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To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined.

In traditional environment exploration algorithms, two problems are still waiting to be solved. One is that as the exploration time increases, the robot will repeatedly explore the areas that have been explored. The other is that in order to explore the environment more accurately, the robot will cause slight collisions during the exploration process. In order to solve the two problems, a DQN-based exploration model is proposed, which enables the robot to quickly find the unexplored area in an unknown environment, and designs a DQN-based navigation model to solve the local minima problem generated by the robot during the exploration. Through the switching mechanism of exploration model and navigation model, the robot can quickly complete the exploration task through selecting the modes according to the environment exploration situation. In the experiment results, the difference between the proposed unknown environment exploration method and the previous known-environment exploration methods research is less than 5% under the same exploration time. And in the proposed method, the robot can achieve zero collision and almost zero repeated exploration of the area when it has been trained for 30w rounds. Therefore, it can be seen that the proposed method is more practical than the previous methods.

In this article, an advanced control system for mobile robots is presented, which combines nonlinear dimensionality reduction via kernel PCA with a soft-interval kernel and a dual neural network architecture (TFNN and WFNN) to enable optimal, fast, and safe navigation in unknown and noisy environments. The main innovation lies in designing an integrated framework that reduces high-dimensional sensory data from 1,067 to 18 key features—while retaining over 99% of variance—thus eliminating noise and improving data quality, and enabling targeted modeling of both target-following and boundary-following behaviors through two specialized neural networks. The system is validated using real-world data collected from a robot equipped with a SICK LMS200 sensor, resulting in a reduction of path error to 3.1 cm (a 62.79% improvement over the noisy baseline), a slip coefficient of 0.022 , a zero collision rate , execution time reduced to 1.09 s , and path length minimized to 14.5 m. Furthermore, with variance coverage enhanced to 99.9%, model convergence time reduced from 25 to 10 epochs, and the interpretability index increased to 8.9; the proposed algorithm demonstrates superior efficiency, safety, and reliability when faced with real and noisy datasets. These achievements highlight the quantitative and qualitative superiority of the proposed method compared to leading approaches such as RRT*, PSO-Fuzzy, and DQN, establishing a new scientific foundation for the development of next-generation intelligent navigation systems.

Efficient autonomous exploration in unknown environments is a core challenge for Unmanned Aerial Vehicle (UAV) applications in unstructured settings. The primary challenges are exploration speed, coverage efficiency, and the autonomous, efficient, and obstacle-/threat-avoiding global guidance of UAV under local observational information. This paper proposes an autonomous exploration method driven by simultaneous incremental map prediction and the fusion of global frontier information to enhance the exploration efficiency of UAVs in unknown unstructured environments. Based on generative deep learning, we introduce an incremental map prediction method for 3D unstructured mountainous terrain, enabling the simultaneous acquisition of map predictions and their uncertainty estimates. Map prediction and trajectory planning are conducted concurrently: by utilizing the simultaneously predicted 3D map and its confidence (i.e., the uncertainty estimates), an overlap analysis is conducted between the flyable areas in the predicted map and the high-confidence regions. Dynamic guidance subspaces are generated by extracting global frontier points, within which shortest-time optimization is adopted for trajectory planning to maximize information gain and coverage per step. Experimental results demonstrate that compared to classical methods, our proposed approach achieves significant performance improvements in key metrics, including map coverage rate, total exploration time, and average path length.

This paper introduces a graph-based, potential-guided method for path planning problems in unknown environments, where obstacles are unknown until the robots are in close proximity to the obstacle locations. Inspired by the Fokker-Planck equation and the intermittent diffusion process, the proposed method generates a tree connecting the initial and target configurations, and then finds a path on it using the available environmental information. The tree and path are updated iteratively when newly encountered obstacle information becomes available. The resulting method is a deterministic procedure proven to be complete, i.e., it is guaranteed to find a feasible path, when one exists, in a finite number of iterations. The method is scalable to high-dimensional problems. In addition, our method does not search the entire domain for the path, instead, the algorithm only explores a sub-region that can be described by the evolution of the Fokker-Planck equation on graph with a changing of diffusion coefficient intermittently. We demonstrate the performance of our algorithm via several numerical examples with different environments and dimensions, including high-dimensional cases.

Grocery shoppers must negotiate cluttered, crowded, and complex store layouts containing a vast variety of products to make their intended purchases. This complexity may prevent even experienced shoppers from finding their grocery items, consuming a lot of their time and resulting in monetary loss for the store. To address these issues, we present a generic grocery robot architecture for the autonomous search and localization of products in crowded dynamic unknown grocery store environments using a unique context Simultaneous Localization and Mapping (contextSLAM) method. The contextSLAM method uniquely creates contextually rich maps through the online fusion of optical character recognition and occupancy grid information to locate products and aid in robot localization in an environment. The novelty of our robot architecture is in its ability to intelligently use geometric and contextual information within the context map to direct robot exploration in order to localize products in unknown environments in the presence of dynamic people. Extensive experiments were conducted with a mobile robot to validate the overall architecture and contextSLAM, including in a real grocery store. The results of the experiments showed that our architecture was capable of searching for and localizing all products in various grocery lists in different unknown environments.

This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method.

This study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict-Based Search (CBS) approach, introducing a unique hierarchical search mechanism for planning paths for multiple robots. The study aims to enhance flexibility in adapting to different environments. Three scenarios were tested, and the accuracy of the proposed algorithm was validated. In the first scenario, path planning was applied in unknown environments, both stationary and mobile, yielding excellent results in terms of time to arrival and path length, with a time of 2.3 s. In the second scenario, the algorithm was applied to complex environments containing sharp corners and unknown obstacles, resulting in a time of 2.6 s, with the algorithm also performing well in terms of path length. In the final scenario, the multi-objective algorithm was tested in a warehouse environment containing fixed, mobile, and multi-targeted obstacles, achieving a result of up to 100.4 s. Based on the results and comparisons with previous work, the proposed method was found to be highly effective, efficient, and suitable for various environments.

Navigation or path planning is the basic need for movement of robots. Navigation consists of two foremost concerns, target tracking and hindrance avoidance. Hindrance avoidance is the way to accomplish the task without clashing with intermediate hindrances. In this paper, an evolutionary scheme to solve the multi-agent, multi-target navigation problem in an unknown dynamic environment is proposed. The strategy is a combination of modified artificial bee colony for neighborhood search planner and evolutionary programming to smoothen the resulting intermediate feasible path. The proposed strategy has been tested against navigation performances on a collection of benchmark maps for A* algorithm, particle swarm optimization with clustering-based distribution factor, genetic algorithm and rapidly-exploring random trees for path planning. Navigation effectiveness has been measured by smoothness of feasible paths, path length, number of nodes traversed and algorithm execution time. Results show that the proposed method gives good results in comparison to others.

Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV’s next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.