments of aircraft and greatly enhance the quality and speed of drilling holes.

In this paper, we present an adaptive feedback controller for nonlinear multi-robot systems with unknown execution times and task dependencies. An adaptive self-tuning PID controller is discussed. A real-time force/torque leading controller for industrial multi-robot cooperation is addressed. Previously reported impedance control and hybrid control approaches are reviewed. Trajectory and velocity following issues for industrial manipulators are considered by using an error-reduction admittance control method. A number of experiments are presented. Experimental results show that this adaptive feedback controller can be used in industrial multi-robot coordination and force-guided assembly. With the proposed adaptive feedback scheduling algorithm, the system performance and the deadline miss ratio can be improved compared with the traditional feedback scheduling algorithm

Distributed multi-robot systems have attracted considerable attention over the past few decades. Multiple robots performing tasks together in a cooperative manner can have a significant advantage over a single robot, especially in parts assembly and load sharing between two or more coordinated robots. Most multi-robot systems are hard real-tune systems and require real-time scheduling. Many real-time schedulers have been discussed including round-robin, earliest-deadline-first (EDF), minimum-laxity-first (MLF), least-slack-time-first (LST), etc. Unfortunately, none of these schemes provide enough support for relative task constraints and timing constraints that are commonly used in multi-robot systems. This paper gives a task-oriented scheduling method that can help guarantee the safety, reliability and time deadline of a distributed multi-robot system. Experiments show that with the proposed algorithm, both the timing constraints and relative task interdependencies can be satisfied.