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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 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.

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.