# Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
###### Abstract
They obtained rms errors of 0.0171 \(m/s\) for the forward velocity \(v_{x}\), 0.0155 \(m/s\) for the lateral velocity \(v_{y}\), and 0.0156 \(rad/s\) for the yaw angular velocity \(w\). [PERSON] et al. [11] presented a multi-layer perceptron (MLP) with a BP algorithm to control the movements of a mobile robot that must generate a path and navigate avoiding obstacles using ultrasonic sensors. An Arduino embedded platform was used to implement the neural control, and the adaptability of the robot was tested with a group of people that acted as the dynamic obstacles. The authors concluded that NNs are great tools applicable in mobile robots evading obstacles since they can work with imprecise information. [PERSON] and [PERSON] [9] used Q-learning (RL algorithm) and a feedback neural network to generate a collision-free trajectory for an autonomous robotic platform (PowerBot) within an uncertain workspace containing stationary and mobile entities. The network received as input the initial position of the mobile robot, the time sample, and the matrix of Q-values, while the output was a three-element vector that held the Cartesian values and the time. The weights of the neural network were updated after each step, using the adapt function, which received as an input the coordinates of the goal. The authors suggested that to minimize computation time the algorithm implementing the trajectory planner can be run in an embedded system. [PERSON] et al. [1] presented a dynamic path planning method based on a gated recurrent unit-recurrent neural network model for the problem of the path planning of a mobile robot in an unknown environment where inputs were derived from sample sets generated by an APF and an ant colony optimization algorithm (ACO). The input of the network was a dataset obtained by the laser sensor collecting surrounding environmental information, while the output was the velocity and the angle of the mobile robot obtained from the ACO algorithm and APF. In addition, the learning model was used to predict the control output angle at the next instant. The authors demonstrated with simulation and physical experiments that the network model could generate a reasonable trajectory in an unknown environment. On the other hand, the training network needed samples generated by the teacher system, and sometimes it was impossible to reach the target point accurately. Solutions involving LSTM neural networks have recently been presented. [PERSON] et al. (1998) proposed a novel robot path planning method that combines the Rapidly Exploring Random Tree (RRT) and LSTM network where numerous paths are generated in the robot configuration space by the RRT method, and then a convolutional autoencoder and LSTM combination network is trained by them. The path generation ability of the trained network was investigated against the starting and goal points in an unknown environment with different static obstacle arrangements. The simulation results confirmed that the proposed network achieves high learning and generalization abilities. The authors [PERSON] and [PERSON] (2018) developed a method that can complete motion planning for a service mobile robot in one step using a convolutional LSTM network. The input of the network is a GRB picture with obstacles, target position, and starting position, while the output of the network is the linear and angular velocity of the robot. The convolution layer of the network serves to mark obstacles, target position and starting position; the LSTM layer describes the temporal characteristics of the movement; and the fully connected layer is used to smoothly adjust the linear and angular speed of the service mobile robot. The proposed method showed a good fault tolerance and was able to complete motion planning tasks in real time.