Function Analysis of Industrial Robot under Cubic Polynomial Interpolation in Animation Simulation Environment

In order to study the effect of cubic polynomial interpolation in the trajectory planning of polishing robot manipulator, firstly, the articular robot operating arm is taken as the research object, and the overall system of polishing robot operating arm with 7 degrees of freedom is constructed. Then through the transformation of space motion and pose coordinate system, Denavit-Hartenberg (D-H) Matrix is introduced to describe the coordinate direction and parameters of the adjacent connecting rod of the polishing robot, and the kinematic model of the robot is built, and the coordinate direction and parameters of its adjacent link are described. A multi-body Dynamic simulation software, Automatic Dynamic Analysis of Mechanical Systems (ADAMS), is used to analyze the kinematic simulation of the robot operating arm system. Finally, the trajectory of the robot manipulator is planned based on the cubic polynomial difference method, and the simulation is verified by Matrix Laboratory (MATLAB). Through calculation, it is found that the kinematic model of polishing robot operating arm constructed in this study is in line with the reality; ADAMS software is used to generate curves of the rotation angles of different joint axes and the displacement of end parts of the polishing robot operating arm changing with time. After obtaining relevant parameters, they are put into the kinematic equation constructed in this study, and the calculated position coordinates are consistent with the detection results; moreover, the polishing robot constructed in this study can realize the functions of deburring, polishing, trimming, and turning table. MATLAB software is used to generate the simulation of the movement trajectory of the polishing robot operating arm, which can show the change curve of angle and angular velocity. The difference between the angle at which the polishing robot reaches the polishing position, the change curve of angular velocity, and the time spent before and after the path optimization is compared. It is found that after path optimization based on cubic polynomial, the change curve of the polishing robot's angle and angular velocity is smoother, and the time is shortened by 17.21s. It indicates that the cubic polynomial interpolation method can realize the trajectory planning of the polishing robot operating arm, moreover, the optimized polishing robot has a continuous and smooth trajectory, which can improve the working efficiency of the robot

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption (TEC) estimation model for the electric bus (EB) fleet planning, operation, and life-cycle assessment. Leveraging the vast variations of temperature in Jilin Province, China, real-world data of 31 ​EBs operating in 14 months were collected with temperatures fluctuating from −27.0 ​to 35.0 ​\ub0C. TEC of an EB was divided into two parts, which are the energy required by the traction and battery thermal management system, and the energy required by the air conditioner (AC) system operation, respectively. The former was regressed by a logarithmic linear model with ambient temperature, curb weight, travel distance, and trip travel time as contributing factors. The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square. The latter was estimated by the operation time of the AC system in cooling mode or heating mode. Model evaluation and sensitivity analysis were conducted. The results show that: (i) the mean absolute percentage error (MAPE) of the proposed model is 12.108%; (ii) the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling; (iii) the MAPE has a 1.746% reduction if considering passengers’ boarding and alighting

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption. First, a model for estimating the trip energy consumption is proposed based on field-collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs

A new approach for reducing urban transport energy

Worldwide, 50% of the population already live in cities, and this percentage is expected to rise. Cities account for an estimated 70% of both energy use and fossil fuel CO2 emissions, and urban passenger travel forms a significant share of this total. This paper introduces a novel approach for reducing both the energy and resulting carbon emissions from such urban travel, in the form of a personal transport energy quota, using a unique cloud technology based intelligent navigation system. The approach has some similarities to the Personal Carbon Trading scheme proposed for the UK some years ago, but is for personal transport only. Like carbon taxes, which have now been introduced in a number of countries/regions, this UK scheme aimed at reducing carbon emissions. The approach proposed in this paper would grant a monthly transport energy quota to all residents. A mobile phone-based application would provide the user with details on the energy costs of each trip, the remaining energy quota at any time, and suggest alternative travel options to minimise trip energy use

Entanglement Routing over Quantum Networks Using Greenberger-Horne-Zeilinger Measurements

Generating a long-distance quantum entanglement is one of the most essential functions of a quantum network to support quantum communication and computing applications. The successful entanglement rate during a probabilistic entanglement process decreases dramatically with distance, and swapping is a widely-applied quantum technique to address this issue. Most existing entanglement routing protocols use a classic entanglement-swapping method based on Bell State measurements that can only fuse two successful entanglement links. This paper appeals to a more general and efficient swapping method, namely n-fusion based on Greenberger-Horne-Zeilinger measurements that can fuse n successful entanglement links, to maximize the entanglement rate for multiple quantum-user pairs over a quantum network. We propose efficient entanglement routing algorithms that utilize the properties of n-fusion for quantum networks with general topologies. Evaluation results highlight that our proposed algorithm under n-fusion can greatly improve the network performance compared with existing ones

SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving

To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photo-realistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-world foggy images

MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding

Given an untrimmed video and natural language query, video sentence grounding aims to localize the target temporal moment in the video. Existing methods mainly tackle this task by matching and aligning semantics of the descriptive sentence and video segments on a single temporal resolution, while neglecting the temporal consistency of video content in different resolutions. In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is an encoder-decoder network, and output features in the decoder part are in conjunction with Transformers to predict the final start and end timestamps. Particularly, our MRT module is hot-pluggable, which means it can be seamlessly incorporated into any anchor-free models. Besides, we utilize a hybrid loss to supervise cross-modal features in MRT module for more accurate grounding in three scales: frame-level, clip-level and sequence-level. Extensive experiments on three prevalent datasets have shown the effectiveness of MRTNet.Comment: work in progres