# Robots in Partially Known and Unknown Environments:
## 2 Related Work
In [2], an ant colony optimization algorithm is implemented to solve the offline robot motion planning. In this work, the workspace is discretized in \(50\times 50\) connected nodes with binary values representing obstacles. The pheromone levels are initialized at each and change according to the ant's paths. In [3], a hybrid approach is presented. Surrounding points set close to critical obstacles are used to find a path, and then particle swarm optimization is applied to improve such a path. When the robot approaches narrow spaces where a feasible path cannot be found, this approach considers more surrounding points, enlarging the grid resolution; if there exists a path, it will be found. The approach can obtain better results than probabilistic roadmap methods [12] and an ant colony optimization-based algorithm [21] solving instances of \(2000\times 2000\). In [22], a new hybrid planning algorithm is proposed. The approach, RRT*-CFS, combines sampling-based and optimization-based methods. Here, RRT*[23] is used to construct an initial feasible path, and then the convex feasible set algorithm is applied to solve the non-convex motion planing. More recently, learning-based approaches have been proposed for the problem. A residual convolutional neural network is proposed in [24]. Inspired by the imitation learning idea, the neural network uses an offline-prepared dataset based on global environment information and the optimal paths for training. An approach for online robot motion planning using neural networks is introduced in [25]. This approach can be used for mobile robots, manipulator robots, and multi-robot systems. Neural networks respond well to changes in the environment related to changes in obstacles or the dynamics of the environment [26]. The instances used include two-dimensional features with \(x\) and \(y\) coordinates and size \(50\times 50\) using \(2500\) neurons to represent the workspace. The instances used include labyrinth workspaces and mobile targets and mobile obstacles. Machine learning algorithms are coupled with case-based reasoning in [4]. Here, the authors present two approaches that use sets of solutions to resemble new ones using case-based reasoning. Given a path planning problem, the first approach selects a set of similar problems with their respective solutions. These solutions are merged and, using case-based reasoning, a solution to the new problem is constructed. The second approach constructs paths by iteratively analyzing the current state and determining the next ones using case-based reasoning based on the nearest neighbor solutions. In [27], an evolutionary approach is presented. The approach uses a matrix representation of the workspace where each cell is the size of the robot and movements are restricted to four axes. It solves offline and online planning. For the experiments, small obstacles are included in the initial path. A re-planning of the path is used to avoid collisions. The instances used are from \(50\times 50\) to \(3000\times 2000\), and the execution times vary from \(1\) s in the smallest instances to \(96\) s in the largest instances. The proposed algorithm obtains better results than the classic methods, especially for the largest problem instances. The same authors present in [28] an evolutionary approach for the dynamic problem. In this case, the positions of the obstacles are unknown, and the robot's sensors are limited. They use the same instances as in their previous work, but the robot only has information on the closest cells. The results vary from \(2\) s in the easy instances to \(250\) s in the more complex ones. These results are better in terms of time and the number of re-plannings needed to find a feasible path compared to [29]; however, the length of the path must be improved. In [30], a convolutionally evaluated gradient first search approach is proposed. A two-dimensional grid is used to model unknown spaces. Convolutional evaluation is used to guide the path to the target location by iteratively evaluating the target orientation.