A New Robot Path Planning Method Based on LSTM Neural Network and Rapidly-Exploring Random Tree Algorithm

As an important field of robot research, robot path planning has been studied extensively in the past decades. A series of path planning methods have been proposed, such as A* algorithm, Rapidly-exploring Random Tree (RRT), Probabilistic Roadmaps (PRM). Although various robot path planning algorithms have been proposed, the existing ones are suffering the high computational cost and low path quality, due to numerous collision detection and exhausting exploration of the free space. In addition, few robot path planning methods can automatically and efficiently generate path for a new environment. In order to address these challenges, this paper presents a new path planning algorithm based on the long-short term memory (LSTM) neural network and traditional RRT. The LSTM-RRT algorithm first creates 2D and 3D environments and uses the traditional RRT algorithm to generate the robot path information, then uses the path information and environmental information to train the LSTM neural network. The trained network is able to promptly generate new path for randomly generated new environment. In addition, the length of the generated path is further reduced by geometric relationships. Hence, the proposed LSTM-RRT algorithm overcomes the shortcomings of the slow path generation and the low path quality using the traditional RRT method.

Strategies for Speeding Up Manipulator Path Planning to Find High Quality Paths in Cluttered Environments

In many manufacturing applications, robotic manipulators need to operate in cluttered environments. Quickly finding high-quality paths is very important in applications that require high part fix and frequent setup changes. This paper presents a point-to-point path planning framework for manipulators operating in cluttered environments. It uses a bi-directional tree-search to find path and facilitates finding a balance between path quality and planning time. The framework dynamically switches between search strategies based on the search progress to produce high-quality paths quickly. This paper three main contributions. First, we extend a previously developed sampling-based modular tree-search. Specifically, we present a strategy that can sample effectively in challenging regions of the search-space by using local approximations of the configuration space. Second, we add new strategies and scheduling logic that decreases the failure rate as well as the planning time compared to the prior work. We also present an inter-tree connection strategy that adapts to collision information gathered over time. We introduce a scheduling rule that regulates the exploitation of focusing hints derived from the workspace obstacles. Third, we present theoretical reasoning behind strategy switching and how it can help decrease planning times and increase path quality. Together, these new features the reduce average failure rate by a factor of 4 and improve the average planning time by 22% over the previous work.

Multipath TCP-Based IoT Communication Evaluation: From the Perspective of Multipath Management with Machine Learning

With the development of wireless networking technology, current Internet-of-Things (IoT) devices are equipped with multiple network access interfaces. Multipath TCP (MPTCP) technology can improve the throughput of data transmission. However, traditional MPTCP path management may cause problems such as data confusion and even buffer blockage, which severely reduces transmission performance. This research introduces machine learning algorithms into MPTCP path management, and proposes an automatic learning selection path mechanism based on MPTCP (ALPS-MPTCP), which can adaptively select some high-quality paths and transmit data at the same time. This paper designs a simulation experiment that compares the performance of four machine learning algorithms in judging path quality. The experimental results show that, considering the running time and accuracy, the random forest algorithm has the best performance in judging path quality.

Analysis of research on the quality of cycling service

The existing research methods and criteria for assessing the efficiency and quality of cycling services are analyzed. Keywords: cycling, bicycle path, quality of transport service, efficiency.