HPS-RRT*: An Improved Path Planning Algorithm for a Nonholonomic Orchard Robot in Unstructured Environments

To improve the adaptability of the spherical mobile robot in unknown environment, this paper proposed the behavior-based fuzzy control method, and integrated fuzzy obstacle avoidance performance into this method. Firstly, the sensors were arranged according to the motion model of the spherical mobile robot, and which improved the flexibility and autonomy of the spherical mobile robot in unstructured environment. In the process of motion, the spherical mobile robot use multi-sensor, which is used to collect the obstacles and the target position information in the environment, to complete the path planning. Then, the MobotSim simulation platform was used to design the behavior-based control experiments for the spherical mobile robot, including wall-following, through narrow road, obstacle avoidance and trending target experiments.etc. The simulation results verified the validity and reliability of the fuzzy control system. Finally, the path planning experiments in the real environment was completed, which controlled the spherical mobile robot behavior by fuzzy controller and priority decisions, including obstacle avoidance, wall-following, cruising, tending to target and unlocking the deadlock behaviors, and so on, and modified the pose of the robot based on the angle of real-time feedback information to avoid obstacles precisely. The experimental results showed that the spherical mobile robot could complete control method based on behavior, further verified the effectiveness and practicability of the fuzzy control system.

With the expansion of human activities into mountainous regions, jungles, deserts, rural roads, and off-road terrains, there is an increasing demand for reliable navigation and location services in wild unstructured environments. Unlike urban environments, where established methodologies exist for constructing navigation maps, roadless unstructured environments lack comprehensive frameworks for navigation map construction. The conventional waypoint-based structures, which are well-suited to urban environments, are often ill-suited to the expansive and unstructured nature of field regions. Moreover, the absence of predefined user paths necessitates a fundamentally different approach to map construction. To address this challenge, we propose a novel method for generating navigation maps in unstructured roadless environment. New methodology leverages remote sensing as input for environmental perception, translating user traversability into geographical parameters and constructing a navigation mesh as a computational map. Compared with existing methods, the new method can adapt to large-scale unstructured environments. To validate the proposed method, reliability sampling tests and operational experiments were conducted on traversable area delineation and the navigation map generation. The experimental results indicate an 84% accuracy in traversable area analysis, with the constructed navigation map effectively supporting positioning and path planning while significantly reducing computational complexity in large-scale path planning tasks. The navigation mesh generated through this method effectively enhances the implementation of navigation and positioning services in off-road and roadless environments. The proposed method facilitates the construction of navigation maps capable of delivering navigation and localization services without requiring human presence in the target area.

There are many sources of uncertainty facing the fuzzy logic controller (FLC) for autonomous mobile robots navigating in changing and dynamic unstructured environments. The traditional type-1 FLC using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 fuzzy logic controller (FLC) using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present the type-2 FLC and its novel application for the real time control of mobile robots. We used the type-2 FLC to implement different robotic behaviours on different robotic platforms in indoor and outdoor unstructured and challenging environments. The type-2 FLCs dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1 FLCs whilst using smaller rule bases.

Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments–such as rural areas and rugged terrains–pose unique obstacles that are not common in structured urban areas. Despite these difficulties, autonomous driving in unstructured outdoor environments is crucial for applications in agriculture, mining, and military operations. Our survey reviews over 250 papers for autonomous driving in unstructured outdoor environments, covering offline mapping, pose estimation, environmental perception, path planning, end‐to‐end autonomous driving, datasets, and relevant challenges. We also discuss emerging trends and future research directions. This review aims to consolidate knowledge and encourage further research for autonomous driving in unstructured environments. To support ongoing work, we maintain an active repository of up‐to‐date literature and open‐source projects at: https://github.com/chaytonmin/Survey‐Autonomous‐Driving‐in‐Unstructured‐Environments .

When deploying multiple UAVs each with its own sensor platform into an unstructured environment, the swarm of these sensor platforms, i.e. the team of UAVs needs to deploy in a manner that will gather information about the local environment, identify and track objects of interest, and act in a co-operative and robust manner. Guidance strategies that result in UAV platforms covering the area of interest whilst avoiding obstacle, and allow UAV platforms to arrive at specific positions at specific times and with specific sensor orientation, is the focus of this presentation. The use of Dudin's sets would provide the framework for guidance and navigation. Coordinated path planning of multiple UAVs for simultaneous arrival with safe flight-paths is described. The simultaneous arrival of all UAVs is guaranteed by paths of equal lengths. Two conditions, namely minimum separation distance and non-intersection of paths at equal length are used/proposed to ensure the safe flight-path of UAVs. These conditions are to avoid inter-UAVs collision. The Dubins paths are used for the path planning. Bisection method is used to find the optimal turning radii of paths. The path planning guarantees design of paths of equal length with shortest paths. This is having advantages in reducing fuel consumption and increasing the durability of UAVs. Furthermore formal modelling techniques are applied to this problem of multi-UAV coordination, decision making and path planning. When UAV teams are called to operate in areas with uncertain information it is impossible to validate every conceivable situation that might arise in operation in such scenarios. By using formal techniques, aspects of the operating model that must necessarily hold true throughout can be mathematically validated. Kripke models of possible worlds are used to represent formally a multi-UAV scenario, where availability of environment information is to be used by the team to collect, exchange and utilise available information to make a complete survey of the specified area. The ability of our proposed Kripke model to cope with the uncertainties and decision making involved in this process can be verified using an exhaustive proof technique known as model checking.

To ensure the safe production of mines, the intelligent trend of underground mining operations is gradually advancing. However, the operational environment of subterranean mining is intricate, making the conventional path-planning algorithm used by mining inspection robots frequently inadequate for real requirements. To safeguard the mining inspection robot, targeting the problem of low search efficiency and path redundancy in the path planning of the existing rapidly exploring random tree (RRT) algorithm in the narrow and complex unstructured environment, a path-planning algorithm combining improved RRT and a probabilistic road map (PRM) is proposed. Initially, the target area is efficiently searched according to the fan-shaped goal orientation strategy and the adaptive step size expansion strategy. Subsequently, the PRM algorithm and the improved RRT algorithm are combined to reduce the redundant points of the planning path. Ultimately, considering the kinematics of the vehicle, the path is optimized by the third-order Bessel curve. The experimental simulation results show that the proposed path-planning algorithm has a higher success rate, smoother path, and shorter path length than other algorithms in complex underground mining environments, which proves the effectiveness of the proposed algorithm.

This study focuses on a multi-objective path planning problem that simultaneously minimizes path length, ensures trajectory smoothness, and enhances safety. Based on the characteristics of the problem, a deep reinforcement learning-based multi-objective RIME (DRL-MORIME) optimization algorithm is proposed. Regarding the limitations of the original MORIME, such as premature convergence, low search efficiency, and proneness to local optima, three improvements are made. During the search process, the DRL framework adaptively selects search operators, effectively balancing global exploration and local exploitation. For archive management, a novel hybrid selection mechanism is proposed to improve the diversity and distribution of the Pareto front. To improve path smoothness, a post-processing strategy reduces redundant nodes in the path. Comprehensive experiments and ablation studies on the ZDT, DTLZ, and WFG benchmark suites validate the superior performance of the DRL-MORIME algorithm. Path planning on maps of varying complexity achieves average improvements of 13.10% in path length, 69.58% reduction in total turning angle, and 13.96% reduction in collision risk. These results confirm the effectiveness of the proposed algorithm for multi-objective path planning of mobile robots.

As a step towards enabling snake robots to move in unstructured environments, this paper considers control strategies where environment adaptation is combined with directional control of snake robot locomotion. The first contribution of the paper is a general framework for motion control of snake robots, which allows the motion to be specified in terms of a body wave component, an environment adaptation component, and a heading control component. As a second contribution, we employ the controller framework to propose a control law for straight line path following control of snake robots in environments with obstacles. The paper presents simulation results where the path following controller is combined with a waypoint guidance strategy in order to steer the snake robot between waypoints in an obstacle environment.

Autonomous platforms are confronted by a diversity of challenges in unstructured environments, which make monitoring performance a non-trivial task. Some of these environments are so complex that they preclude persistent, nearby operator oversight. This absence of oversight motivates the need for an online monitoring system, specifically for robots operating in difficult environments. We develop a test methodology and set of online monitoring metrics by extending methods for characterizing robotic-systems using offline metrics. We implement this test methodology in an unstructured, outdoor environment and show the resulting performance information gained from our online monitoring solution. This online monitoring approach is generalizable such that it characterizes any robotic system that meets our set of hardware and software criteria.

This paper studies the obstacle avoidance and navigation of mobile robots in unstructured environments. Because of the shortcomings of one single sensor, the system integrates the stereo vision sensor blumblebee2 with the lidar sensor to detect the information surrounding the mobile robot’s surroundings. And then through data fusion to get a more complete, more accurate scene distribution. Then use the improved system of ant colony optimization to increase the convergence speed and precision of the algorithm in robot path planning. Finally, the simulation experiment is carried out in the environment of Matlab and Visual Studio, and the physical experiment is carried out under the ROS platform. The experimental results show the feasibility and effectiveness of the proposed method.

The growing number of collaborative robotics in unstructured environments creates highly nonconvex nonlinear shared dynamical systems. For safety and speed, path planning and collision avoidance are of the utmost importance in these situations. We present a novel nonlinear MPC solution for use on a three-dimensional four-axis robotic manipulator. The system is the first of it's kind to take into account moving obstacles. Using the OpEn framework, optimisation is done by the PANOC and ALM techniques. Experimentation demonstrates extremely fast solver times on both PC and embedded platforms.

Stone Aerospace is developing the SUNFISH® autonomous underwater vehicle which addresses many of the challenges of remote, autonomous exploration in unstructured environments. The AUV was designed to be a highly-capable platform for operating in a wide variety of complex 3D spaces, ranging from man-made (e.g. piers or harbors) to natural (e.g. reefs or caves). SUNFISH is a person-portable, 6-DOF hovering vehicle with built-in precision navigation and control, multibeam mapping, imaging, and CTD capabilities. Building on this base functionality7, we have developed high-level capabilities for performing simultaneous localization and mapping (SLAM), exploration, path planning, and precision docking. This paper describes these capabilities, and presents a demonstration in the unstructured labyrinthine 3D environment of Peacock Springs in northern Florida, USA. SUNFISH demonstrated the capability to autonomously explore this environment, creating a real-time map with which it navigated through the complex. SUNFISH penetrated 120 m into the caves and was able to autonomously find its way back to the deployment location. The capabilities developed for SUNFISH also have direct application to general inspection, monitoring, and survey tasks, as well as providing a basis for the types of autonomous vehicles that will explore where humans cannot go.

In the current research field of mobile robots, optimal path planning is a hot topic. Ant colony algorithm is diffusely used in mobile robot path planning because of its its positive feedback, distributed and so on. However, traditional basic ant colony algorithm has some disadvantages, such as easy local convergence, slow search convergence rate and low efficiency. To solve these problems, this paper proposes an improved ant colony algorithm. Based on the algorithm principle, a new method for global pheromone initialization is put forward by improving and optimizing the pheromone mechanism, and there is a certain probability to carry out “variation” operation in each search iteration process. Meanwhile, the calculation method of heuristic function is improved to reduce the possibility of premature convergence of the algorithm falling into local optimum. The two-dimensional space model of mobile robot is created by grid method. Then they are simulated and tested respectively, and the test results are compared and analyzed. The experimental results show that the improved algorithm can obviously optimize the defects of the original algorithm, which not only can plan the path faster, but also can plan the better path, and the search efficiency has been significantly improved.

This paper proposes a new path planning algorithm based on the CNN model. The path planning problem is completed with the dynamics of CNN by establishing a relationship between path control points and CNN cells. Based on the analysis of one dimensional space of CNN algorithm, a CNN equation is constructed and the path updating algorithm under the curvature constraint is obtained, then the stability of the algorithm is discussed. Path planning simulation based on two-dimensional space shows that this algorithm can avoid re-planning or falling into the local minimum, which means it can be successfully used in the path planning and maintenance of robots on the ground in dynamic environment.

With the continuous expansion of urban power grids, the planning and optimization of cable laying paths have become increasingly important in power system design. Traditional path planning methods often suffer from low efficiency, local optima, and poor adaptability to complex environments. To address these issues, this paper conducts a systematic study on cable laying path optimization based on the Ant Colony Algorithm (hereinafter called ACA), aiming to enhance the intelligence and engineering applicability of path planning. Based on the classical ACA and its improved variants, this study proposes strategies suitable for complex environments, including adaptive adjustment of heuristic factors, optimization of pheromone update mechanisms, and the integration of local search strategies. The results demonstrate that the proposed improved ACA achieves better optimization performance in terms of path length, current-carrying balance, and laying efficiency. The algorithm exhibits strong engineering adaptability and optimization capability in cable path planning, providing effective design references for practical power engineering. The innovation of this study lies in the targeted structural improvements to the ACA based on the specific requirements of cable engineering applications. Moreover, path planning visualization is implemented on the MATLAB platform, offering theoretical and methodological references for future path optimization in other engineering scenarios.