Collision avoidance and path planning problems have been investigated and solved using many techniques, such as RRT, A, A*, RRT*, and decision trees [11,12,13,14]. These techniques are mostly suitable for applications where the environment state is known and not changing. In a bid to offer a better collision avoidance solution, researchers introduced map localization and position-based methods. In map localization and positioning, cameras are used to capture the state of the environment and detect obstacles and their sizes to determine the path to follow [15]. This usually follows the process of mapping, localization, and planning. In this scenario, as with A, RRT, and related approaches, the environment needs to be known beforehand to design a policy for path planning; hence, it is not best suited for a dynamic environment [16,17,18]. Furthermore, the environment used to develop the model can change over time, and maintaining and updating the models ensures that it can adapt to changes and continue to navigate safely, which is costly, time-consuming, and requires knowledge and experience.

In recent years, the use of reinforcement learning and DRL has increased significantly due to its excellent environmental awareness and decision control performance [19]. The ATAri 2600 [20] and AlphaGo [21] developed by DeepMind are two of the early success stories of RL. In mobile robots, the use of RL/DRL to directly map the state of the environment to the control signal for a dynamic path planning solution remains a challenge. Kasun et al. [22] investigated robot navigation in an uneven outdoor environment using a fully trained DRL network to obtain a cost map to perform the navigation task. The network has prior knowledge of the environment by accepting elevation maps of the environment, the robot poses, and the goal axis as input. Xue et al. [23] and Ruan et al. [24] investigated the use of a double-deep Q-network (DDQN). The size and position of the obstacle and the target position are taken as input to the network, and the robot’s velocity values are output. In [25], a deep deterministic policy gradient (DDPG) algorithm was used to select a control policy for hybrid unmanned aerial underwater vehicles using the robot’s state, LiDAR measurements, and distance to the goal point. A goal-orientated approach to obstacle avoidance was implemented by [26,27]. Their work was based on processing a large amount of depth image information using DRL to reach its goal while avoiding obstacles in a continuous or unknown environment. In another work, Choi et al. [28] proposed the integration of both path planning and reinforcement learning methods to predict the next movement of an obstacle using the calculated distance from the LiDAR information. Wang et al. [29] implemented the curriculum learning of a DRL robot to navigate among movable obstacles. Rather than collecting human demonstrations as in [30], they introduced the use of prior knowledge.

Most collision avoidance models developed using DRL obtain the state of the environment through LiDAR information. When training the learning network, many researchers have used different FOVs (90°, 180°, 270°, or 360°), and the number of LiDAR beams (10–60) has been used by many researchers, which directly impacts the computational complexity of the network [31,32]. Tai et al. [33] developed a navigation learning model in a simulated environment using a 10-dimensional laser beam as one input to the model. Han et al. [34] used the fusion of RGB images from a camera and 2D LiDAR sensor data as input to a DRL network of self-state attention to investigate the effect of using 2D LiDAR on a tall robot. In their work, the training environment is captured and processed before passing it to the training network. Xie et al. [35] applied a proportional-integral-derivative (PID) controller to improve the training rate of a convolutional neural network that takes 512 stacked laser beams as input. In [36], a reinforcement learning method is developed that automatically learns the best number of beams required based on the application. Their work was tested on object detection and shows that the appropriate beam configuration improves the performance of the LiDAR application. Zhang et al. [37] developed a neural network for safe navigation based on different LiDAR sensor configurations (FOV, number of LiDAR sensors mounted and LiDAR orientation). Their work shows that the models with a LiDAR sensor with an FOV of 240° in all scenarios perform better than all other FOVs used. Another work by [38] chooses an FOV with a minimum angle of 13.4° and a maximum angle of 11.5° to train a DRL model for robot navigation. Their approach was able to navigate safely with the limited FOV. Jinyoung et al. [39] investigated the performance of a narrow FOV LiDAR in robot navigation. They developed a navigation model using long-short-term Memory (LSTM), a type of recurrent neural network with a local-map critic (LMC). However, these researchers did not provide details of the criteria used in the selection of these FOVs.

A LiDAR sensor emits light beams to its surroundings, which in turn bounce off surrounding objects back to the sensor. The beam that takes the shortest time to return to the sensor is used to calculate the shortest distance to an impending obstacle, which is further used to control the robot velocity values while training a neural network during navigation. Therefore, it is important to investigate the effect of the LiDAR sensor configuration required to train a DRL based on the required application.