###### Abstract
An optimal local path planning approach should enable robots to adaptably deal with their environments, such as assisting in avoiding dynamic obstacles or identifying traversable routes through varying terrain conditions. In unstructured environments, classical path planning concepts such as Rapidly exploring Random Tree (RRT) variants, Batch Informed Tree (BIT), D* algorithm variants, artificial potential field methods, A* algorithm variants and learning-based path planning methods have been used [27]. Over the past years, deep learning has been used to enhance the performance of sensor fusion [28], multimodal scene understanding [29], and robot local path planning [30] techniques. For autonomous navigation, a robot must have the ability to understand both its pose and enough about the external environment to determine an optimal and traversable path to safely reach a goal position without human assistance in the robot control loop. However, fully autonomous navigation in unstructured environments has not yet been achieved despite significant advancements in computing and engineering technology. Autonomous vehicle navigation on urban roads has been of great interest due to its emerging commercial applications around the world. As a result, autonomous perception technologies are developing continuously due to the competitiveness of this industry [31]. Compared to the level of development of methods for autonomous navigation in urban settings [32], however, autonomous navigation in off-road scenarios has not been studied to the same extent, meaning there are significant opportunities for new research in this area. A large number of autonomous navigation techniques have been explored, and these can be broadly divided into two subsections according to the approach used to execute control commands following the processing of the input sensory data. As shown in Figure 1, autonomous navigation methods can be categorised as the classical modular pipeline or end-to-end learning-based approaches. The modular pipeline architecture includes intermediate representations that humans can interpret and that can provide information related to the failure modes of the approach. Furthermore, the system development can be parallelised among several expert teams because of its modular nature. Perception, localisation and mapping, path planning and robot control modules may consist of classical, learning-based or hybrid methods [8; 33]. Due to algorithmic and modelling limitations, however, the modular approach may not be optimal for general autonomous navigation applications. In addition, machine learning-based modules must be separately trained and validated using auxiliary loss functions, which can create suboptimality across the system as a whole. In contrast, end-to-end learning-based systems learn policies from observations of the outcomes that result from the actions of autonomous systems [18; 34]. In these systems, Deep Reinforcement Learning (DRL) is used to refine the control algorithms used to determine autonomous actions. However, commonly employed methods like imitation learning can suffer from overfitting and cause problems with the poor generalisation of the system behaviour when deploying in different environment scenarios [11]. In addition, when errors occur in an end-to-end system, it can be complex to investigate because the origin of these errors is hidden in the holistic neural network-based architectures [8; 35]. It is clear, however, that future development of autonomous robot navigation will rely on further advancements in robot environmental perception and machine learning. This paper provides a comprehensive review of the current state of research and the most promising advancements relevant to the field of ground robot navigation in unstructured environments, including perception and sensor fusion, scene understanding, and path planning.