A Planar Multi-Inertial Navigation Strategy for Autonomous Systems for Signal-Variable Environments

The challenge of precise dynamic positioning for mobile robots is addressed through the development of a multi-inertial navigation system (M-INSs). The inherent cumulative sensor errors prevalent in traditional single inertial navigation systems (INSs) under dynamic conditions are mitigated by a novel algorithm, integrating multiple INS units in a predefined planar configuration, utilizing fixed distances between the units as invariant constraints. An extended Kalman filter (EKF) is employed to significantly enhance the positioning accuracy. Dynamic experimental validation of the proposed 3INS EKF algorithm reveals a marked improvement over individual INS units, with the positioning errors reduced and stability increased, resulting in an average accuracy enhancement rate exceeding 60%. This advancement is particularly critical for mobile robot applications that demand high precision, such as autonomous driving and disaster search and rescue. The findings from this study not only demonstrate the potential of M-INSs to improve dynamic positioning accuracy but also to provide a new research direction for future advancements in robotic navigation systems.

Developing a Flying Explorer for Autonomous Digital Modelling in Wild Unknowns

Digital modelling stands as a pivotal step in the realm of Digital Twinning. The future trend of Digital Twinning involves automated exploration and environmental modelling in complex scenes. In our study, we propose an innovative solution for robot odometry, path planning, and exploration in unknown outdoor environments, with a focus on Digital modelling. The approach uses a minimum cost formulation with pseudo-randomly generated objectives, integrating multi-path planning and evaluation, with emphasis on full coverage of unknown maps based on feasible boundaries of interest. The approach allows for dynamic changes to expected targets and behaviours. The evaluation is conducted on a robotic platform with a lightweight 3D LiDAR sensor model. The robustness of different types of odometry is compared, and the impact of parameters on motion planning is explored. The consistency and efficiency of exploring completely unknown areas are assessed in both indoor and outdoor scenarios. The experiment shows that the method proposed in this article can complete autonomous exploration and environmental modelling tasks in complex indoor and outdoor scenes. Finally, the study concludes by summarizing the reasons for exploration failures and outlining future focuses in this domain.

Representation granularity enables time-efficient autonomous exploration in large, complex worlds

We propose a dual-resolution scheme to achieve time-efficient autonomous exploration with one or many robots. The scheme maintains a high-resolution local map of the robot's immediate vicinity and a low-resolution global map of the remaining areas of the environment. We believe that the strength of our approach lies in this low- and high-resolution representation of the environment: The high-resolution local map ensures that the robots observe the entire region in detail, and because the local map is bounded, so is the computation burden to process it. The low-resolution global map directs the robot to explore the broad space and only requires lightweight computation and low bandwidth to communicate among the robots. This paper shows the strength of this approach for both single-robot and multirobot exploration. For multirobot exploration, we also introduce a "pursuit" strategy for sharing information among robots with limited communication. This strategy directs the robots to opportunistically approach each other. We found that the scheme could produce exploration paths with a bounded difference in length compared with the theoretical shortest paths. Empirically, for single-robot exploration, our method produced 80% higher time efficiency with 50% lower computational runtimes than state-of-the-art methods in more than 300 simulation and real-world experiments. For multirobot exploration, our pursuit strategy demonstrated higher exploration time efficiency than conventional strategies in more than 3400 simulation runs with up to 20 robots. Last, we discuss how our method was deployed in the DARPA Subterranean Challenge and demonstrated the fastest and most complete exploration among all teams.

Faster navigation of semi-structured forest environments using multirotor UAVs

Modern approaches for exploration path planning generally do not assume any structural information regarding the operational area. Therefore, they offer good performance when the region of interest is entirely unknown. However, for some applications such as plantation forest surveying, partial information regarding the survey area is known before the exploration process. Because the region of interest consists only of the lower portions of the tree stems themselves, the ground and high-elevation sections of the environment are unimportant and do not need to be observed. Due to these unconventional conditions, existing methods favoring faster survey speeds produce suboptimal surveys as they do not try and ensure even coverage across the entire exploration volume, while methods that favor reconstruction accuracy produce excessively long survey times. This work proposes a structured exploration approach specifically for plantation forests utilizing a lawnmowing pattern to maximize coverage while minimizing re-visited regions, guiding the unmanned aerial vehicle to visit all areas. Experiments are conducted in various environments, with comparisons made to state-of-the-art exploration planners regarding survey time and coverage. Results suggest that the proposed methods produce surveys with significantly more predictable coverage and survey times at the expense of a longer survey.

Efficient Informative Path Planning via Normalized Utility in Unknown Environments Exploration

Exploration is an important aspect of autonomous robotics, whether it is for target searching, rescue missions, or reconnaissance in an unknown environment. In this paper, we propose a solution to efficiently explore the unknown environment by unmanned aerial vehicles (UAV). Innovatively, a topological road map is incrementally built based on Rapidly-exploring Random Tree (RRT) and maintained along with the whole exploration process. The topological structure can provide a set of waypoints for searching an optimal informative path. To evaluate the path, we consider the information measurement based on prior map uncertainty and the distance cost of the path, and formulate a normalized utility to describe information-richness along the path. The informative path is determined in every period by a local planner, and the robot executes the planned path to collect measurements of the unknown environment and restructure a map. The proposed framework and its composed modules are verified in two 3-D environments, which exhibit better performance in improving the exploration efficiency than other methods.

Smart Vehicle Path Planning Based on Modified PRM Algorithm

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length.

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.