Path Planning Research Articles

A multi-strategy improved sparrow search algorithm for indoor AGV path planning

To address the problems of weak search ability, easily falling into local optimal solutions and poor path quality of sparrow search algorithm in AGV path planning, a multi-strategy improved sparrow search algorithm (MISSA) is proposed in this paper. MISSA improves the global search ability by improving the discoverer position update operator and introducing the sine cosine algorithm; adopts the adaptive number of vigilantes and adaptive adjustment step size to improve the convergence speed; introduces the Levy flight variation strategy to reduce the probability of falling into any local optimal solution; optimizes the boundary handling mechanism to prevent the loss of population diversity at a later stage; finally, uses the large-scale neighborhood search strategy and path smoothing mechanism for path optimization to further improve the path quality. The superiority-seeking ability of MISSA was verified by 12 standard test functions, and then 30 simulation experiments were conducted in grid maps with two specifications of 20×20 and 30×30. The experimental results showed that, by using MISSA, the path length was reduced by 44.1% and 63.1%, the number of turns was reduced by 68.4% and 78.4%, and the risk degree was reduced by 61.3% and 77.2%, which verifies the superiority of MISSA in path planning. Finally, MISSA was ported to the QBot2e mobile robot for physical verification to prove its feasibility in practical applications.

Optimal path planning for unmanned aerial vehicles in power line inspection in rechargeable scenario

To address the issue that a single charge of UAVs (Unmanned Aerial Vehicles) do not satisfy task requirements, this paper proposes a solution involving the deployment of charging stations in the mission area. An energy-efficient path planning method is designed for UAVs with charging stations, which simultaneously determines the deployment locations and quantities of charging stations. The method formulates the energy optimization path planning problem of UAVs as an integer linear programming and an algorithm based on greedy strategy is designed for solving the model. A numerical example including simulations across various scenarios demonstrate that the proposed method can search a more energy-efficient paths compared to single-agent multi-sortie schemes with a greedy strategy. This research contributes to the development of intelligent and autonomous UAV systems for power line inspection, enhancing grid reliability and safety.

3D Voxel-based Path Planning for AVs in Dynamic Complex Environments

Abstract. With the rapid advancement of science and technology, autonomous driving technology has been widely applied in various fields such as the automotive industry, intelligent warehousing, and emergency rescue. Traditional 2D path planning has limitations in dealing with the complex and dynamic environments of the real world, making it difficult to meet the high precision and complex path planning requirements of autonomous vehicles. With the development of data acquisition technology, large-scale point cloud data has become easily accessible, which can better extract the three-dimensional complex of the environment. Nevertheless, the unstructured nature and vast amount of point cloud data make path planning on it extremely challenging. Voxel models can effectively compress point cloud data and provide neighboring topological structures. In view of this, we propose a voxel-based framework for the path planning of autonomous vehicles in dynamic and complex 3D environments, which involves converting point cloud data into voxels and extracting navigation spaces. Subsequently, deep reinforcement learning techniques are utilized to achieve obstacle avoidance and navigation on the voxel map. It is expected that this framework will contribute to the development of 3D path planning and enhance the utilization of point cloud data in the navigation field.

A novel autonomous exploration algorithm via LiDAR/IMU SLAM and hierarchical subsystem for mobile robot in unknown indoor environments

Abstract Autonomous exploration in unknown environments is an essential capability for mobile robots. The complexity of autonomous exploration, however, means that existing algorithms struggle to balance efficiency and comprehensiveness, causing low mapping accuracy and redundant path planning. To perform accurate and efficient exploration tasks, we have proposed a novel autonomous exploration algorithm via LiDAR/IMU (Inertial Measurement Unit) Simultaneous Localization and Mapping (SLAM) and hierarchical subsystem for mobile robot in unknown environments. Firstly, to enhance mapping accuracy for mobile robot exploration, LiDAR/IMU SLAM is improved with the assistance of backward propagation and iterated Kalman filter, and Bidirectional Rapidly–Exploring Random Trees* (BI–RRT*) is applied for efficient frontier point detection. Secondly, we optimize local path planning by leveraging information theory through perceptual quality evaluation, which is then integrated with global path planning utilizing an enhanced Travelling Salesman Problem solver and a sparse grid map to amplify exploration efficiency. Thirdly, an enhanced hierarchical autonomous exploration method for mobile robots is proposed, which incorporates local path planning for seamless navigation around highly promising exploration spots, coupled with global path planning to effectively interconnect various sub–regions. Finally, simulations and field tests have demonstrated that the proposed method explores an unknown indoor environment with a 30.8% reduction in exploration time and a 29.9% reduction in exploration path in comparison with Dynamic Stage Viewpoint Planner. The map constructed in this paper has more accurate details and exploration paths have been shortened to ensure effective exploration.

Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

Efficient path planning for autonomous vehicles based on RRT* with variable probability strategy and artificial potential field approach.

With the rapid development of autonomous driving technology, path planning has gained significant attention as it holds great potential for improving safety. The Rapidly-exploring Random Tree star(RRT*) algorithm has attracted much attention because of its good adaptability and expansibility. However, how solving problems in the RRT* algorithm such as slow convergence time, significant search range randomness, and unpredictability is a challenge. Therefore, an RRT* enhancement algorithm combining variable probability goal-bias strategy and artificial potential field(APF) method(Improved A-RRT*) is proposed in this paper. Firstly, the variable probability goal-bias strategy is introduced in the sampling process to make random tree expand towards the target direction and improve the directional searchability of the random tree. Secondly, the potential field function in APF is improved to prevent falling into local optimum problems during path generation. Thirdly, improved APF is combined with RRT*, the target generates a gravitational field on random tree, and the obstacle generates a repulsive force on it, leading random tree to grow toward the target region. Finally, the proposed algorithm is compared with RRT* algorithm and its derivative algorithm. The experimental results demonstrate that the proposed algorithm has obvious optimizations in convergence speed and path quality.

An Automatic Mapping Method of Navigation Map for Outdoor Road Scenes

Abstract. In the process of autonomous navigation of outdoor mobile robots, four modules are involved: perception, localization, planning, and controlling. The perception module utilizes sensors such as cameras and radars based on various principles to analyze the robot's surroundings in real-time. The localization module uses GPS (Global Positioning System), IMU (Inertial Measurement Unit), and prior maps for real-time positioning analysis. The planning module plans optimal path based on the outputs of the first two modules, and the controlling module directs the robot's chassis to move along the planned optimal path. For the localization module, the accuracy of GPS positioning results heavily depends on weather conditions and GPS signal receptions. Even if the positioning results of imu are integrated, the positioning accuracy still cannot meet the needs of robot navigation. Therefore, using prior maps for repositioning can compensate for this accuracy deficiency. The planning module also requires path planning based on prior maps. If the a priori map storage is large, it will lead to difficulties in usage, maintenance, and updates. Therefore, it is crucial to research lightweight navigation map mapping methods. In this paper, an automatically mapping method of lightweight navigation maps is proposed, combining cameras and LiDAR (Light Detection and Ranging), including semantic informations necessary for outdoor navigation positioning, such as pole-like objects and traffic signs for robot longitudinal positioning, and lane line elements for robot lateral positioning. This method automatic generates robot navigation maps in Lanelet2 format, providing support for subsequent positioning and path planning modules.

Airspace Constrained Free-Flight Analysis: Implications for Uncrewed Air Traffic Management

This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic.

High-Precision Monitoring during the Installation of Large Steel Structures by UAV Nap-of-the-Object Photogrammetry

Abstract. This paper proposes and validates a method based on UAV nap-of-the-object photogrammetry for refined 3D digital modeling to achieve high-precision monitoring during the installation of large steel structures, offering a comparative analysis with traditional oblique photogrammetry methods. Through field tests and data processing on the steel structure of a sports stadium in Zhejiang Province, the results demonstrate that nap-of-the-object photogrammetry can capture high-resolution images, offering significant advantages in detail representation and modeling of complex structures. Experimental data reveal that the model's root mean square error (RMSE) generated by nap-of-the-object photogrammetry is 14.56mm, markedly lower than the 51.99mm achieved by traditional methods, meeting the Level II accuracy standard specified in the "Technical Specifications for Nap-of-the-Object Photogrammetry." The study further shows that, when combined with optimized flight path planning, nap-of-the-object photogrammetry enhances both the efficiency and accuracy of data collection while improving operational safety. This technique is highly applicable and flexible, making it suitable for data acquisition of complex structures and high-rise buildings. In summary, the proposed method exhibits significant advantages in terms of accuracy, efficiency, safety, and applicability, providing a practical solution for high-precision, comprehensive digital modeling of large steel structures.

Covariance Control for Uncrewed Aircraft Systems Under Correlated Uncertainty

Uncrewed aircraft systems and advanced air mobility are associated with uncertain conditions, such as wind prediction errors, and they may deviate from dedicated airspaces referred to as corridors. However, further studies on these aspects are required. This study focuses on the covariance control for a stochastic discrete-time linear system under correlated uncertainties. A covariance control problem with chance constraints can be formulated as a convex-programming problem. Numerical simulations of uncrewed aircraft systems path planning can be used to compare two control laws: covariance control laws that consider correlated and noncorrelated noise. Numerical simulations were conducted to verify that the covariance control law considering correlated noise generates a trajectory that appropriately satisfies the constraints. Consequently, the effect of considering correlated noise in the covariance control law was clarified. In addition, covariance control has been demonstrated to generate appropriate trajectories for various state constraints. Moreover, numerical simulations indicated that covariance control facilitated the control of uncertainty, and the effectiveness of the covariance control was evaluated. The insights gained from the results of this study can inform enhanced uncertainty management for improving reliability and safety in real-world applications of uncrewed aircraft systems and advanced air mobility.

Metaheuristics-based multistage iterative optimization framework for decomposed high-speed rail systems planning problem

High-speed rail (HSR) systems planning and decision-making is a large-scale, multistage, intricate problem, where each stage represents a complex sub-problem. The solution approach to the multistage problem needs information exchange across the stages. A non-optimal planning and decision at any stage could yield a sub-optimal HSR system. This study proposes a novel multistage iterative HSR station location and alignment optimization framework (HSLAOF) to solve the main problem by decomposing it into the identification of (a) HSR potential regions and corridor, (b) station location, (c) shortest sequence/corridor of stations, and (d) collision-free alignment (horizontal and vertical) sub-problems. It integrates these sub-problems, which are solved separately using customized artificial intelligence- and motion planning-based metaheuristics such as particle swarm optimization for station location, ant colony optimization for shortest sequence of stations, path planner method for horizontal alignment and exploration, and exploitation-based ant colony optimization for vertical profile. In HSLAOF, the information from the upper and lower stages of the optimization process are exchanged and combined for a holistic optimized HSR system. Application of HSLAOF in the Mumbai-Ahmedabad HSR project yielded a solution that has 14.24 % lower total system cost, no environmental impact, 5.82 % better socio-economic impact, 21.14 % lesser noise and vibration impacted population, and 2.79 % higher station location utility than the one proposed by the experts. For predefined station location, the HSLAOF yielded alignment with 13.90 % lower total system cost and 24.00 % less noise and vibration impacted population than the one developed by experts. It required about 150 min to obtain a solution for each iteration.

Agent path planning based on adaptive polymorphic ant colony optimization

In the path planning of intelligent agents, ant colony algorithm is a popular path solving strategy and has been widely used. However, the traditional ant colony algorithm has problems of local optimum and redundant turning points. The adaptive polymorphic ant colony algorithm greatly improves the search and convergence speed through multi-group division and cooperation mechanism, which helps to enhance the global search ability and avoid falling into the local optimal solution. This paper proposes an improved pheromone update strategy and path selection record table construction to further improve the accuracy of path planning. Finally, the path is processed by cubic B-Spline smoothing curve to effectively reduce the turning points and realize the smoothing of the path. After MATLAB and ROS (Robot Operating System)-Gazebo simulation verification, the results show that the algorithm has good feasibility in complex environments. In summary, the improved adaptive polymorphic ant colony algorithm in this paper brings significant optimization and improvement to the global search of intelligent agents.

Path planning of mobile robot based on improved PRM and APF

In the field of mobile robot path planning, the artificial potential field (APF) method has been widely researched and applied due to its intuitiveness and efficiency. However, the APF algorithm often encounters challenges such as local minima and unreachable goals in complex environments. To address these issues, this paper proposes innovative path planning algorithm that integrates the advantages of the probabilistic roadmaps method (PRM), by introducing Sobol sampling and elliptical constraints to enhance PRM. The improved PRM not only reduces redundant nodes but also enhances the quality of sampling points. Furthermore, this paper uses the path nodes from the improved PRM as virtual target points for the APF algorithm, and effectively solves the inherent flaws of the APF algorithm through the segmented processing of the attractive force function and the introduction of a relative distance factor in the repulsive force function. Simulation results show that the algorithm reduces planning time, node count, and path length, demonstrate significant improvements in efficiency and performance. In addition, experiments with omnidirectional mobile robots further confirm the effectiveness and reliability of the algorithm in practical applications.

Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm

The conventional Ant Colony Optimization (ACO) algorithm, applied to logistics robot path planning in a two-dimensional grid environment, encounters several challenges: slow convergence rate, susceptibility to local optima, and an excessive number of turning points in the planned paths. To address these limitations, an improved ant colony algorithm has been developed. First, the heuristic function is enhanced by incorporating artificial potential field (APF) attraction, which introduces the influence of the target point’s attraction on the heuristic function. This modification accelerates convergence and improves the optimization performance of the algorithm. Second, an additional pheromone increment, calculated from the difference in pheromone levels between the best and worst paths of the previous generation, is introduced during the pheromone update process. This adjustment adaptively enhances the path length optimality. Lastly, a triangle pruning method is applied to eliminate unnecessary turning points, reducing the number of turns the logistics robot must execute and ensuring a more direct and efficient path. To validate the effectiveness of the improved algorithm, extensive simulation experiments were conducted in two grid-based environments of varying complexity. Several performance indicators were utilized to compare the conventional ACO algorithm, a previously improved version, and the newly proposed algorithm. MATLAB simulation results demonstrated that the improved ant colony algorithm significantly outperforms the other methods in terms of path length, number of iterations, and the reduction of inflection points, confirming its superiority in logistics robot path planning.

SHARP CURVE TRAJECTORY TRACKING OF A UNIVERSAL OMNI-WHEELED MOBILE ROBOT USING A SLIDING MODE CONTROLLER

Abstract In our previous work [1] and [2], two dynamic models of a Universal Omni-Wheeled Mobile Robot (UOWMR), and trajectory tracking controllers (three linear and one non-linear) were developed and presented. The purpose of that work was to identify suitable combinations of dynamic models and trajectory-tracking controllers, choosing the combination with the highest tracking accuracy and the lowest solver execution time. The ultimate purpose is utilizing these physics-based tools in a Reinforcement Learning (RL) agent developed for path planning and navigation in an unstructured environment. Three trajectories were investigated including a smooth path, a sharp curved path, and a smooth path with disturbance forces. The Sliding Mode Controller (SMC) following a trajectory with sharp (right-angled) curves was not investigated in that work. The sharp corners caused indeterminacy in the path derivatives required for the SMC algorithm. In this short paper, we complete our studies of the SMC on sharp corner paths utilizing slight trajectory corner smoothing.

Efficient micro-mobility path planning without using high-definition maps

Efficient path planning is crucial for the safe autonomous operation of micro-mobility vehicles in unknown environments. When planning paths for micro-mobility, factors, such as the changes in the user's intended destination and user comfort, must be considered. Moreover, for driving in unknown areas, the path planner should not rely on predetermined detailed information like High-Definition maps (HD maps). In this paper, we propose an innovative path planning algorithm for automated driving that solely uses basic perception data from RGB camera, IMU and GPS, thereby eliminating the dependency on HD maps. We initially convert perception data into an occupancy grid map. Subsequently, a global path planner, based on a modified A* algorithm, computes an efficient trajectory, particularly adept to navigation in unknown environments and to scenarios where goal position moves. Additionally, we developed a local planner that optimize multi-clothoid curves using designed constraints to predict the best trajectory. Our experiments, conducted in both simulated and real-world environments, validate the effectiveness of our approach for micro-mobility path planning using only perception data.

Investigation of printing turn angle effects on structural deformation and stress in selective laser melting

Additive manufacturing (AM) technology facilitates the creation of complex structures, where the printing path significantly impacts thermal distribution, subsequently influencing stress distribution and structural deformation. The primary challenge in path planning is to determine a printing turn angle that ensures uniform thermal distribution, thereby minimizing structural deformation while maintaining printing efficiency. To address this issue, we propose a composite function, which comprehensively characterizes the effects of the printing turn angle and the length of the printing path on the printing results. Combining a specific cubic porous structure, we calculate the maximum (Pmax) and minimum (Pmin) values of the composite function P, and compare the structural deformation and stress of the Pmax and Pmin paths with those of the typical Pzigzag path. Finite element method (FEM) simulation and experimental validation show that the Pmax path achieves significantly lower structural deformation and residual stress compared to the Pzigzag path and Pmin path.

Design and Development Path Planning of Unmanned Aerial Vehicles for Stealth Missions in Dangerous Environments

Design and Development Path Planning of Unmanned Aerial Vehicles for Stealth Missions in Dangerous Environments

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Adaptive Deep Ant Colony Optimization–Asymmetric Strategy Network Twin Delayed Deep Deterministic Policy Gradient Algorithm: Path Planning for Mobile Robots in Dynamic Environments

Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynamic environments. Addressing these challenges, this study introduces an adaptive deep ant colony optimization (ADACO) algorithm, which significantly improves efficiency and convergence speed through enhanced pheromone diffusion mechanisms and updating strategies, applied to global path planning. To adapt to dynamically changing environments and achieve more precise local path planning, an asymmetric strategy network TD3 algorithm (ATD3) is further proposed, which utilizes global path planning information within the strategy network only, creating a new hierarchical path planning algorithm—ADACO-ATD3. Simulation experiments demonstrate that the proposed algorithm significantly outperforms in terms of path length and number of iterations, effectively enhancing the mobile robot’s path planning performance in complex dynamic environments.