Real-time Path Research Articles

Underground rescue path planning based on a comprehensive risk assessment approach

Fire incidents in underground environments, such as subway stations and shopping malls, pose significant hazards due to restricted ventilation and confined spaces. These conditions complicate rescue operations, particularly given the unpredictable nature of fires. Effective integration of fire risk assessment into rescue path planning is essential for ensuring both safety and operational efficiency. However, fire risk is inherently complex, varying across both temporal and spatial dimensions, and accurate assessment depends on precise fire situation inference. Despite advancements in fire simulation technologies, inconsistencies in geometric structures between computational units limit seamless integration with path planning models. Consequently, many existing studies rely on simplistic and less reliable linear fire inference models, compromising the safety of rescue operations. This paper addresses these challenges by proposing an underground rescue path planning method based on a comprehensive fire risk assessment, aimed at enhancing both safety and operational efficiency. A fire risk assessment approach, driven by fire situation inference, is introduced, employing a novel grid-matching transformation to capture the spatio-temporal dynamics of fire conditions using high-precision simulation software. Additionally, an improved A* algorithm is developed for real-time rescue path optimization, minimizing path risk based on the results of the risk assessment. The proposed method is validated through a detailed case study, demonstrating its effectiveness and reliability.

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.

Essential vs Removable Bath Anharmonicity: Path Integral Results with Model Electronic-Vibrational Baths.

Fully quantum mechanical real-time path integral calculations on a two-level system (TLS) interacting with a simple or composite anharmonic bath are presented. The bath consists of a few or many units, each comprising two or three electronic states coupled to secondary harmonic vibrational baths. The primitive harmonic bath approximation leads to qualitatively wrong system dynamics at high temperatures, while the effective harmonic bath mapping becomes exact in the macroscopic bath limit. Significant deviations are observed when the number of bath units is small, including skewing and blue shifts in the population oscillations and enhanced or suppressed coherence. These effects reflect the essential anharmonic character of the bath and cannot be captured by harmonic bath treatments.

Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm

The artificial potential field algorithm has been widely applied to mobile robots and robotic arms due to its advantage of enabling simple and efficient path planning in unknown environments. However, solving the local minimum problem is an essential task and is still being studied. Among current methods, the technique using the virtual hill concept is reliable and suitable for real-time path planning because it does not create a new local minimum and provides lower complexity. However, in the previous study, the shape of the obstacles was not considered in determining the robot’s direction at the moment it is trapped in a local minimum. For this reason, longer or blocked paths are sometimes selected. In this study, we propose an enhanced virtual hill algorithm to reduce errors in selecting the driving direction and improve the efficiency of robot movement. In the local minimum area, a dead-end algorithm is also proposed that allows the robot to return without entering deeply when it encounters a dead end.

Real-time Path Re-planning to Deal with Dynamic Obstacles Using a Parallel Probabilistic Roadmap

Real-time Path Re-planning to Deal with Dynamic Obstacles Using a Parallel Probabilistic Roadmap

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.

Towards Autonomous Operation of UAVs Using Data-Driven Target Tracking and Dynamic, Distributed Path Planning Methods

A hybrid real-time path planning method has been developed that employs data-driven target UAV trajectory tracking methods. It aims to autonomously manage the distributed operation of multiple UAVs in dynamically changing environments. The target tracking methods include a Gaussian mixture model, a long short-term memory network, and extended Kalman filters with pre-specified motion models. Real-time vehicle-to-vehicle communication is assumed through a cloud-based system, enabling virtual, dynamic local networks to facilitate the high demand of vehicles in airspace. The method generates optimal paths by adaptively employing the dynamic A* algorithm and the artificial potential field method, with minimum snap trajectory smoothing to enhance path trackability during real flights. For validation, software-in-the-loop testing is performed in a dynamic environment composed of multiple quadrotors. The results demonstrate the framework’s ability to generate real-time, collision-free flight paths at low computational costs.

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.

Light Path Guided Culling for Hybrid Real-Time Path Tracing

Rendering visually convincing images requires realistic lighting. Path tracing has long been used in offline rendering to produce photorealistic images. While recent hardware advancements allow ray tracing methods to be employed in real-time renderers, they come with a significant performance and memory impact. Real-time path tracing remains a challenge. We present light path guided culling (LiPaC), a novel culling algorithm for ray tracing that achieves almost optimal culling results by considering the number of light paths encountered by objects. In addition, we describe a hybrid path tracing pipeline using LiPaC to render large and highly dynamic scenes in real-time on the current generation of consumer hardware.

Multi-Unmanned Aerial Vehicle-Assisted Flood Navigation of Waterborne Vehicles Using Deep Reinforcement Learning

Abstract During disasters, such as floods, it is crucial to get real-time ground information for planning rescue and response operations. With the advent of technology, unmanned aerial vehicles (UAVs) are being deployed for real-time path planning to provide support to evacuation teams. However, their dependency on expert human pilots for command and control limits their operational capacity to the line-of-sight range. In this article, we utilize a deep reinforcement learning algorithm to autonomously control multiple UAVs for area coverage. The objective is to identify serviceable paths for safe navigation of waterborne evacuation vehicles (WBVs) to reach critical location(s) during floods. The UAVs are tasked to capture the obstacle-related data and identify shallow water regions for unrestricted motion of the WBV(s). The data gathered by UAVs is used by the minimum expansion A* (MEA*) algorithm for path planning to assist WBV(s). MEA* addresses the node expansion issue with the standard A* algorithm, by pruning the unserviceable nodes/locations based on the captured information, hence expediting the path planning process. The proposed approach, MEA*MADDPG, is compared with other prevalent techniques from the literature over simulated flood environments with moving obstacles. The results highlight the significance of the proposed model as it outperforms other techniques when compared over various performance metrics.

Real-time path planning for autonomous vehicle off-road driving.

Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others. In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning. The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Real-Time Path Guiding Using Bounding Voxel Sampling

We propose a real-time path guiding method, Voxel Path Guiding (VXPG), that significantly improves fitting efficiency under limited sampling budget. Our key idea is to use a spatial irradiance voxel data structure across all shading points to guide the location of path vertices. For each frame, we first populate the voxel data structure with irradiance and geometry information. To sample from the data structure for a shading point, we need to select a voxel with high contribution to that point. To importance sample the voxels while taking visibility into consideration, we adapt techniques from offline many-lights rendering by clustering pairs of shading points and voxels. Finally, we unbiasedly sample within the selected voxel while taking the geometry inside into consideration. Our experiments show that VXPG achieves significantly lower perceptual error compared to other real-time path guiding and virtual point light methods under equal-time comparison. Furthermore, our method does not rely on temporal information, but can be used together with other temporal reuse sampling techniques such as ReSTIR to further improve sampling efficiency.

Real-Time Path Planning for Unmanned Aerial Vehicles Based on Compensated Voronoi Diagram

Real-Time Path Planning for Unmanned Aerial Vehicles Based on Compensated Voronoi Diagram

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.

Correction Redistribution Mechanism Based on Forward-Reverse Solutions and Real-Time Path Dynamic Adaptive Re-Planning for Multi-AUVs Collaborative Search

Correction Redistribution Mechanism Based on Forward-Reverse Solutions and Real-Time Path Dynamic Adaptive Re-Planning for Multi-AUVs Collaborative Search

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

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

A hybrid task-constrained motion planning for collaborative robots in intelligent remanufacturing

Industrial manipulators have extensively collaborated with human operators to execute tasks, e.g., disassembly of end-of-use products, in intelligent remanufacturing. A safety task execution requires real-time path planning for the manipulator’s end-effector to autonomously avoid human operators. This is even more challenging when the end-effector needs to follow a planned path while avoiding the collision between the manipulator body and human operators, which is usually computationally expensive and limits real-time application. This paper proposes an efficient hybrid motion planning algorithm that consists of A∗ algorithm and an online manipulator reconfiguration mechanism (OMRM) to tackle such challenges in task and configuration spaces, respectively. A∗ algorithm is first leveraged to plan the shortest collision-free path of the end-effector in task space. When the manipulator body is risky to the human operator, our OMRM then selects an alternative joint configuration with minimum reconfiguration effort from a database to assist the manipulator to follow the planned path and avoid the human operator simultaneously. The database of manipulator reconfiguration establishes the relationship between the task and configuration space offline using forward kinematics, and is able to provide multiple reconfiguration candidates for a desired end-effector’s position. The proposed new hybrid algorithm plans safe manipulator motion during the whole task execution. Extensive numerical and experimental studies, as well as comparison studies between the proposed one and the state-of-the-art ones, have been conducted to validate the proposed motion planning algorithm.

Real-time Neural Appearance Models

We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior—transformation of directions into learned shading frames—facilitates accurate reconstruction of mesoscale effects. The second prior—a microfacet sampling distribution—allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a compact unified neural representation. By exposing hardware accelerated tensor operations to ray tracing shaders, we show that it is possible to inline and execute the neural decoders efficiently inside a real-time path tracer. We analyze scalability with increasing number of neural materials and propose to improve performance using code optimized for coherent and divergent execution. Our neural material shaders can be over an order of magnitude faster than non-neural layered materials. This opens up the door for using film-quality visuals in real-time applications such as games and live previews.

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.

Path integrals, complex probabilities and the discrete Weyl representation

A discrete formulation of the real-time path integral as the expectation value of a functional of paths with respect to a complex probability on a sample space of discrete valued paths is explored. The formulation in terms of complex probabilities is motivated by a recent reinterpretation of the real-time path integral as the expectation value of a potential functional with respect to a complex probability distribution on cylinder sets of paths. The discrete formulation in this work is based on a discrete version of the Weyl algebra that can be applied to any observable with a finite number of outcomes. The origin of the complex probability in this work is the completeness relation. In the discrete formulation the complex probability exactly factors into products of conditional probabilities and exact unitarity is maintained at each level of approximation. The approximation of infinite dimensional quantum systems by discrete systems is discussed. The method is illustrated by applying it to scattering theory and quantum field theory. The implications of these applications for quantum computing is discussed.