3D Digital Grid Map Initialization and Path Planning for Autonomous Robot Navigation

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles.

For path planning of mobile robots in complex unknown three-dimensional (3D) environments, an accurate 3D volumetric representation of the environment is usually required. In this paper, we present a novel method to produce globally consistent 3D grid maps in real-time, through a grid-map update strategy and an efficient data structure. We transform point clouds into the world frame provided by a visual SLAM module, then process them by the grid mapping module to produce globally consistent 3D grid maps. Experimental results in an indoor scenario demonstrate that our approach is able to update the 3D grid map efficiently and can cope with loop closures while operating in real-time.

PurposeTwo and one half‐dimensional (2.5D) grid maps are useful for navigation in outdoor environment or on non‐flat surface. However, little attention has been given to how to find an optimal path in a 2.5D grid map. The purpose of this paper is to develop a path‐planning method in a 2.5D grid map, which aims to provide an efficient solution to robot path planning no matter whether the robot is equipped with the prior knowledge of the environment.Design/methodology/approachA 2.5D grid representation is proposed to model non‐flat surface for mobile robots. According to the graph extracted from the 2.5D grid map, an improved searching approach derived from A* algorithm is presented for the shortest path planning. With reasonable assumption, the approach is improved for the path planning in unknown environment.FindingsIt is confirmed by experiments that the proposed planning approach provide a solution to the problem of optimal path planning in 2.5 grid maps. Furthermore, the experiment results demonstrate that our 2.5D D* method leads to more efficient dynamic path planning for navigation in unknown environment.Originality/valueThis paper proposes a path‐planning approach in a 2.5D grid map which is used to represent a non‐flat surface. The approach is capable of efficient navigation no matter whether the global environmental information is available at the beginning of exploration.

Necessity to recognize the world like a home environment by a humanoid robot has recently been arisen for daily usages. As an observation sensor, stereo vision is the most common device for a humanoid robot to obtain the environmental data, but it is more erroneous than a laser sensor. To overcome the inaccuracy of stereo vision, we propose a particle-based SLAM technique so that the SLAM posterior is estimated by multiple hypotheses. The major difficulty of the particle-based SLAM with 3D grid maps is the high computational cost. To reduce the computational cost, we also propose a scheduling method for the time when to match and for particles that engage in the matching process. Through experiments with a humanoid robot, HRP-2, it is shown that the proposed approach can reduce the computational cost while preserving estimation accuracy.

Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

The task assignment and path planning of a multi-AUVs system has now attracted considerable attention and become a hotspot in the research. In this paper, a novel algorithm of multi-AUVs task assignment and path planning based on Biologically Inspired Neural Network Map (BINN) is proposed. Firstly, the grid map is built by discretizing the three-dimensional underwater environment into many equal grids. Secondly, the activity values of all AUVs in the BINN maps of each target are calculated. Then, the AUV with the highest activity value in the BINN map of the target is selected as the winning AUV for the target. Finally, the winning AUV performs path planning according to the BINN strategy. Through the simulation experiment, it is proved that the novel BINN algorithm proposed in this paper can effectively and reasonably distribute multi-AUVs and reduce the overall sailing distance.

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. Mainstream methods (e.g., PCN and TopNet) use Multi-layer Perceptrons (MLPs) to directly process point clouds, which may cause the loss of details because the structural and context of point clouds are not fully considered. To solve this problem, we introduce 3D grids as intermediate representations to regularize unordered point clouds and propose a novel Gridding Residual Network (GRNet) for point cloud completion. In particular, we devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information. We also present the differentiable Cubic Feature Sampling layer to extract features of neighboring points, which preserves context information. In addition, we design a new loss function, namely Gridding Loss, to calculate the L1 distance between the 3D grids of the predicted and ground truth point clouds, which is helpful to recover details. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks. KeywordsPoint cloud completionGriddingCubic feature sampling

This study focuses on an unknown environment exploration method for generating a 3D occupancy grid map using a mobile manipulator robot with an RGB-D camera. To determine the moving destination and camera pose, a boundary between known and unknown regions is detected as a frontier, and a frontier observation map is created to record the number of frontiers that can be observed at a certain position and pose. Subsequently, based on the frontier observation map, an approach to efficiently generate a map in a short travel distance and time by actively changing the camera posture while moving is explored. However, if the robot simply sets the position by using the largest number of frontiers as the destination, it may have to move back and forth to the same location many times when missing some frontiers, resulting in more time to generate the map. To solve this problem, a method of modifying the frontier observation map based on the distance and angle to preferentially observe the frontier near the robot and in the direction where the robot is moving, respectively, is proposed. The effectiveness of the modification process in determining the destination in terms of the length of the path traveled and map generation time for several environments is verified by simulations. In addition, the effect of actively changing the camera posture while the robot is moving is evaluated.KeywordsMobile manipulator robot3D mappingAutonomous exploration

Optimal path planning is a key component in recent autonomous driving technology. Many navigation companies have developed services for predicting and navigating the optimal path by receiving information from users in real-time. However, these global path-based methods cannot predict the local, refined path due the resolution limitation of the GPS. In this paper, we present an object detection and lane detection system to predict the optimal local path using point cloud data(PCD) acquired by an autonomous dataset collection vehicle.

Ships transport large volumes of cargo, and are therefore major contributors to the global economy. Ship collisions can cause significant economic losses. Path-planning algorithms can prevent such collisions by suggesting the optimal path for navigation. Conventional path-planning algorithms are disadvantageous, because they do not consider the navigation practices followed by experienced navigators. Therefore, in this study, we developed the requirements for optimal local path planning of autonomous ships by considering the open sea, restricted waters, and two-ship and multi-ship interactions, in addition to the navigation practices adopted by navigators and rules in COLREGs part B. First, the navigation practices under various scenarios were collected. Subsequently, these practices were linked to COLREGs part B to extract the key rules and keywords related to collision avoidance. Finally, the requirements for generating the optimal local path were drafted based on the rules and keywords. The utility of the requirements was demonstrated by applying them to representative path-planning algorithms for the timely and accurate evaluation of their effectiveness. The proposed requirements can be utilized to improve the existing path-planning algorithms and develop superior algorithms in the future.

This paper proposes an improved algorithm applied to path planning for the inspection of unmanned aerial vehicles (UAVs) in urban pipe corridors, which introduces a collaborative game between spherical vector particle swarm optimization (SPSO) and differential evolution (DE) algorithms. Firstly, a high-precision 3D grid map model of urban pipe corridors is constructed based on the actual urban situation. Secondly, the cost function is formulated, and the constraints for ensuring the safe and smooth inspection of UAVs are proposed to transform path planning into an optimization problem. Finally, a hybrid algorithm of SPSO and DE algorithms based on the Nash bargaining theory is proposed by introducing a cooperative game model for optimizing the cost function to plan the optimal path of UAV inspection in complex urban pipe corridors. To evaluate the performance of the proposed algorithm (GSPSODE), the SPSO, DE, genetic algorithm (GA), and ant colony optimization (ACO) are compared with GSPSODE, and the results show that GSPSODE is superior to other methods in UAV inspection path planning. However, the selection of algorithm parameters, the difference in the experimental environment, and the randomness of experimental results may affect the accuracy of experimental results. In addition, a high-precision urban pipe corridors scenario is constructed based on the RflySim platform to dynamically simulate the optimal path planning of UAV inspection in real urban pipe corridors.

A novel technique called laser simulator approach for visibility search graph-based path planning has been developed in this article to determine the optimum collision-free path in unknown environment. With such approach, it is possible to apply constraints on the mobile robot trajectory while navigating in complex terrains such as in factories and road environments, as the first work of its kind. The main advantage of this approach is the ability to be used for both global/local path planning in the presence of constraints and obstacles in unknown environments. The principle of the laser simulator approach with all possibilities and cases that could emerge during path planning is explained to determine the path from initial to destination positions in a two-dimensional map. In addition, a comparative study on the laser simulator approach, A* algorithm, Voronoi diagram with fast marching and PointBug algorithms was performed to show the benefits and drawbacks of the proposed approach. A case study on the utilization of the laser simulator in both global and local path planning has been applied in a road roundabout setting which is regarded as a complex environment for robot path planning. In global path planning, the path is generated within a grid map of the roundabout environment to select the path according to the respective road rules. It is also used to recognize the real roundabout from a sequence of images during local path planning in the real-world system. Results show that the performance of the proposed laser simulator approach in both global and local environments is achieved with low computational and path costs, in which the optimum path from the selected start position to the goal point is tracked accordingly in the presence of the obstacles.