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The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.

This paper proposes a novel modular selfreconfigurable robot (MSRR) "FreeBOT", which can be connected freely at any point on other robots. FreeBOT is mainly composed of two parts: a spherical ferromagnetic shell and an internal magnet. The connection between the modules is genderless and instant, since the internal magnet can freely attract other FreeBOT spherical ferromagnetic shells, and not need to be precisely aligned with the specified connector. This connection method has fewer physical constraints, so the FreeBOT system can be extended to more configurations to meet more functional requirements. FreeBOT can accomplish multiple tasks although it only has two motors: module independent movement, connector management and system reconfiguration. FreeBOT can move independently on the plane, and even climb on ferromagnetic walls; a group of FreeBOTs can traverse complex terrain. Numerous experiments have been conducted to test its function, which shows that the FreeBOT system has great potential to realize a freeform robotic system.

In this paper, the 2D robot-to-robot relative pose (position and orientation) estimation problem based on ego-motion and noisy distance measurements is considered. We address this problem using an optimization-based method, which does not require complicated numerical analysis while yields no inferior relative localization (RL) results compared to existing approaches. In particular, we start from a state-of-the-art method named square distances weighted least square (SD-WLS), and reformulate it as a non-convex quadratically constrained quadratic programming (QCQP) problem. To handle its non-convex nature, a semidefinite programming (SDP) relaxation optimization-based method is proposed, and we prove that the relaxation is tight when measurements are free from noise or just corrupted by small noise. Further, to obtain the optimal solution of the relative pose estimation problem in the sense of maximum likelihood estimation (MLE), a theoretically optimal WLS method is developed to refine the estimate from the SDP optimization. Comprehensive simulations and well-designed experiments are presented for validating the tightness of the SDP relaxation, and the effectiveness of the proposed algorithm is highlighted by comparing it to the existing approaches.

This paper introduces an obstacle-crossing strategy, and the self-reconfiguration algorithm for a new class of modular robots called the rolling sphere, which can fit obstacles represented by cubes of different sizes due to the chain connection of multiple spheres. For the self-reconfiguration of the rolling spheres, a large gradient is obtained by classifying its action types and hierarchically minimizing the distance between the initial configuration and the final configuration. The most direct use of this large gradient is the fast crossing of various obstacles, by jointing multiple self-reconfigurations according to the OctoMap of the obstacles. It is verified in simulation that the self-reconfiguration takes full advantage of the parallel movement of multiple modules to reduce the total time steps, and the obstacle-crossing strategy can adapt to a variety of obstacles.

Towards long range and high endurance sailing, energy is of utmost importance. Moreover, benefiting from the dominance of the sailboat itself, it is energy-saving and environment-friendly. Thus, the sailboat with energy planning problem is meaningful. However, until now, the sailboat energy optimization problem has rarely been considered. In this paper, we focus on the energy consumption optimization of an autonomous sailboat. It has been formulated as a Nonlinear Programming problem (NLP). We deal with it with a hybrid control scheme, in which pseudo-spectral (PS) optimal control method is used in heading control, and a model-free framework guided by Extreme Seeking Control (ESC) is used in sail control. The optimal path is generated with the optimal input motor torques in time series. As a result, both simulation and experiments have validated motion planning and energy planning performance. Notably, about 7% of energy is saved on average. Our proposed method can make sailboats sailing longer and sustainable.

Automatic latching for charging in a disturbed environment for Unmanned Surface Vehicle (USVs) is always a challenging problem. In this paper, we propose a two-stage automatic latching system for USVs charging in berth. In Stage I, a vision-guided algorithm is developed to calculate an optimal latching position for charging. In Stage II, a novel latching mechanism is designed to compensate the movement misalignments from the water disturbance. A set of experiments have been conducted in real-world environments. The results show the latching success rate has been improved from 40% to 73.3% in the best cases with our proposed system. Furthermore, the vision-guided algorithm provides a methodology to optimize the design radius of the latching mechanism with respect to different disturbance levels accordingly. Outdoor experiments have validated the efficiency of our proposed automatic latching system. The proposed system improves the autonomy intelligence of the USVs and provides great benefits for practical applications.

This paper presents a novel Cable Climbing Robot CCRobot-III, which is the third version designed for bridge cable inspection tasks, aiming at surpassing previous versions in terms of climbing speed and payload capacity. Benefiting from Split-type Wire-driven design, CCRobot-III can climb along a 90-110mm diameter bridge cable in inchworm-like gait at a speed of up to 12m/min, and carrying more than 40kg payload at the same time. CCRobot-III consists of a climbing precursor and a main-body frame. The two parts are connected and driven by steel wires. The climbing precursor, acting as a mobile anchor, moves quickly on a bridge cable. The mainbody frame, acting as a mobile winch, carries payload and pulls itself to a certain position with steel wires. Both parts have one or two pairs of palm-based gripper, which is the key component for providing strong adhesion to support the robot climbing. Experimental results have shown that CCRobotIII possesses outstanding climbing performance, high payload capacity, and good adaptability to complex conditions of cable surface. Moreover, it has potential engineering applications on the cable-stayed bridge for fieldwork.

Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.

Collaborative object transportation has become a popular study trend with its remarkable application foreground. In previous relevant studies, localization of the transported object has always been accomplished by additional devices rather than robot onboard equipments. This paper presents a generalized multi-robot leader-follower system for collaborative object transportation and an onboard object localization algorithm for trajectory tracking of the target object. In this system, the mobile robots can directly push a cubic object without extra gripping devices, when tracking the reference trajectory. During the control process, the object is regarded as an virtual leader, whose localization information is utilized as the feedback, while the mobile robots are considered as the followers. In absence of external localization systems, the proposed onboard localization algorithm provides the real-time position information of the object using scan data from lidars equipped on the robots. A performed measurement accuracy test shows high precision of this algorithm. Finally, a lane-changing experiment of object transportation is conducted, and it verifies this multi-robot leader-follower system.

The commercial solar energy market is dominated by non-concentrating photovoltaics and concentrating solar thermal systems. With the development of high efficiency thermoelectric materials, the solar thermoelectric generator is becoming to be a competitive alternative solar energy technology in certain applications. This paper proposes a simulation model and a design methodology for solar thermoelectric generator. Geometry parameters, namely the height, the fill-ratio, the ratio of the cross-section area of n-type material over p-type material of thermoelectric module, solar concentration ratio, were analyzed under different operating conditions. In order to perform the analysis, an L27 (35) orthogonal array was employed to assess all of the design parameters returning the maximum output power. By the analysis of variance, the effect of each design parameter on the output power performance and the interaction between design parameters are identified. The optimal design parameter set is also obtained and it is found that the optimal design performs can potentially achieve 5.47 W output power compared with the output power of 1.95 W from the original design in most operating conditions.

In this paper, we propose a navigation algorithm oriented to multi-agent environment. This algorithm is expressed as a hierarchical framework that contains a Hidden Markov Model (HMM) and a Deep Reinforcement Learning (DRL) structure. For simplification, we term our method Hierarchical Navigation Reinforcement Network (HNRN). In high-level architecture, we train an HMM to evaluate the agents perception to obtain a score. According to this score, adaptive control action will be chosen. While in low-level architecture, two sub-systems are introduced, one is a differential target-driven system, which aims at heading to the target; the other is a collision avoidance DRL system, which is used for avoiding dynamic obstacles. The advantage of this hierarchical structure is decoupling the target-driven and collision avoidance tasks, leading to a faster and more stable model to be trained. The experiments indicate that our algorithm has higher learning efficiency and rate of success than traditional Velocity Obstacle (VO) algorithms or hybrid DRL method.

Personal handwriting style fonts generation is a diverting but time-consuming task due to the large size of Chinese character set. In addition, unlike standard printed style fonts, hand-writing style fonts are of more complicated stroke and glyph feature. In this paper, an improved network architecture is proposed for learning and generation of personal hand-writing style fonts based on small character set. The network is composed of three sub-networks: 1) a classification network for identifying the general style of the target fonts; 2) a generating network for transferring the identified fonts to the target fonts; 3) a discriminating network for differentiating the generated image from real ones. The experiments revealed the effectiveness of the model for generating personal hand-writing style font with relatively small data size, reduction by a scale of 10 comparing to previous reported works.

This paper presents a new model of automatic music generation which is based on simulated annealing and genetic algorithm. Our model uses simulated annealing algorithm to create melody units on different chords, and regards them as the genes in genetic algorithm, which can pick out a fine combination of these genes. This model is efficient for producing massive fair-sounding pieces of double-track music, when provided with a customized metric. Hence, it can be especially useful for massively producing background musics for video games, or providing inspirations for music composers. In this paper, we detailedly state how to transform the prior knowledge about music theory into a reasonable metric, and propose a sample metric for reference. Using this sample metric, we generate a set of music pieces as test cases. The evaluation results show that these pieces fully satisfy the need of quality and creativity.

The ability to maintain balance and stability is a major challenge for quadruped robots in locomotion. For example, when walking in the unstructured environment, the robot not only needs to adjust its gait according to the terrain information, but also needs to tune the body posture based on the real-time body posture acquired from inertial measurement unit (IMU) to maintain stability. In this paper, we present a method that focuses on the design of the posture correction controller based on the IMU feedback for adaptive slope walking for a quadruped robot. To this end, the posture correction controller is integrated into the foot trajectory generator to maintain balance. We experimentally validated our approach both on simulation and real quadruped robot. Compared with the one only with the foot trajectory generator, the quadruped robot can walk more stable with the posture correction controller integrated into the foot trajectory generator and has a better locomotion ability. Our method increases locomotion stability when walking on the slopes.

In this paper, we present an accurate approach to estimate vehicles' pose and shape from off-board multiview images. The images are taken by monocular cameras and have small overlaps. We utilize state-of-the-art convolutional neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a Cross Projection Optimization (CPO) method to estimate the 3D pose. During the iterative CPO process, an adaptive shape adjustment method named Hierarchical Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach is evaluated under both simulated and real-world scenes for performance verification. It's shown that our algorithm outperforms other existing monocular and stereo methods for vehicles' pose and shape estimation. This approach provides a new and robust solution for off-board visual vehicle localization and tracking, which can be applied to massive surveillance camera networks for intelligent transportation., Comment: 8 pages, 8 figures, published in ROBIO 2018

Trajectory tracking is a decisive factor that affects autonomous driving performance of mobile robots in real-world applications, such as industry, service and other areas. Nowadays, there has been many works about path planning and trajectory tracking. Whereas there are problems to be improved to make mobile robot well applied in practical world. For example, generated trajectory can't be tracked smoothly, and the robot has to stop and rotate itself to adjust pose and pass segments in trajectory because of constrains of path curvature and robot's kinematics model. In this paper, a new trajectory planning algorithm with cubic spline interpolation method was proposed, which fully considered constrains of robot model including velocity and acceleration bounds. And corresponding trajectory was generated. Then a robust controller was built to conduct tracking tests. Experimental results demonstrate that our planning and tracking algorithm could help robot achieve a smooth, accurate and efficient trajectory tracking performance without any stop.

In the field of autonomous driving, abundant researches have been carried out in development of advanced Lidar or vision algorithms for mapping and localization, and some efforts in the field of sensor fusion have been made to combine the advantages of multiple sensor types. However, most works focused on the 2D map construction, which had its own limitation with complex environments. In this paper, a new algorithm based on the fusion of Lidar and Inertial Measurement Unit (IMU) data is developed to construct a 2.5D map. We integrate the height information transformed from IMU sensor with laser scan results to construct a 2.5D occupied grid map. The proposed algorithm is convenient to implement on real-world mobile vehicles, and it is fast and robust. The effectiveness of our algorithm is validated by both simulation and practical experiment.

Leader-follower system could expand the load capacity in transport tasks. While few related literatures pay attention to the physical limitation and limit the maximum output velocity of designed controller. This paper expands a trajectory tracking controller with velocity constraints to leader-follower system. Leader and follower are both subjected to linear velocity constraint and angular velocity constraint due to the physical limitation. Selected trajectory tracking controller is applied in leader-follower system. Meanwhile, noises in reference trajectory of follower are released by Kalman filter. Then, a physical experiment platform with Turtlebots is established to verify this control scheme. Finally, experiment results of several examples proved the practicability of proposed control system. The velocity commands of this control system would not exceed the limitation of physical limitation.

Autonomous Surface Vehicles (ASVs) provide an effective way to actualize applications such as environment monitoring, search and rescue, and scientific researches. However, the conventional ASVs depends overly on the stored energy. Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an auxiliary propulsion system. The electric energy cost of Hybrid Sailboat needs to be optimized to achieve the ocean automatic cruise mission. Based on adjusted setting on sails and rudders, this paper seeks the optimal trajectory for autonomic cruising to reduce the energy cost by changing the heading angle of sailing upwind. The experiment results validate the heading angle accounts for energy cost and the trajectory with the best heading angle saves up to 23.7% than other conditions. Furthermore, the energy-time line can be used to predict the energy cost for long-time sailing., 6pages, IEEE ROBIO 2018, accpeted

This paper investigates the cooperative moving-target enclosing control problem of networked unicycle-type nonholonomic vehicles with constant linear velocities. The information of the target is only known to some of the vehicles, and the topology of the vehicle network is described by a directed graph. A dynamic control law is proposed to steer the vehicles, such that they can get close to orbiting around the target while the target is moving with a time-vary velocity. Besides, the constraint of bounded angular velocity for the vehicles can always be satisfied. The proposed control law is distributed in the sense that each vehicle only uses its own information and the information of its neighbors in the network. Finally, simulation results of an example validate the effectiveness of the proposed control law.

In this paper, a new mechanical design of adaptive mini gripper has been proposed. This mini gripper is able to attach itself on a wide range of trees with different diameters and even some rough surfaces with bumps and asperities, due to its adaptive structure. Worm gear design enables its self-lock capbility when attaching itself to a surface, preventing it from falling down. Besides, self-lock property of worm gear also contributes to the energy saving ability. Furthermore the motion of the gripper is all active, which also improves the stability of the gripper. The structure is of $55 \mathrm {m}\mathrm {m}^{*} 55 \mathrm {m}\mathrm {m}^{*} 47\mathrm {m}\mathrm {m}$ in size, which is also an advantage to the wide use of this gripper. What’s more, this adaptive mini gripper weighs 79 grams, but it can hold 1700 grams in weight, more than 21.5 times of its own weight. In addition, the miniaturization work of the gripper contributes to mini size of climbing robots, and more work and practical use can be done with a much smaller size.

Accurate turning is not an easy task for the quadruped robot and previous work mainly addresses the problem with complex control strategy or systems. This paper proposes a design method for the accurate turning motion of a quadruped robot with 12 degrees of freedom (DOF). Unlike others, we have achieved precise turning motion with a relatively simple geometric approach. Once the parameters of desired movement are set, such as the turning radius and angles, the robot can realize corresponding circle locomotion. A design architecture including geometric analysis, 3D foot trajectory design and inverse kinematics is presented to solve the problem systematically. In addition, according to the different conditions, the corresponding compensation coefficient is necessarily considered in order to acquire precise values of leg joints. At last, comparison results by several experiments have identified the feasibility and accuracy of turning along a circle trajectory based on our proposed method.

Trajectory tracking control is one of the core techniques that impacts the auto-driving performance of a mobile robot. Whereas, there lacks enough work on reference trajectory generation and controller design for practical usage. This paper considers mobile robots with unicycle vehicle model on which most of automatic guided vehicles (AGVs) in real world are built. A new trajectory planning algorithm is developed, and is applied along with a control law considering constraints of the unicycle model and limited motor capabilities. The proposed algorithm is easy to be implemented on real world AGVs, and it yields a fast, accurate and robust trajectory tracking performance. The effectiveness of the algorithm is validated by simulation tests.

A vision-based perception system for understanding the outdoor environment has been proposed. This system utilizes and applies the useful information from the binocular cameras of a legged robot for landform — terrain — ground environmental perception. The ground texture recognition can provide information about the material of the ground then a legged robot can choose an appropriate walking pose accordingly. With the terrain perception and obstacle detection, combined with semantic segmentation of the environment, a legged robot can move towards the target with intelligent obstacle avoidance strategy. Based on this vision-based perception system, we propose a vision-based perception framework for outdoor navigation tasks applicable to legged robots. In this framework, environmental modeling and situation assessment will be firstly carried out combined with multi-mode sensor fusion, and then footsteps and local path planning can be generated.

This paper presents the gait design and comparison study of a quadruped robot. The main contributions of this paper are: (i) Explore the modularized system design of an 8 degree-of-freedom quadruped robot with a power management system. (ii) Propose a gait design method based on drive function. (iii) Propose another gait design method based on foot trajectory. (iv) The simulation of the walk and trot gaits with above-mentioned methods are validated in Webots. (v) The walk and trot gait are successfully implemented in the real robot. Experimental results show that the stable locomotion can be achieved with improved elliptical foot trajectory.

Currently, the shock absorption system for outdoor AGV is mainly built with identical-spring solution, in which all the damping springs have the identical elastic coefficient. In this paper, we introduce a method to design the shock absorption system of the AGV using dual-spring solution. Springs of two different elastic coefficients are used, and an enhanced performance in obstacle-crossing capability can be achieved. The proposed dual-spring solution design method takes several critical outdoor working requirements as design constraints, and integrates damping system analysis to determine the optimal coefficients for the dual-spring system. To validate the effectiveness of this dual-spring design method, theoretical analysis is fully derived and a AGV chassis is physically built using dual-spring solution and is subjected to a series of validation tests.

Monocular cameras are not generally considered as a 3D measurement tool in the field of intelligent transportations. This belief limits the application of monocular cameras in many applications such as guiding robots or vehicles for autonomous driving. In this paper, we demonstrate that with the reference of a license plate, a monocular camera can be used as a tool to measure 3 dimensional parameters (width, height and length) of a passenger vehicle down to 10 cm accuracy. The proposed measurement method does not reply on any training process, but is designed as a fast and direct approach utilizing a series of fundamental computer vision techniques such as ROI extraction, edge/line detection, space coordinates transformations, etc. A series of validation tests were conducted and the results demonstrated the robustness of this measurement method and implies the possibility to broaden the application scope of monocular cameras in the field of autonomous driving and intelligent transportation systems.

Localization and navigation are fundamental issues to autonomous mobile robotics. In the case of the environmental map has been built, the traditional two-dimensional (2D) lidar localization and navigation system can't be matched to the initial position of the robot in dynamic environment and will be unreliable when kidnapping occurs. Moreover, it relies on high-cost lidar for high accuracy and long range. In view of this, the paper presents a low cost navigation system based on a low cost lidar and a cheap webcam. In this approach, 2D-codes are attached to the ceiling, to provide reference points to aid the indoor robot localization. The mobile robot is equipped with webcam pointing to the ceiling to identify 2D-codes. On the other hand, a low-cost 2D laser scanner is applied to build a map in unknown environment and detect obstacles. Adaptive Monte Carlo Localization (AMCL) is implements for lidar positioning, A* and Dynamic Window Approach (DWA) are applied in path planning based on a 2D grid map. The error analysis and experiments has validated the proposed method.