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Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method.

This study focuses on the multiphase flow properties of crowd motions. Stability is a crucial forewarning factor for the crowd. To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd, a novel motion structure analysis model is established based on purposiveness, and is used to describe the continuity of pedestrians’ pursuing their own goals. We represent the crowd with self-driven particles using a destination-driven analysis method. These self-driven particles are trackable feature points detected from human bodies. Then we use trajectories to calculate these self-driven particles’ purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd. Finally, we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability. Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds, and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors. Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.

This paper presents the TransBoat, a novel omnidirectional unmanned surface vehicle (USV) with a magnetbased docking system for overwater construction with wave disturbances. This is the first such USV that can build overwater structures by transporting modules. The TransBoat incorporates two features designed to reject wave disturbances. First, the TransBoat's expandable body structure can actively transform from a mono-hull into a multi-hull for stabilization in turbulent environments by extending its four outrigger hulls. Second, a real-time nonlinear model predictive control (NMPC) scheme is proposed for all shapes of the TransBoat to enhance its maneuverability and resist disturbance to its movement, based on a nonlinear dynamic model. An experimental approach is proposed to identify the parameters of the dynamic model, and a subsequent trajectory tracking test validates the dynamics, NMPC controller and system mobility. Further, docking experiments identify improved performance in the expanded form of the TransBoat compared with the contracted form, including an increased success rate (of ~ 10%) and reduced docking time (of ~ 40 s on average). Finally, a bridge construction test verifies our system design and the NMPC control method.

This paper provides a new scheme to improve the sailing distance and working time of the autonomous sailboat. The main part of the article introduces a new design of sail tug which can carry more batteries that provides more power for control system, enabling sailboats to sail longer in both time and distance. The modified sail tug was tested at an advanced test site equipped with high precision infrared motion capture system. The air curtain machines in test site can produce constant wind field, to drive sailboat steadily. Comparative experiments have been conducted to show the advantages of self-supporting ability of autonomous sail tug over ordinary autonomous sailboat.

Wing sail land-yacht is one of the promising vehicles which utilizes wind energy. However, its autonomous driving in all directions is challenging, especially the upwind steering (tacking) maneuver. This paper proposes a novel design of low cost three-wheeled land-yacht with a T-frame and a foamed wing sail, which is able to move and tack upwind owing to the lightweight design. To achieve the successful tacking, a model describing the steering process is developed based on the law of energy conservation and used to predict the minimum initial velocity for steering. In the model, an acceleration error function C induced by the environment disturbance and manufacturing error is identified by a series of experiments. Finally, the tacking experiments verify the steering model and show that the land-yacht can achieve the tacking with a high success rate of 94.7%, based on the predicted minimum initial steering velocity.

The ship wake affects the motion of Unmanned Surface Vehicles (USVs) essentially. While USV simulators have been proposed recently, they do not support the ship wake. In this paper, a computational efficient simulation of ship wake is proposed. The simulation is based on influence-point model. In some portion of the wake, this model is inaccurate. Extra processes are applied to pre-manipulate the model to ensure its accuracy. The simulation can be applied to a general scenario, especially when the ship velocity varies with time. The results show that the wake is accurate, and the computational complexity is low. The simulated wake has been equipped into a USV simulator, providing a more realistic environment disturbance.

To perform high endurance ocean cruise mission, energy is of paramount importance for Autonomous Surface Vehicles (ASVs). Autonomous sailboats, as a new type of ASV, can provide an energy-saving solution. However, conventional autonomous sailboats have low mobility in complex marine environment. Hybrid sailboat, which is equipped with an auxiliary electric propulsion system, can solve both energy and mobility problems. To achieve ocean cruise mission, the energy consumption of hybrid sailboat needs to be optimized. In this paper, the control method of the hybrid system is redesigned to address this problem. Moreover, to solve the problem of sideways drift when sailing upwind, the course stability of hybrid sailboat needs to be enhanced. This paper presents an efficient design optimization to reduce leeway angle. Notably, the leeway angle can be reduced by 55.4% and about 58.9% of energy is saved on average. Our optimization strategy can enable hybrid sailboat to sail more stable and longer.

This paper proposes an interactive system for the non-musician learners to get inspired from a song. Differing from complex models of deep learning or simple Markov models sparse of music inter-features, in this research, we unify the composing of a song in a general architecture with music theory, and thus provide a much more understandable view of the music generation for non-musician learners. The proposed model focuses on extracting the extant feature from a target song and recreating different phrases with the representing probabilistic graph underlying the target song based on the relationship among notes in a phrase. Furthermore, an interactive interface between the users and the proposed system is built with a tunable parameter for them to be involved in the music generation and creating procedure. This procedure provides practical experience in aiding the non-musicians to understand and learn from composing a song. Approximately 700 samples of preferences questionnaire survey about the generated music and original music and more than 3000 samples for interactive preferences voting for the tunable parameter have been collected. Quantities of experiments have proved the validation of the proposed system.

This paper proposes a methodology to improve the robot localization accuracy with only range measurement data for wireless sensors, e.g. Wi-Fi, Bluetooth, mobile network base station, ultra-wide band (UWB) tag, or even cameras and lidar with landmark range data. Accordingly, we modeled the robot localization as an optimization problem by using only the range data. Intuitively, this is a nonconvex problem which arises the difficulty of getting the optimal solution. Therefore we further reformulate it as a convex problem and the global optimal solution is guaranteed. Furthermore, the comparison experiments of a robot implementing a traditional Kalman filter using smoothing data as well as our proposed method have been done with the motion capture system benchmark. Both the simulation and field experiments validate the robustness and accuracy of our proposed method. The quantitative evaluation of the localization accuracy increases by 61.29% in simulation and 39.03% in field experiment to the best within a workspace of 4m ×4m in the scenarios.

Obstacle avoidance is crucial for autonomous surface vehicles (ASVs) in the sea because rescue is extremely difficult there. OceanVoy, a sailboat toward long range energy-saving voyage, has to overcome the dual challenges, i.e. from the environmental interference and its low mobility preventing from precise obstacle avoidance. We propose a control scheme based on real-time collision risk assessment and a hybrid propulsion system to enhance safety of OceanVoy. A novel sailboat safety zone (SSZ) has been designed to warn the potential collision during its sailing. Both intrinsic characteristics of OceanVoy and environmental factors have been considered in SSZ. We use lateral and axial thrusters to provide emergency propulsion. A collision avoidance algorithm is executed to coordinate motors in rudder, sail and thrusters based on SSZ. Both simulation and experiments have been conducted and the results have validated our system and collision avoidance scheme.

Self-assembly has attracted growing interests in modular robotics during past decades. Recent work accelerates the assembly process by parallelizing the docking actions among robots. However, these methods can only apply to ideal environments without obstacles. Otherwise, robots will get trapped during the assembly process, due to the complex scenes with obstacles. This paper presents an efficient parallel assembly planning algorithm for modular robots by taking the surrounding obstacles into consideration. By this algorithm, the docking actions are able to avoid immovable obstacles, and therefore parallel self-assembly of robots can adapt to complex environments. To validate the efficacy and generality, the authors have implemented this algorithm in a grid-world simulation environment with 25 distinct maps. The simulation results show a much higher success rate (more than 80%) of our proposed algorithm compared with the existing parallel self-assembly planning algorithms. Finally, the feasibility of the algorithm is affirmed by a self-assembly experiment on the automated guided vehicles (AGVs).

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.

The coronavirus disease 2019 (COVID-19) outbreak has increased mortality and morbidity world-wide. Oropharyngeal swabbing is a well-known and commonly used sampling technique for COVID-19 diagnose around the world. We developed a robot to assist with COVID-19 oropharyngeal swabbing to prevent frontline clinical staff from being infected. The robot integrates a UR5 manipulator, rigid-flexible coupling (RFC) manipulator, force-sensing and control subsystem, visual subsystem and haptic device. The robot has strength in intrinsically safe and high repeat positioning accuracy. In addition, we also achieve one-dimensional constant force control in the automatic scheme (AS). Compared with the rigid sampling robot, the developed robot can perform the oropharyngeal swabbing procedure more safely and gently, reducing risk. Alternatively, a novel robot control schemes called collaborative manipulation scheme (CMS) which combines a automatic phase and teleoperation phase is proposed. At last, comparative experiments of three schemes were conducted, including CMS, AS, and teleoperation scheme (TS). The experimental results shows that CMS obtained the highest score according to the evaluation equation. CMS has the excellent performance in quality, experience and adaption. Therefore, the proposal of CMS is meaningful which is more suitable for robot-sampling.

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.

Wind-driven vehicle is a kind of promising way to utilize wind energy due to its non-pollution feature. This paper presents a practical design of the wind-driven vehicle in the form of land-yacht, which is equipped with a wing sail and a micro-controller unit. Theoretical analysis and simulation were performed to find the total force coefficient corresponding to each combination of attack angle and heading angle, and the best attack angle corresponding to each different heading angle. A prototype was built and tested in a wind-field environment. The simulation and experiment were concordant, both of which showed the relation between the movement of land-yacht and these angles when moving upwind well.

Obstacle avoidance of autonomous sailboats is a complicated task due to big inertia, highly nonlinear motion of sailboat and uncertain disturbance from wind and water current. To deal with the obstacle avoidance problem of autonomous sailboats, we promote a novel method based on reinforcement learning with coarse-to-fine strategy. In this strategy, coarse stage is used to detect the autonomous sailboat roughly in the test environment. Then fine stage is applied to localize the autonomous sailboat accurately. Hereby, the avoidance performance is improved by the transition from coarse to fine. We have verified our algorithms both in simulation and real experiments. With our method, the sailboat avoids the obstacle in higher precision than the method without the coarse-to-fine strategy. The final success rate of obstacle avoidance is near 83% and the rate of reaching goal is 70%. Both simulation results and experiments show that our method is effective.

Autonomous sailboats are potential solutions for in-situ ocean measurement thanks to their high energy efficiency. Indeed, the ability for them to maintain their position is crucial for ocean data collection. Previous study has demonstrated a position keeping method for an autonomous sailboat and has theoretically proved the stability. As this method is ineffective against wind disturbance, we propose a more feasible algorithm for real environment in this paper. A retrofitted model sailboat is developed to evaluate the performance of our approach in an artificial wind field. Simulation and experiment results under several different cases show that our proposed algorithm is robust.

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.

This paper describes a low-cost, wide-range and multi-functional vision backend for Sailboat Research Testbed (SRT). SRTs are of great significance in autonomous sailing vessels (ASVs) research. The motivation behind this vision backend is the demand for various functions of an SRT, such as sailboat localization and control, yet the cost of building such a testbed should be economic. Hence, cost-effective hardware along with robust and lightweight vision algorithms are integrated into a vision backend to assist our SRT. This vision backend overcomes several inherent problems when applying some CV algorithms in the water environment, and presents multiple functionalities with high performance. This paper details our solutions and design of this vision backend. Moreover, a variety of schemes are introduced to reduce the cost when building SRTs.

Autonomous sailboat as an energy saving robotic vessel driven by wind, is becoming an emerging trend in ocean sampling, surveillance and management. Hereby, collision avoidance for autonomous sailboats appears to be an insurmountable problem due to its restricted manoeuvrability. In this study, we propose a method to realize the collision avoidance for autonomous sailboats under the Racing Rule of Sailing (RRS). Using the safety circles concept, the speed and heading information of the autonomous sailboats are used to predict and determine the collision risk. Then the correct RRS rule is matched according to the collision situation when the Closest Point of Approach (CPA) of autonomous sailboats is less than zero. And collision avoidance response is planned and implemented accordingly. The proposed method was implemented and verified in experiments. Four different sailing cases were discussed and demonstrated with successful collision avoidance for autonomous sailboats.

Wind perception is crucial for autonomous sailboat which has features of long distance operation. Previously, anemometers based on thermal dynamics, acoustics or cup-vane anemometer have already been widely used, but they are generally large in size. This paper starts with vibration mechanics to develop a new kind of anemometer which is particularly suitable for mini-sized autonomous sailboats. An IMU which is fixed to one end of a non-rigid body, such as spring. The non-rigid body will vibrate if it is exposed to wind field. Physical quantities related to vibration can reflect different wind speeds. We modeled the new design and developed a prototype with a low cost. Experiments are conducted to collect data. Based on curve fitting of the experiment data, the wind speed is estimated with a mean error at 0.35 m/s for measuring range from 0 to 10.7 m/s.

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.

The challenges in transforming paintings into music is well-known, since the relationship between two kinds of art is unclear. Different composers write different music when the same painting is presented to them. In this paper, a cross-mordality model has been proposed for transforming images into multitrack music based on the framework of deep convolutional generative adversarial networks (DCGANs). The proposed model is trained on a classical music dataset and a dataset of impressionist paintings. The model can be applied to transfer impressionist paintings into classical music with two tracks. By using music evaluation methods, the harmonicity of the generated music can be confirmed. Our model is the first attempt of our knowledge at transforming paintings into music segments.