Path Planning Method Research Articles

A digital twin-driven pre-grasp path planning method for robotic deep-frame grasping of disordered workpieces

In a sheet metal parts welding shop, traditional robot path-planning methods prove inadequate for achieving automatic robot loading and unloading of disordered workpieces in the deep material frames. Therefore, this paper proposes a robotic motion planning framework in Deep-frame Disordered Workpiece Grasping (DDWG), ensuring efficient automatic loading and unloading tasks with high efficiency and collision-free operations. In conjunction with the actual DDWG scenario, an Oriented Bounding Boxes (OBB) based hierarchical enclosing box algorithm is used for collision detection. Further, this paper proposes a two-segment Middle Pre-Grasp Rapidly-exploring Random Tree (MPG-RRT) algorithm for path planning, utilizing pre-grasp point (middle point) guidance. In the DDWG process, a pre-adjustable pre-grasp point is introduced to expand the two random trees toward the intermediate points. Additionally, a probabilistic goal bias and a goal gravity strategy are incorporated with an adaptive gravity step size at each exploring stage to guide the expansion of random trees toward the goal point direction and to optimize paths by removing redundant nodes. Finally, an existing digital twins-driven robotic production line is reconfigured to simulate motion planning in two DDWG scenarios, which validates the effectiveness and superiority of the proposed method.

Modern Methods and Algorithms of Path Planning for Autonomous Mobile Robots, Their Objective Functions

The current situation in the world of research algorithms for path planning by autonomous robots shows that there are a large number of algorithms and their modifications that require researchers and engineers of autonomous robotics to have a fine understanding of the fundamental aspects of their functioning. The general goal of the research is to conduct an analysis of existing algorithms, methods and their modifications from the point of view of the component of the mathematical apparatus, namely their target functions. The research examines 38 modern algorithms and methods of path planning by autonomous robots. Considered the importance and criticality of understanding the objective function for the field of robotics. Examples illustrating the importance of this aspect are given. In the process of research, algorithms are presented in the form of formalized generalized mathematical formulas taking into account possible minimization/maximization, their improvements and improvements. For each considered algorithm and method, a conclusion is given regarding its target function. In the study, the methods of "Dynamic games" are separately highlighted and the application of the fundamental "Method of solving functions of A.O. Chikryi" for path planning is considered. In general, information on the advantages and importance of understanding the mathematical apparatus of pathfinding algorithms by autonomous robots for solving scientific problems is provided. The generalized results are summarized in a single comparative table, which provides the main formalized function of the algorithms, reveals the main idea, advantages/disadvantages, the scope of application and an example of use. The comparative table is presented in the form of a generalized auxiliary reference element of the study.

A Self-Adaptive Escape Route Planning Model Based on Dynamic Wildfire Information

Background: Escape routes are important measures for firefighters to ensure their own safety, providing predetermined paths to safe areas. Their establishment needs to consider numerous factors, such as the timeliness and safety of the routes. Aims: Optimize the path planning method previously studied by our team to ensure the dynamic nature, timeliness, and safety of the routes. Methods: (1) Propose a comprehensive safety index that encompasses both spatial and temporal safety indices, providing a more holistic approach to route safety. (2) Introduce spatial adaptive factors and spatial safety windows corresponding to the spatial safety index within the comprehensive safety index. (3) Present a new concept, the “observation cycle”, as a standard for the frequency of updating wildfire spread information, thereby addressing the issue of a lack of real-time input information. Based on this, we propose a reliable dynamic update rule for its updating. Results: Compared to the unoptimized model, the final optimized model’s planned escape routes offer impressive dynamic performance, effectively guarding against sudden changes in wildfire conditions, enhancing route safety, and ensuring timeliness. Conclusions: This research ensures that firefighters can effectively guard against the threats posed by sudden changes in wildfire conditions when escaping in wildfire environments, while also guaranteeing timeliness and safety.

Recent progress, challenges and future propects of applied deep reinforcement learning : A practicial perspecive in path planning

Path planning is one of the most crucial elements in the field of robotics, such as autonomous driving, minimally invasive surgery and logistics distribution. This review begins by summarizing the limitations of conventional path planning methods and recent work on DRL-based path planning methods. Subsequently, the paper systematically reviews the construction of key elements of DRL methods in recent work, with the aim of assisting readers in comprehending the foundation of DRL research, along with the underlying logic and considerations from a practical perspective. Facing issues of sparse rewards and the exploration-exploitation balance during the practical training process, the paper reviews enhancement methods for training efficiency and optimization results in DRL path planning. In the end, the paper summarizes the current research limitations and challenges in practical path planning applications, followed by future research directions.

Enhanced Unmanned Surface Vehicle Path Planning Based on the Pair Barracuda Swarm Optimization Algorithm: Implementation and Performance in Thousand Island Lake

The path planning problem for unmanned surface vehicles (USVs) is related to multiobjective optimization, including shortest path, minimum energy consumption, and obstacle avoidance, making it particularly complex in multi-island and multiobstacle environments such as Thousand Island Lake. An enhanced path planning method for USVs based on the pair barracuda swarm optimization (PBSO) algorithm is proposed, and the complex water environment of Thousand Island Lake is taken as an example. The PBSO algorithm simulates the social behaviour of pair barracuda innovative and deep memory mechanisms, which can enhance the algorithm’s global search ability and local optimal escape ability in high-dimensional space. The probabilistic roadmap (PRM) method was initially used to model complex environments with multiple islands and obstacles. Moreover, four evaluation indicators were proposed to evaluate the performance of the obtained path: total navigation distance (TND), number of returns (NT), average turning angle (ATA), and minimum safe distance (MSD) from obstacles. The PBSO algorithm is used to optimize the initial path to reduce frequent turns and turning amplitudes during navigation. Path planning experiments were conducted on four simulated map environments with different ranges and complexities. Compared with state-of-the-art heuristic path planning methods, our method can identify the optimal path faster and has better stability. The enhanced USV path planning method based on the PBSO algorithm provides a new path planning strategy for the practical application of USVs under the real Thousand Island Lake.

A path planning method in three-dimensional complex space based on Bézier curves and a hybrid zebra optimization algorithm

A path planning method in three-dimensional complex space based on Bézier curves and a hybrid zebra optimization algorithm

Lane change path planning using sweeping region for full-sized autonomous driving bus in urban environment

This paper presents a path planning method for the lane change maneuver of a full-sized autonomous driving bus in urban environment. A sampling method is incorporated for lane change path planning. Path candidates for lane changes are generated using the pattern of a quintic polynomial function. For optimal path selection among the candidates, a cost function and constraints are designed and applied, considering ride quality, lane change efficiency, and safety. Safety conditions account for lane departure and collision with obstacles. Considering large vehicles such as buses, the region swept by the vehicle body is derived by exploiting the geometrical relationship of the control point and corners, assuming that the control point follows the investigated path candidate. The sweeping region is utilized for accurate and efficient evaluation of the safety condition of the path candidate. Subsequently, longitudinal motion is planned based on Model Predictive Control (MPC) to maintain adequate clearance with surrounding vehicles and follow the desired speed. The proposed algorithm has been evaluated based on closed-loop simulations and vehicle-in-the-loop tests. The test results show that the proposed algorithm plans a safe lane change path, ensuring the vehicle body remains within the safe region. Additionally, the proposed algorithm is shown to enhance real-time performance.

Optimizing UAV performance with IoT and fuzzy linear fractional transportation models

Unmanned aerial vehicles (UAVs) have become indispensable in replacing human pilots for high-risk, high-intensity operations, including search-and-rescue missions, infrastructure inspection, and environmental monitoring. As a key enabler of these operations, UAV task planning systems require advanced trajectory optimization to operate effectively in dynamic, complex environments. This paper presents an innovative model integrating fully fuzzy linear fractional transportation programming (FTMP) with Internet of Things (IoT) technologies to enhance UAV performance. Unlike conventional path-planning methods, the proposed model leverages bio-inspired artificial intelligence (AI) algorithms and mixed-integer programming to solve multi-objective optimization problems under uncertainty. A critical feature of this platform is its ability to continuously adjust flight altitude based on real-time data, accounting for varying terrain morphology, fuel constraints, flight restrictions, and threats, thus ensuring optimal 3D trajectory planning. This makes the platform particularly effective in emergency scenarios such as natural disasters, explosions, or fires, where it supports rapid and accurate search-and-rescue efforts. The UAV system not only minimizes human involvement but also reduces the response time by transmitting real-time video, audio, and GPS data to responders over long distances. The proposed solution offers several advantages over traditional aerial platforms, including: (a)Precision tracking and path optimization with a demonstrated accuracy exceeding 88 % across recall, precision, and F-measure metrics. (b)Cost-effectiveness: The UAV is compact, lightweight, and affordable, significantly reducing operational expenses compared to helicopters. (c)Adaptability: Remote sensing allows for seamless control in rugged and obstructed environments. (d)Scalability: IoT integration supports multi-drone coordination for continuous real-time data collection and transmission to smartphones or command centers. This pragmatic UAV model offers transformative potential for emergency response, environmental monitoring, and smart infrastructure management, validating its reliability and efficiency through comprehensive performance metrics.

Multi-robot dynamic path planning with priority based on simulated annealing

This paper presents a path planning method based on an improved simulated annealing (SA) for multi-robot navigation in a 2D plane. The method can achieve collision-free and efficient movement in environments where dynamic obstacles exist. To address the problem of considerable computational effort of general heuristic algorithms, this study improves the running process of the algorithm so that it can lock the optimal path in the process of searching for a path at a very fast speed. In addition, a prioritisation strategy is proposed for the problem of difficult coordination among multiple robots. The method has a large improvement in the coordinated operation between individual robots. Simulation tests show that the proposed method can coordinate multiple robots to avoid collisions, whilst effectively avoiding local minima and completing the task in the shortest possible time. Compared with other algorithms, the advantages of the improved SA are more obvious, and the path length obtained is about 10% shorter than other dynamic path planning algorithms, and the success rate can reach 100%.

Proposed Multi-ST Model for Collaborating Multiple Robots in Dynamic Environments

Coverage path planning describes the process of finding an effective path robots can take to traverse a defined dynamic operating environment where there are static (fixed) and dynamic (mobile) obstacles that must be located and avoided in coverage path planning. However, most coverage path planning methods are limited in their ability to effectively manage the coordination of multiple robots operating in concert. In this paper, we propose a novel coverage path planning model (termed Multi-ST) which utilizes the spiral-spanning tree coverage algorithm with intelligent reasoning and knowledge-based methods to achieve optimal coverage, obstacle avoidance, and robot coordination. In experimental testing, we have evaluated the proposed model with a comparative analysis of alternative current approaches under the same conditions. The reported results show that the proposed model enables the avoidance of static and moving obstacles by multiple robots operating in concert in a dynamic operating environment. Moreover, the results demonstrate that the proposed model outperforms existing coverage path planning methods in terms of coverage quality, robustness, scalability, and efficiency. In this paper, the assumptions, limitations, and constraints applicable to this study are set out along with related challenges, open research questions, and proposed directions for future research. We posit that our proposed approach can provide an effective basis upon which multiple robots can operate in concert in a range of ‘real-world’ domains and systems where coverage path planning and the avoidance of static and dynamic obstacles encountered in completing tasks is a systemic requirement.

Research on autonomous obstacle avoidance and automatic obstacle avoidance of minimally invasive surgical robots

Abstract. With the development of modern society, people pay more and more attention to health and safety, especially to the development of medical techniques. The demand for higher medical standards has led to minimally invasive surgery becoming a better choice for patients. Minimally invasive surgeries involve less trauma, lower blood loss, quicker recovery, and a lower risk of infection. However, compared to traditional surgeries, these surgeries demand higher precision and skill from the surgeons, making surgical robots the preferred tool for most minimally invasive operations. Surgical robots can eliminate the surgeon's hand tremors during the operation, enhance the precision of surgical operations, and offer greater flexibility with their small end size inside human organs. Despite these advantages, minimally invasive surgical robots can not avoid obstacles and plan the path of human organs and cavities in real-time. This paper reviews the dynamic path planning and real-time obstacle avoidance of modern minimally invasive surgical robots, including their development progress and research directions. It also introduces in detail the mainstream research methods of path planning and real-time obstacle avoidance in modern research. Finally, the current technical challenges and the prediction of the future research direction are pointed out and the reference suggestions are provided.

Continuous fiber printing path generation based on Euler diagram division

Continuous fiber printed structures are widely used in aerospace applications because of their excellent performance and high design freedom. Existing path planning methods optimize the continuous fiber printing direction by stress lines or discrete angles, but these methods are far from perfect printing paths and lack planning for connectivity relations. In this study, a planning method for continuous fiber printing paths based on constructing Eulerian circuits from a weighted undirected graph is proposed. The principal stress lines are transformed into a weighted undirected graph, which divides the Eulerian subgraph by splitting the common nodes to plan the continuous fiber printing path circuit. Subsequently, the printing structure parameters are optimized based on simulation analysis. The experimental results, taking MBB (Messerschmitt-Bolkow-Blohm) beams as an example, show that the printing path designed in this paper improves the structural strength by 52.9% and the structural stiffness by 81.7% compared with the conventional printing path.

Multi-objective optimization based robotic path planning for interpolation reconstruction model of CT data

Robotic path planning plays a pivotal role in computer-aided liver tumor thermal ablation surgery. However, traditional methods face challenges in terms of low reconstruction efficiency and planning safety. To address these issues, we propose a method for robotic path planning of liver tumor thermal ablation surgery. Firstly, an interlayer interpolation algorithm based on optical flow estimation is utilized to compensate for large interlayer spacing in computed tomography (CT) images by inserting predicted images between sequence images. Secondly, the voxel traversal strategy and patch intersection calculation strategy in the standard marching cube (MC) algorithm is optimized to improve the efficiency of abdominal tissues reconstruction. Finally, comprehensive clinical constraints are summarized to ensure the surgery safety and the strength pareto evolutionary algorithm II (SPEA-II) is leveraged to optimize surgery path, which can obtain even distribution of solutions in high-dimension optimization problems. Extensive experiments conducted on the 3Dircadb and SLIVER07 datasets revealed that our proposed method reduces reconstruction time by 21.5% compared to the standard MC algorithm, while achieving an average overlap rate of 88.25% and an average Hausdorff distance of 15.25 mm between Pareto front points and surgeon's recommended puncture points.

A Review of UAV Path-Planning Algorithms and Obstacle Avoidance Methods for Remote Sensing Applications

The rapid development of uncrewed aerial vehicles (UAVs) has significantly increased their usefulness in various fields, particularly in remote sensing. This paper provides a comprehensive review of UAV path planning, obstacle detection, and avoidance methods, with a focus on its utilisation in both single and multiple UAV platforms. The paper classifies the algorithms into two main categories: (1) global and local path-planning approaches in single UAVs; and (2) multi-UAV path-planning methods. It further analyses obstacle detection and avoidance methods, as well as their capacity to adapt, optimise, and compute efficiently in different operational environments. The outcomes highlight the advantages and limitations of each method, offering valuable information regarding their suitability for remote sensing applications, such as precision agriculture, urban mapping, and ecological surveillance. Additionally, this review also identifies limitations in the existing research, specifically in multi-UAV frameworks, and provides recommendations for future developments to improve the adaptability and effectiveness of UAV operations in dynamic and complex situations.

Integrated automatic parking path planning and trajectory tracking optimization method

Current automatic parking systems often separate path planning and trajectory tracking, increasing complexity and failing to meet the parking needs of autonomous cars. Consequently, this study presents an approach based on Multi-stage Nonlinear Model Predictive Control (MSNMPC) for integrated autonomous parking path planning and trajectory tracking optimization. The method represents the evolution of uncertainty parameters over time through a scenario tree, and the MSNMPC controller with a predictive horizon P defines P + 1 phases, each with specific cost and constraint functions that depend only on the vehicle state and control inputs of that phase to satisfy the constraints of all phases of the parking process. In addition, the method integrates path planning and tracking control into one optimization problem, which is solved online to achieve integrated parking control. Simulation confirms that, in comparison to MPC with RRT* hierarchical control, the integrated parking scheme of MSNMPC has less lateral error and offers superior flexibility and tracking performance. To verify the generality of the scheme, it was validated in the diagonal parking scenario and the vertical parking scenario, respectively. The results show that, compared to the control experiments, the parking elapsed time has been reduced by 10.76% and 9.02%, respectively, enhancing parking efficiency. In addition, the parking error has decreased by 30.77% and 38.46%, respectively, thus improving parking accuracy. Moreover, the minimum safe distance in the control scheme for addressing uncertainty factors is 0.7 m greater than that of the control experiments, meeting the driving requirements for driverless vehicles in parking scenarios.

VNS-BA*: An Improved Bidirectional A* Path Planning Algorithm Based on Variable Neighborhood Search.

The A* algorithm is an effective method for path planning; however, it has certain drawbacks, such as a high number of turns, low planning efficiency, and redundant searches. To address these issues, this paper proposes an improved bidirectional A* global path planning algorithm based on a variable neighborhood search strategy, named VNS-BA*. The new algorithm first employs an 8-11-13 neighborhood search method for node expansion. Then, the bidirectional search strategy is optimized by using the current nodes of the opposite path and the global target point, enabling the two paths to meet in the middle of the map. Finally, redundant turns are removed from the path, and cubic spline interpolation is applied to achieve local smoothing at the turns. The effectiveness of the improved algorithm was validated on different maps and compared with A* and its three derived improved versions. The simulation results indicate that VNS-BA* shows significant improvements in terms of the number of path turns, turn angles, and planning efficiency.

An Improved HBA Method for Path Planning of Substation Inspection Robots

Aiming at the problems of honey badger algorithm (HBA) in the path planning of substation inspection robot, such as long path length, time-consuming search and high obstacle risk rate, an improved HBA algorithm called IHBA is proposed. Firstly, the honey badger population is initialized by reverse learning mechanism to increase its diversity. Secondly, the adaptive weight factor is used to improve the density factor of the HBA, which effectively balances the exploration capacity in different stages and improves the optimization accuracy of the population. Finally, the elite mutation strategy is used to strengthen the honey badgers’ location, which can guide the population to produce offspring in the food source area with better adaptability. To verify the improved effect of the algorithm and its path planning performance, the path planning experiments are designed, which indicate that compared with the BOA, IACO, GFA and classic HBA, the proposed IHBA in this paper has shorter path, higher search efficiency and lower obstacle risk rate, which can not only help the substation inspection robot plan the optimal path globally, but also increase the smoothness of the planned path.

Research on an Intelligent Learning Path Planning Method for the VR-SimTrainer Virtual Reality Training Software Based on Deep Learning

This study explores an intelligent learning path planning method for the VR-SimTrainer virtual reality training software based on deep learning. With the increasing application of virtual reality technology in education and vocational training, the traditional fixed learning path training method can no longer meet the needs of personalized learning. To address this problem, this paper introduces deep learning technology to dynamically adjust the learning path by analyzing the real-time operation data and learning behavior of the trainees, providing a personalized training experience for the trainees. Experimental results show that compared with traditional path planning methods, the intelligent learning path planning method based on deep learning can significantly improve the learning effectiveness and efficiency of trainees. In addition, the research also reveals some challenges faced in the application of this method, such as the high data requirements and the limitations of model generalization ability. Overall, this study provides new ideas for the intelligent development of virtual reality training software and demonstrates the potential value of deep learning in personalized education and adaptive learning.

Flexible Path Planning for Multi-Agent Field Observation

This paper proposes a flexible path-planning method for conducting agricultural observation tasks using multiple autonomous guided vehicles. The observation task was formulated as the rural postman problem by graphing the agricultural field to be observed and assigning costs to mandatory and optional paths. The observation area was re-divided according to the progress of each robot’s work, and the latest path was planned to minimize the difference between the working time of individual robots, thereby reducing the overall working time. Simulations were conducted to verify the influence of the parameters used in the proposed method. The effect of workload adjustment on progress delay was verified, and the increase in overall working time owing to delay was reduced by an average of 32.9%. A performance comparison was conducted using Google OR-Tools, software specialized for combinatorial optimization. Superior solutions with shorter computation times were obtained using the proposed method.

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.