A Smooth Path between the Classical Realm and the Quantum Realm

A simple example of classical physics may be defined as classical variables, p and q, and quantum physics may be defined as quantum operators, P and Q. The classical world of p&q, as it is currently understood, is truly disconnected from the quantum world, as it is currently understood. The process of quantization, for which there are several procedures, aims to promote a classical issue into a related quantum issue. In order to retain their physical connection, it becomes critical as to how to promote specific classical variables to associated specific quantum variables. This paper, which also serves as a review paper, leads the reader toward specific, but natural, procedures that promise to ensure that the classical and quantum choices are guaranteed a proper physical connection. Moreover, parallel procedures for fields, and even gravity, that connect classical and quantum physical regimes, will be introduced.

Parking Space Detection and Path Planning Based on VIDAR

The existing automatic parking algorithms often neglect the unknown obstacles in the parking environment, which causes a hidden danger to the safety of the automatic parking system. Therefore, this paper proposes parking space detection and path planning based on the VIDAR method (vision-IMU-based detection and range method) to solve the problem. In the parking space detection stage, the generalized obstacles are detected based on VIDAR to determine the obstacle areas, and then parking lines are detected by the Hough transform to determine the empty parking space. Compared with the parking detection method based on YOLO v5, the experimental results demonstrate that the proposed method has higher accuracy in complex parking environments with unknown obstacles. In the path planning stage, the path optimization algorithm of the A ∗ algorithm combined with the Bezier curve is used to generate smooth curves, and the environmental information is updated in real time based on VIDAR. The simulation results show that the method can make the vehicle efficiently avoid the obstacles and generate a smooth path in a dynamic parking environment, which can well meet the safety and stationarity of the parking requirements.

Efficient and Safe Robotic Autonomous Environment Exploration Using Integrated Frontier Detection and Multiple Path Evaluation

Autonomous exploration and remote sensing using robots have gained increasing attention in recent years and aims to maximize information collection regarding the external world without human intervention. However, incomplete frontier detection, an inability to eliminate inefficient frontiers, and incomplete evaluation limit further improvements in autonomous exploration efficiency. This article provides a systematic solution for ground mobile robot exploration with high efficiency. Firstly, an integrated frontier detection and maintenance method is proposed, which incrementally discovers potential frontiers and achieves incremental maintenance of the safe and informative frontiers by updating the distance map locally. Secondly, we propose a novel multiple paths planning method to generate multiple paths from the robot position to the unexplored frontiers. Then, we use the proposed utility function to select the optimal path and improve its smoothness using an iterative optimization strategy. Ultimately, the model predictive control (MPC) method is applied to track the smooth path. Simulation experiments on typical environments demonstrate that compared with the benchmark methods, the proposed method reduce the path length by 27.07% and the exploration time by 27.09% on average. The real-world experimental results also reveal that our proposed method can achieve complete mapping with fewer repetitive paths.

Multi UAV Coverage Path Planning in Urban Environments

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints , calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.

Research on the Path Planning Algorithm of a Manipulator Based on GMM/GMR-MPRM

Autonomous, flexible, and human–robot collaboration are the key features of the next-generation robot. Such unstructured and dynamic environments bring great challenges in online adaptive path planning. The robots have to avoid dynamic obstacles and follow the original task path as much as possible. A robust and efficient online path planning method is required accordingly. A method based on the Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and the Probabilistic Roadmap (PRM) is proposed to overcome the above difficulties. During the offline stage, the GMM was used to model teaching data, and it can represent the offline-demonstrated motion and constraints. The optimal solution was encoded in the mean value, while the environmental constraints were encoded in the variance value. The GMR generated a smooth path with variance as the resample space according to the GMM of the teaching data. This representation isolated the old environment model with the novel obstacle. During the online stage, a Modified Probabilistic Roadmap (MPRM) was used to plan the motion locally. Because the GMM provides the distribution of all the feasible motion, the sampling space of the MPRM was generated by the variable density resampling method, and then, the roadmap was constructed according to the Euclidean and Probability Distance (EPD). The Dijkstra algorithm was used to search for the feasible path between the starting point and the target point. Finally, shortcut pruning and B-spline interpolation were used to generate a smooth path. During the simulation experiment, two obstacles were added to the recurrent scene to indicate the difference from the teaching scene, and the GMM/GMR-MPRM algorithm was used for path planning. The result showed that it can still plan a feasible path when the recurrent scene is not the same as the teaching scene. Finally, the effectiveness of the algorithm was verified on the IRB1200 robot experiment platform.