Real-time Path Planning Research Articles

Design of Intelligent Firefighting and Smart Escape Route Planning System Based on Improved Ant Colony Algorithm.

Due to the lack of real-time planning for fire escape routes in large buildings, the current route planning methods fail to adequately consider factors related to the fire situation. This study introduces a real-time fire monitoring and dynamic path planning system based on an improved ant colony algorithm, comprising a hierarchical arrangement of upper and lower computing units. The lower unit employs an array of sensors to collect environmental data in real time, which is subsequently transmitted to an upper-level computer equipped with LabVIEW. Following a comprehensive data analysis, pertinent visualizations are presented. Capitalizing on the acquired fire situational awareness, a propagation model for fire spreading is developed. An enhanced ant colony algorithm is then deployed to calculate and plan escape routes by introducing a fire spread model to enhance the accuracy of escape route planning and incorporating the A* algorithm to improve the convergence speed of the ant colony algorithm. In response to potential anomalies in sensor data under elevated temperature conditions, a correction model for data integrity is proposed. The real-time depiction of escape routes is facilitated through the integration of LabVIEW2018 and MATLAB2023a, ensuring the dependability and safety of the path planning process. Empirical results demonstrate the system's capability to perform real-time fire surveillance coupled with efficient escape route planning. When benchmarked against the traditional ant colony algorithm, the refined version exhibits expedited convergence, augmented real-time performance, and effectuates an average reduction of 17.1% in the length of the escape trajectory. Such advancements contribute significantly to enhancing evacuation efficiency and minimizing potential casualties.

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

A Design of Three-Dimensional Spatial Path Planning Algorithm Based on Vector Field Histogram.

In this paper, we present a novel three-dimensional spatial path planning algorithm based on the Vector Field Histogram* (VFH*) approach, specifically tailored for underwater robotics applications. Our method leverages the strengths of VFH* in obstacle avoidance while enhancing its capability to handle complex three-dimensional environments. Through extensive simulations, we demonstrate the superior performance of our algorithm compared to traditional methods, such as RS-RRT algorithm. Our results show significant improvements in terms of computational efficiency and path optimality, making it a viable solution for real-time path planning in dynamic underwater environments.

Dynamic path planning of autonomous bulldozers using activity-value-optimised bio-inspired neural networks and adaptive cell decomposition

Autonomous bulldozers encounter critical challenges in dynamic path planning in complex construction environments such as dynamic obstacles and narrow passages. Bio-inspired neural networks (BINN) are commonly employed for real-time path planning in dynamic environments. However, the algorithm is not feasible enough to calculate the positive input of neighbour cells, thus resulting in unreasonable distribution of activity values in the obstacle-dense regions. Additionally, the use of regular grids results in excessive computation. These drawbacks cause the algorithm to plan irrational paths and reduce computational efficiency. This study proposed an activity-value-optimised BINN and adaptive cell decomposition for the dynamic path planning of autonomous bulldozers. First, an adaptive cell decomposition method was proposed to model a highly intricate and constantly evolving environment, resulting in a reduction in the computational workload of the BINN. Second, the calculation of the activity value is optimised by enhancing the shunt equation of the BINN, which considers both obstacles and grid levels. This improvement enabled more reasonable distribution results. The smoothness of dynamic path planning is refined by incorporating the turning radius, which can be more effectively integrated with local path planning. Compared with the original BINN algorithm and other baseline algorithms, a practical large-scale hydropower project application demonstrated that the proposed method can effectively reduce the number of computational iterations, enrich the travel direction, and improve path quality while ensuring dynamic obstacle avoidance.

Real-time autonomous path planning for dynamic wildfire monitoring with uneven importance

Real-time autonomous path planning for dynamic wildfire monitoring with uneven importance

Real-time local path planning strategy based on deep distributional reinforcement learning

Local path planning and obstacle avoidance in complex environments are two challenging problems in the research of intelligent robots. In this study, we develop a novel approach grounded in deep distributional reinforcement learning to address these challenges. Within this methodology, agents instantiated by deep neural networks perceive real-time local environmental information through sensor data, addressing inherent stochasticity and local path planning tasks in complex environments. End-to-end training is facilitated via distributional reinforcement learning algorithms and reward functions informed by heuristic knowledge. Optimal actions for path planning are determined through return value distributions. Finally, the simulation results show that the success rate of the proposed distributed algorithm is 98% in a random environment and 94% in a dynamic environment. This proves that the algorithm has better generalization and flexibility than the non-distributed algorithm.

A Lightweight Reinforcement-Learning-Based Real-Time Path-Planning Method for Unmanned Aerial Vehicles

A Lightweight Reinforcement-Learning-Based Real-Time Path-Planning Method for Unmanned Aerial Vehicles

UAV Multi-Dynamic Target Interception: A Hybrid Intelligent Method Using Deep Reinforcement Learning and Fuzzy Logic

With the rapid development of Artificial Intelligence, AI-enabled Uncrewed Aerial Vehicles have garnered extensive attention since they offer an accessible and cost-effective solution for executing tasks in unknown or complex environments. However, developing secure and effective AI-based algorithms that empower agents to learn, adapt, and make precise decisions in dynamic situations continues to be an intriguing area of study. This paper proposes a hybrid intelligent control framework that integrates an enhanced Soft Actor–Critic method with a fuzzy inference system, incorporating pre-defined expert experience to streamline the learning process. Additionally, several practical algorithms and approaches within this control system are developed. With the synergy of these innovations, the proposed method achieves effective real-time path planning in unpredictable environments under a model-free setting. Crucially, it addresses two significant challenges in RL: dynamic-environment problems and multi-target problems. Diverse scenarios incorporating actual UAV dynamics were designed and simulated to validate the performance in tracking multiple mobile intruder aircraft. A comprehensive analysis and comparison of methods relying solely on RL and other influencing factors, as well as a controller feasibility assessment for real-world flight tests, are conducted, highlighting the advantages of the proposed hybrid architecture. Overall, this research advances the development of AI-driven approaches for UAV safe autonomous navigation under demanding airspace conditions and provides a viable learning-based control solution for different types of robots.

An efficient method for modeling and evaluating the bench terrain of open-pit mines

In order to quantitatively analyze the roughness of the bench floor during open-pit mine blasting, this study proposes a real-time measuring method for the three-dimensional terrain of the bench floor during the excavation process. Real-time monitoring is conducted at the boundary and discrete internal points of the workbench floor during electric shovel operation, utilizing real-time kinematic global navigation satellite system (RTK-GNSS) positioning technology. An improved convex hull algorithm is introduced to automatically extract the optimal boundary of discrete point clouds based on their spatial distribution characteristics. This study establishes a digital elevation model (DEM) using five interpolation algorithms for 3D terrain visualization simulation. Through cross-validation, a comparative analysis of the DEM accuracy, the simulation results of the ordinary kriging interpolation algorithm were found to be optimized. The optimized interpolation algorithm is applied to simulate the 3D terrain in the Dexing open-pit copper mine, and the relevant terrain parameters were calculated. This dataset can serve as a precise foundation for the real-time path planning of elevation blasting design and ground leveling operations.

Accessibility and Equity Analysis of Highway-Railway Traffic Network Based on Real-Time Route Planning Data: A Case Study of Shandong Peninsula Urban Agglomeration, China

As a bond of geospatial and socioeconomic ties, transport supports the development of regions. With the rapid development of the economy, the coordinated development of regional transport has become a concern. To narrow the gap in transport development between cities and achieve sustainable development in regional transport, this study took the Shandong Peninsula urban agglomeration as the research object and using real-time path planning data assessed accessibility of the traffic network under two travel scenarios: cars and car-connected train. Based on the results of accessibility calculations, the Gini coefficient and Theil index were used to measure the equity of regional traffic networks. The results showed that (1) overall accessibility under the two travel scenarios showed a pattern of gradual decay from the center to the periphery; (2) both the accessibility and equity of the highway network were better than those of the railway network; (3) differences in regional development were the main sources of overall regional transport inequity. The research results provide a useful reference for the planning and construction of urban agglomeration traffic networks.

Path planning and obstacle avoidance control of UUV based on an enhanced A* algorithm and MPC in dynamic environment

Addressing the challenges of suboptimal path planning and insufficient dynamic obstacle avoidance for Unmanned Underwater Vehicles (UUVs), this paper presents a composite strategy that merges an enhanced A* path planning algorithm with Model Predictive Control (MPC). This dual-faceted approach synthesizes path planning and trajectory tracking control. Firstly, the six-degree-of-freedom kinematic and dynamic model of the UUV is established based on the modeling method of underwater vehicles. Secondly, an enhanced A* algorithm is implemented to generate an optimal reference path for the UUV within a three-dimensional environment. Subsequently, MPC is employed for trajectory tracking control. When encountering unforeseen dynamic obstacles on the reference path, the system initiates a real-time dynamic re-planning process, modifying the trajectory to circumvent obstacles while optimizing the objective function to guarantee the UUV's safe passage and accurate arrival at the intended destination. The simulation results prove the efficacy of this integrated method, demonstrating notable enhancements in the UUV's capacity for dynamic obstacle avoidance and the execution of real-time path planning.

A real-time digital twin for active safety in an aircraft hangar

The aerospace industry prioritises safety protocols to prevent accidents that can result in injuries, fatalities, or aircraft damage. One of the potential hazards that can occur while manoeuvring aircraft in and out of a hangar is collisions with other aircraft or buildings, which can lead to operational disruption and costly repairs. To tackle this issue, we have developed the Smart Hangar project, which aims to alert personnel of increased risks and prevent incidents from happening. The Smart Hangar project uses computer vision, LiDAR, and ultra-wideband sensors to track all objects and individuals within the hangar space. These data inputs are combined to form a real-time 3D Digital Twin (DT) of the hangar environment. The Active Safety system then uses the DT to perform real-time path planning, collision prediction, and safety alerts for tow truck drivers and hangar personnel. This paper provides a detailed overview of the system architecture, including the technologies used, and highlights the system’s performance. By implementing this system, we aim to reduce the risk of accidents in the aerospace industry and increase safety for all personnel involved. Additionally, we identify future research directions for the Smart Hangar project.

Research on AUV dynamic obstacle avoidance planning based on rolling speed obstacle method

In this paper, aiming at the problem of poor path planning and obstacle avoidance effect of autonomous underwater vehicle (AUV) in a dynamic environment, a feasible rolling speed obstacle method is proposed. This method combines the rolling window method with the speed obstacle method, and designs a suitable three-dimensional model predictive controller based on the rolling window method under a hybrid obstacle avoidance structure, and achieves stable tracking of the reference path by optimizing the objective function. A three-dimensional collision cone and speed obstacle cone model is constructed while the window is rolling. If the collision avoidance condition is met, the critical collision point is calculated, and the AUV is guided to avoid obstacles safely by tracking the critical collision point; if collision avoidance ends, guide AUV trajectory recovery. The final simulation and experimental results show that the performance of the rolling speed obstacle method in avoiding dynamic obstacles is 30% higher than the rolling window method and 40% higher than the speed obstacle method. The method used in this paper can effectively improve the dynamic obstacle avoidance ability of AUV in real-time path planning.

A review of unmanned aerial vehicle path planning techniques

The paper offers an exhaustive scholarly review of the algorithms and techniques employed in Unmanned Aerial Vehicle (UAV) path planning, categorizing these methodologies based on spatial dimensions, planning steps, and the nature of planning maps. It provides a critical evaluation of a plethora of algorithms, including random search methods, particle swarm algorithms, genetic algorithms, and A* algorithms, among others. The study elucidates the advantages and limitations of each algorithm, with a particular focus on their efficacy in real-time planning and navigation within complex three-dimensional environments. It underscores that while the domain of pre-flight path planning has reached a level of relative maturity, there exists a conspicuous gap in the literature concerning real-time obstacle avoidance and optimal path planning, particularly when constrained by limited computational resources. The paper thus serves as both a comprehensive review and a call for further research aimed at addressing these identified lacunae to ensure the generation of safe and feasible UAV trajectories.

3D real-time dynamic path planning for UAV based on improved interfered fluid dynamical system and artificial neural network

In complex and volatile unknown flight environments, the limited environmental information obtained by sensors in the face of sudden dynamic and static obstacles makes it extremely challenging for unmanned aerial vehicles (UAVs) to obtain a safe and efficient path to avoid obstacles and reach a designated target point. Therefore, a real-time dynamic path planning method based on an improved interfered fluid dynamical system (IFDS) and artificial neural network (ANN) is proposed to enhance path quality and computational efficiency. Firstly, to address the issue of insufficient sample quality and quantity, IFDS is employed as the fundamental method for path planning to simulate and generate an adequate amount of sample data for the ANN training. Then, an enhanced sand cat swarm optimization algorithm (ESCSO) with an adaptive social neighborhood search mechanism and Lévy flight strategy is proposed to improve the sample quality. Secondly, the information between the UAV and the target points and obstacles is extracted from the sample data as the input for the network, the parameters of the IFDS are used as the feature extraction at the output of the network, and the ESCSO is applied to optimize the weights and biases of the ANN, enabling offline training of the neural network. Finally, the trained neural network is utilized to dynamically output IFDS parameters based on the real-time environmental information obtained from the sensors, enabling the generation of real-time obstacle avoidance paths. Experimental results in a series of complex simulated environments demonstrate that the proposed method outperforms other algorithms in terms of path quality and meets real-time requirements. It provides excellent obstacle avoidance characteristics for the UAV.

An Obstacle Avoidance Path Planning and Evaluation Method for Intelligent Vehicles Based on the B-Spline Algorithm.

To meet the real-time path planning requirements of intelligent vehicles in dynamic traffic scenarios, a path planning and evaluation method is proposed in this paper. Firstly, based on the B-spline algorithm and four-stage lane-changing theory, an obstacle avoidance path planning algorithm framework is constructed. Then, to obtain the optimal real-time path, a comprehensive real-time path evaluation mechanism that includes path safety, smoothness, and comfort is established. Finally, to verify the proposed approach, co-simulation and real vehicle testing are conducted. In the dynamic obstacle avoidance scenario simulation, the lateral acceleration, yaw angle, yaw rate, and roll angle fluctuation ranges of the ego-vehicle are ±2.39 m/s2, ±13.31°, ±13.26°/s, and ±0.938°, respectively. The results show that the proposed algorithm can generate real-time, available obstacle avoidance paths. And the proposed evaluation mechanism can find the optimal path for the current scenario.

Real-time path planning based on improved artificial potential field method

Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.

Role-Based User Allocation Driven by Criticality in Edge Computing

Edge computing is a promising solution to enabling highly accessible resources and latency-sensitive services for nearby users. In public safety, it can provide critical support for urban crowd/hazard management services, such as real-time path planning, hazard warning, etc. In a crowd/hazard scenario, crowds can be allocated to nearby edge servers for obtaining real-time support, e.g., evacuation instructions for those who want to evacuate and crowd flow updates for those who want to rescue, etc. In such scenarios, the behaviors of different roles (like rescuers and evacuees) and the positive/negative interactions among them must be considered in user allocation for reducing injuries and fatalities. In this paper, these issues are defined as a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role-Based Criticality</i> (RBC) model to describe the fatal risks of different roles in the crowd/hazard scenarios. Based on the model, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role-Based User Allocation</i> (RUA) problem is formulated. To tackle this problem, we devise an optimal solution named RUA-ILP based on Integer Linear Programming. To accommodate large-scale scenarios, we propose two representative approximate approach named RUA-A and RUA-GA to ensure efficient and effectiveness user allocation respectively. They can maximize the overall role-based criticality which can reduce injuries and fatalities in crowd/hazard scenarios by theoretical proofing and extensive experiments conducted on a real-world dataset.

Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths

Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths

An obstacle avoidance approach for UAV path planning

The recent pandemic of COVID-19 has proven to be a test case for Unmanned Aerial Vehicles (UAVs). UAVs have shown great potential for plenty of applications in the face of this pandemic, but their scope of applications becomes limited due to the dependency on ground pilots. Irrespective of the application, it is imperative to have an autonomous path planning to utilize UAVs to their full potential. Collision-free trajectories are expected from the path planning process to ensure the safety of UAVs and humans on the ground. This work proposes a path planning technique where collision avoidance is mathematically proven under an uncertainty prerequisite, that the UAV follows its requested moving position within some threshold distance. This scheme ensures UAV safety by considering the underlying control’s system overshoots. Obstacles play a guiding role in selecting collision-free trajectories. These obstacles are modeled as rectangular shapes with interest points defined around their corners. These points further define collision-free permissible edges, and later we apply the Dijkstra algorithm to these edges before having the desired trajectory. Regardless of the size of deployment area, our proposed scheme incurs low computational load due to the dependency on pre-defined interest points only thereby making it suitable for real-time path planning. Simulation results obtained using MATLAB’s UAV Toolbox show that the proposed method succeeds in getting short collision-free trajectories.