Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via back propagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

The active scene flow estimation technology aims at higher quality of scene flow data through actively changing the ego-vehicle’s trajectory as well as sensor observation position. The major challenges for active 3D point cloud scene flow estimation for autonomous driving (AD) include mis-registrations of point cloud frames, inaccurate scene prediction, and inefficient scene similarity evaluation. This paper presents a framework of active scene flow estimation for AD applications, which consists of three main modules: robust scene flow estimation, reliable reachable area detection, and efficient observation position decision. The novelty of this work is threefold: 1) developing a robust bi-direction attention-based mechanism neural network scene flow estimation method; 2) developing a reliable reachable area detection strategy through hidden points removing (HPR) based scene prediction and a legality checking scheme between the ego-vehicle and the road as well as the predicted scene; 3) developing an efficient observation position decision strategy through building a scene similarity measure, which can help evaluate the differences between two frames of point clouds from different views. The proposed method is trained and verified using a CARLA simulator based dataset, the FlyingThings3D and KiTTI datasets, which have the accurate scene flow ground-truth. The experiment results demonstrate the superior estimation performance and generalization capacity of our method for various AD scenes as well as different system configurations, compared with other state-of-the-art (SOTA) methods.

Spatial-temporal graph learning has emerged as the state-of-the-art solution for modeling structured spatial-temporal data in learning region representations for various urban sensing tasks (e.g., crime forecasting, traffic flow prediction). However, most existing models are vulnerable to the quality of the generated region graph due to the inartistic graph-structured information aggregation schema. The ubiquitous spatial-temporal data noise and incompleteness in real-life scenarios bring difficulties to generate high-quality region representations. In this paper, we propose a Spatial-Temporal Adversarial Graph contrastive learning model (STAG) to tackle this challenge for adaptive self-supervised graph augmentation. Specifically, we propose a learnable contrastive learning function that enables the automated distillation of important multi-view self-supervised signals for adaptive spatial-temporal graph augmentation. To enhance the representation discrimination ability and robustness, the designed adversarial contrastive learning mechanism empowers STAG to adaptively identify hard samples for better self-supervision. Finally, a cross-view contrastive learning paradigm is introduced to model the inter-dependencies across view-specific region representations and preserve the underlying relation heterogeneity. We verify the superiority of our STAG method in various spatial-temporal prediction tasks on several benchmark datasets.

The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies. This paper presents a novel adaptive environment model based collision avoidance reinforcement learning (i.e., AEMCARL) framework for an unmanned robot to achieve collision-free motions in challenging navigation scenarios. The novelty of this work is threefold: (1) developing a hierarchical network of gated-recurrent-unit (GRU) for environment modeling; (2) developing an adaptive perception mechanism with an attention module; (3) developing an adaptive reward function for the reinforcement learning (RL) framework to jointly train the environment model, perception function and motion planning policy. The proposed method is tested with the Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various crowded scenes. Both simulation and experimental results have demonstrated the superior performance of the proposed method over baseline methods.

The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.

The challenges of developing low-cost, large-scale multi-robot navigation systems include noisy measurements, a large number of robots, and computing efficiency for collision avoidance. This paper presents a distributed motion planning framework for a large number of robots to navigate with robust collision avoidance using low-cost range only measurements. The novelty of this work is threefold. (1) Developing a distributed collision-free navigation system for a large-scale robot group in which each robot performs motion planning based on the noisy range measurements of neighboring robots; (2) Developing a set of algorithms for each robot to accurately estimate the relative positions and orientations based on the range measurements and relative velocities; (3) Developing a velocity obstacle (VO) based motion planning algorithm for each robot which can take into account of the estimation uncertainties in relative positions and orientations. The proposed approach is tested with various numbers of differential-driven robots in the Gazebo simulator and real-world experiments. Both simulation and experiment results validate the superior performance of the proposed approach compared to other state-of-art technologies.

To improve the structure and output characteristics of current 3-DOF PM motors, a type of hybrid drive type permanent magnet 3-DOF motor is researched, which can realize the 3-DOF motion. The spin rotation torque characteristics are further optimized by improving the magnetic field distribution. Based on the introduction of the structure and operation principle of the motor, the magnetic field are modeled and analyzed with the analytic method and finite element method. The results show that the calculating results of analytic method and finite element method are identical, which proves the effectiveness of the analytical models and provides the references for further design and optimization of this kind of motors.

Aiming at the characteristics of Senegal type wind turbine, a new type of wind wheel with horizontal structure of wind turbine is studied combined with the characteristics of vertical axis wind turbine and horizontal axis wind turbine. The structure of turbine and the distribution of the surrounding flow field are studied by using ANSYS Workbench software. Simultaneously, for generators installed on the turbine, a permanent magnet synchronous generator with single-layer winding used for distributed energy system which can realize deflection operation is proposed. The size of the permanent magnet generator and material are determined by calculation and analysis. Based on aerodynamics principle and fluid dynamics software, the flow field characteristics of wind turbine are simulated. The force of the wind turbine is analyzed in the presence or absence of obstructions in the wind field. A new structure of parallel arrangement of two rows of machines with different heights is proposed. In this paper, the flow field around the wind turbine, especially the wake at the downstream is researched and simulated. The result shows that the new turbine structure can improve the utilization of wind energy and make full use of wind energy. The feasibility of this structure is verified with simulation, which provides the further optimization basis for the wind farm units.