Obstacle Scenarios Research Articles

Obstacle avoidance path planning for AUVs in a three-dimensional unknown environment based on the C-APF-TD3 algorithm

To enhance the obstacle avoidance path planning ability of AUV in three-dimensional unknown underwater environments with obstacle constraints, an improved algorithm combining Constrained Artificial Potential Field (C-APF) and the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3) is proposed (C-APF-TD3). First, the kinematic constraints of AUV are incorporated into the APF algorithm, allowing C-APF to generate an approximate path for the AUV. Then, the AUV obstacle avoidance path planning problem is formulated as a Markov Decision Process (MDP), designing state space, action space, and reward functions. The TD3 training is guided by the approximate path planned using C-APF, ultimately resulting in a policy model for AUV path planning. Finally, various simulation experiments are established and designed for different obstacle scenarios. The experimental results show that the C-APF-TD3 algorithm produces more optimized trajectories compared to the C-APF algorithm. Furthermore, compared to the C-APF-DDPG algorithm, it achieves a policy model with efficient control performance with higher convergence efficiency and average returns, enhancing the adaptability and robustness of AUV obstacle avoidance path planning in three-dimensional unknown environments.

Research on Real-Time Roundup and Dynamic Allocation Methods for Multi-Dynamic Target Unmanned Aerial Vehicles.

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation.

Enhancing RODNet detection in complex road environments based on ESM and ISM methods

In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.

Safe path planning of mobile robot based on improved particle swarm optimization

Path planning is a fundamental aspect of mobile robot navigation, playing a crucial role in enabling robots to autonomously navigate while avoiding obstacles. Nevertheless, traditional path planning algorithms face navigation challenges, including obstacle avoidance and the potential for getting stuck in local minima or deadlocks along the path. To tackle these challenges, the study proposes an enhanced path planning method based on control barrier function (CBF). This approach introduces a safety velocity adjustment mechanism based on CBF and combines it with the particle swarm optimization (PSO), adjusting the safe speed in global planning and addressing the issue of local minima. Experimental simulations are conducted to validate the flexibility and global optimization performance of the proposed path planning method across various obstacle scenarios.

Collision Avoidance Path Planning and Tracking Control for Autonomous Vehicles Based on Model Predictive Control.

In response to the fact that autonomous vehicles cannot avoid obstacles by emergency braking alone, this paper proposes an active collision avoidance method for autonomous vehicles based on model predictive control (MPC). The method includes trajectory tracking, adaptive cruise control (ACC), and active obstacle avoidance under high vehicle speed. Firstly, an MPC-based trajectory tracking controller is designed based on the vehicle dynamics model. Then, the MPC was combined with ACC to design the control strategies for vehicle braking to avoid collisions. Additionally, active steering for collision avoidance was developed based on the safety distance model. Finally, considering the distance between the vehicle and the obstacle and the relative speed, an obstacle avoidance function is constructed. A path planning controller based on nonlinear model predictive control (NMPC) is designed. In addition, the alternating direction multiplier method (ADMM) is used to accelerate the solution process and further ensure the safety of the obstacle avoidance process. The proposed algorithm is tested on the Simulink and CarSim co-simulation platform in both static and dynamic obstacle scenarios. Results show that the method effectively achieves collision avoidance through braking. It also demonstrates good stability and robustness in steering to avoid collisions at high speeds. The experiments confirm that the vehicle can return to the desired path after avoiding obstacles, verifying the effectiveness of the algorithm.

Optimizing anti-collision strategy for MASS: A safe reinforcement learning approach to improve maritime traffic safety

Maritime autonomous surface ships (MASS) promise enhanced efficiency, reduced human errors, and to improve maritime traffic safety. However, MASS navigation in complex maritime traffic presents challenges, especially in collision avoidance strategy optimization (CASO). This paper proposes a novel risk-based CASO approach based on safe reinforcement learning (SRL) with a reliability and risk hierarchical critic network (SRL-R2HCN) approach. Key steps in developing the approach start with the formulation of collision risk assessment. This is followed by the construction of a hierarchical network structure, supplemented by the supporting reward function, multi-objective function, and reliability measurement to realize the SRL-R2HCN. Finally, simulation experiments are conducted in mixed obstacle scenarios, and the results are compared with traditional algorithms to showcase the advancement and fidelity of the new SRL-R2HCN method. The results demonstrate that the proposed method can accurately assess collision risks in mixed obstacle scenarios and generate safe, efficient, and reliable collision avoidance strategies. The outcomes of this research provide a sound theoretical basis for the future development of MASS navigation safety and significant potential to improve the safe and efficient operations of MASS. Furthermore, the methodology could also benefit maritime transportation and shipping management.

Three-dimensional unmanned aerial vehicle path planning utilizing artificial gorilla troops optimizer incorporating combined mutation and quadratic interpolation operators

In real terrain and dynamic obstacle scenarios, the complexity of the 3D UAV path planning problem greatly increases. Thus, to procure the optimal flight path for UAVs in such scenarios, an augmented Artificial Gorilla Troops Optimizer, denoted as OQMGTO, is proposed. The proposed OQMGTO algorithm introduces three strategies: combination mutation, quadratic interpolation, and random opposition-based learning, aiming to enhance the ability to timely escape from local optimal path areas and rapidly converge to the global optimal path. Given the flight distance, smoothness, terrain collision, and other five realistic factors of UAVs, specific constraint conditions are proposed to address complex scenarios, aiming to construct a path planning model. By optimizing this model, OQMGTO algorithm solves the path planning problem in complex scenarios. The extensive validation of OQMGTO algorithm on CEC2017 test suite enhances its credibility as a powerful optimization tool. Comparison experiments are conducted in simulated terrain scenarios, including six multi-obstacle terrain scenarios and three dynamic obstacle scenarios. The experimental findings validate OOMGTO algorithm can assist UAV in searching for excellent flight paths, featuring high safety and reliability characteristics, which confirms the superiority of OOMGTO algorithm for path planning in simulated terrain scenarios. Furthermore, in four flight missions carried out in real terrains, OQMGTO algorithm demonstrates superior search performance, planning smooth trajectories without mountain collision.

Generalized Labeled Multi-Bernoulli Filter-Based Passive Localization and Tracking of Radiation Sources Carried by Unmanned Aerial Vehicles

This paper discusses a key technique for passive localization and tracking of radiation sources, which obtains the motion trajectory of radiation sources carried by unmanned aerial vehicles (UAVs) by continuously or periodically localizing it without the active participation of the radiation sources. However, the existing methods have some limitations in complex signal environments and non-stationary wireless propagation that impact the accuracy of localization and tracking. To address these challenges, this paper extends the δ-generalized labeled multi-Bernoulli (GLMB) filter to the scenario of passive localization and tracking based on the random finite-set (RFS) framework and provides the extended Kalman filter (EKF) and unscented Kalman filter (UKF) implementations of the δ-GLMB filter, which fully take into account the nonlinear motion of the radiation source. By modeling the “obstacle scenario” and the influence of external factors (e.g., weather, terrain), our proposed GLMB filter can accurately track the target and capture its motion trajectory. Simulation results verify the effectiveness of the GLMB filter in target identification and state tracking.

Algorithm Selection and Application for Robot Path Planning Problems

This article primarily delves into the investigation of robotic path planning in the presence of obstacles, aiming to ascertain optimal traversal routes. It conducts a thorough categorization and discussion of two critical aspects: the ascertainability of the overall map and the regularity of obstacles. In addressing this inquiry, two obstacle scenarios, namely regular and irregular, are posited. The article scrutinizes the path planning and obstacle circumvention techniques for both scenarios. Regarding regular obstacles, a comparative assessment of the A* algorithm, Floyd’s algorithm, and Dijkstra’s algorithm was conducted, culminating in the selection of the A* algorithm for its superior efficiency. For irregular obstacles, the article proposes a pre-processing approach involving the utilization of Matlab’s iterative pixel point traversal to assess obstacle proportions within nodes, subsequently converting irregular obstacles into a regularized format. To summarize, in scenarios with a known overall map, the article advocates employing the A* algorithm for proficient path planning. In situations where the map is undisclosed but obstacles exhibit regularity, the D* algorithm is recommended. For instances involving irregular obstacles and an undisclosed map, a dynamic method for handling newly incorporated nodes into the map is proposed. This article presents tailored solutions for robotic path planning across diverse conditions, offering concise yet effective problem-solving strategies. In conclusion, this study offers a comprehensive array of solutions for robot path planning in obstructed environments, enabling the selection of apt methodologies based on varying conditions and exigencies.

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.

Energy-Efficient Speed Planning for Autonomous Driving in Dynamic Traffic Scenarios

In the field of autonomous driving, velocity planning is of paramount importance for handling dynamic obstacle scenarios. To avoid unnecessary acceleration and deceleration, self-driving vehicles need to find an energy-optimized velocity trajectory. Moreover, in complex traffic environments, the vehicle trajectory must consider the spatio-temporal coupling problem to avoid unrealistic driving paths. To address these challenges, this paper proposes a hierarchical planner that first plans the path and then performs speed planning based on the already planned path. Specifically, we focus on the energy consumption factor and use dynamic programming for speed planning while combining safety and comfort considerations. The optimal energy-saving trajectory is obtained by combining the speed profile with the optimal path. To cope with complex scenarios on real roads, we propose an adaptive trajectory adjustment strategy based on model predictive control to track by adaptively selecting tracking modes. Finally, hardware-in-the-loop experimental validation demonstrates that our proposed method significantly reduces energy consumption compared with the traditional decoupling method while ensuring that the autonomous vehicle adapts well to complex traffic scenarios.

TurtleBot Operation with Barrier Bypass and Point Navigation Functionality

In the quest for a simple, efficient, and cost-effective method for autonomous robot navigation, it is crucial to develop a reliable obstacle avoidance system. This project explores how obstacles affect navigation and the time required for the robot to manoeuvre around them. An obstacle avoidance method that relies solely on the LIDAR as the primary sensor is proposed, which is both cheap to manufacture and easy to install. TurtleBot3 is used for experimentation, as it is easy to work with and allows for quick adjustments to the algorithm. The approach involves an optimized algorithm designed to navigate various obstacle scenarios effectively as well as multiple points considered to be target positions. By leveraging LIDAR data for obstacle detection, the algorithm addresses diverse obstacle situations. Experimental results demonstrate the efficiency of the method in navigating through different barrier scenarios and different coordinates.

On the use of the spectral element method for the modeling of fluid–structure interaction problems

This study addresses a fluid–structure interaction problem that models flow in a channel. Simulations were conducted to investigate the method's effectiveness when applied to real obstacle scenarios, where the obstacle is explicitly represented within the channel. To tackle the Navier–Stokes equations, we utilized the spectral–Fourier–asymptotic approach, which is a mesh-free method that combines Chebyshev polynomials and Fourier series with the asymptotic method based on power series.

Research on the local path planning of an orchard mower based on safe corridor and quadratic programming.

Path planning algorithms are challenging to implement with mobile robots in orchards due to kinematic constraints and unstructured environments with narrow and irregularly distributed obstacles. To address these challenges and ensure operational safety, a local path planning method for orchard mowers is proposed in this study. This method accounts for the structural characteristics of the mowing operation route and utilizes a path-velocity decoupling method for local planning based on following the global reference operation route, which includes two innovations. First, a depth-first search method is used to quickly construct safe corridors and determine the detour direction, providing a convex space for the optimization algorithm. Second, we introduce piecewise jerk and curvature restriction into quadratic programming to ensure high-order continuity and curvature feasibility of the path, which reduce the difficulty of tracking control. We present a simulation and real-world evaluation of the proposed method. The results of this approach implemented in an orchard environment show that in the detouring static obstacle scenario, compared with those of the dynamic lattice method and the improved hybrid A* algorithm, the average curvature of the trajectory of the proposed method is reduced by 2.45 and 3.11 cm -1, respectively; the square of the jerk is reduced by 124 and 436 m 2/s 6, respectively; and the average lateral errors are reduced by 0.55 cm and 4.97 cm, respectively, which significantly improves the path smoothness and facilitates tracking control. To avoid dynamic obstacles while traversing the operation route, the acceleration is varied in the range of -0.21 to 0.09 m/s 2. In the orchard environment, using a search range of 40 m × 5 m and a resolution of 0.1 m, the proposed method has an average computation time of 9.6 ms. This is a significant improvement over the open space planning algorithm and reduces the average time by 12.4 ms compared to that of the dynamic lattice method, which is the same as that of the structured environment planning algorithm. The results show that the proposed method achieves a 129% improvement in algorithmic efficiency when applied to solve the path planning problem of mower operations in an orchard environment and confirm the clear advantages of the proposed method.

基于PSO-A*算法的农业无人机作物喷洒路径规划

Currently, drones have been gradually applied in the field of agriculture, and have been widely used in various types of agricultural aerial operations such as precision sowing, pesticide spraying, and vegetation detection. The use of agricultural UAVs for pesticide spraying has become an important task in the agricultural plant protection process. However, in the crop spraying process of agricultural UAVs, it is necessary to traverse multiple spray points and plan obstacle avoidance paths, which greatly affects the efficiency of agricultural UAV crop spraying operations. To address the above issues, traditional particle swarm optimization (PSO) algorithms have strong solving capabilities, but they are prone to falling into local optima. Therefore, this study proposes an improved PSO algorithm combined with the A* algorithm, which introduces a nonlinear convergence factor balancing algorithm for global search and local development capabilities in the traditional PSO algorithm, and adopts population initialization to enhance population diversity, so that the improved PSO algorithm has stronger model solving capabilities. This study designs two scenarios for agricultural UAV crop spraying path planning: one without obstacles and one with obstacles. Experimental simulation results show that using the PSO algorithm to solve the obstacle-free problem and then using the A* algorithm to correct the path obstructed by obstacles in the obstacle scenario, the agricultural UAV crop spraying trajectory planning based on the PSO-A* algorithm is real and effective. This research can provide theoretical basis for agricultural plant protection and solve the premise of autonomous operation of UAVs.

RDT-RRT: Real-time double-tree rapidly-exploring random tree path planning for autonomous vehicles

The complexity of the environment makes rapidly-exploring random tree (RRT) difficult to handle dynamic obstacle avoidance and system constraint in real-time path planning for autonomous vehicles. To handle this issue, this paper proposes a novel real-time double-tree rapidly-exploring random tree (RDT-RRT) algorithm framework. The collision-free path by RRT after B-spline smooth treatment is adopted as the reference path to reduce invalid sampling. Integrating G1 Hermite interpolation with G2 Hermite interpolation reduces the sampling dimension and takes more efficient samples. The optimal distance metric is designed considering dynamic collision detection mechanism and utilized to estimate the costs of the samples in terms of path curvature. Moreover, to have a better understanding of the environment, convolutional neural network (CNN) is embedded to strengthen the collision detection mechanism. By RDT-RRT the smooth, collision-free paths with small curvature changes can be evaluated. For the evaluations of our proposal in global and local planning, the experiments for a real scaled autonomous vehicle are implemented through parallel computing. By comparing with the mainstream RRT-based algorithms, it has shown that in terms of the path quality, our method reduces 92 % and 88 % of cumulative curvature change respectively in obstacle-free and static obstacle scenarios. Compared with other RRT methods, RDT-RRT performs faster convergence rate and Parallel computing increases the updating frequency from 1.1 Hz to 5.5 Hz. The obstacle avoidance capabilities are also improved.

Dual-game based UAV swarm obstacle avoidance algorithm in multi-narrow type obstacle scenarios

Due to the advantages of rapid deployment, flexible response and strong invulnerability, unmanned aerial vehicle (UAV) swarm has been widely applied in collaborative warfare and emergency communication. However, UAV swarm in complex environments is prone to chaotic collapse due to obstructions. A UAV swarm obstacle avoidance system model for multi-narrow type obstacles is established. Due to the fact that only one UAV is allowed to pass through each small hole at any given moment, addressing the issue of congestion caused by swarming effects becomes crucial in addition to managing the competitive allocation of multiple UAVs to multiple holes. Aiming at this problem, a dual-game real-time obstacle avoidance scheme is proposed for UAV swarm with multi-narrow type obstacle scenarios, which divides the flight process of the UAV swarm into two stages: maintaining the flight state of the UAV swarm unchanged when no obstacles are encountered, and implementing matching separation and motion state switching by means of dual-game strategy when facing multi-narrow type obstacles, ultimately achieving orderly passage after multiple rounds of games. For the proposed scheme, a dual-game based Flocking (DGF) obstacle avoidance algorithm is proposed. Specifically, the motion state of each UAV obtained from the game is parameterized and integrated with the Flocking algorithm to calculate the motion control input for each UAV. The solution is iteratively obtained until the UAV swarm completes the obstacle avoidance. Simulation results demonstrate that the proposed DGF algorithm not only enables smooth obstacle avoidance for the UAV swarm in multi-narrow type obstacle scenarios, but also effectively resolves the internal chaos problem in the UAV swarm, thereby preventing rigid collisions.

Depth-based Sampling and Steering Constraints for Memoryless Local Planners

By utilizing only depth information, the paper introduces a novel two-stage planning approach that enhances computational efficiency and planning performances for memoryless local planners. First, a depth-based sampling technique is proposed to identify and eliminate a specific type of in-collision trajectories among sampled candidates. Specifically, all trajectories that have obscured endpoints are found through querying the depth values and will then be excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. Subsequently, we apply a tailored local planning algorithm that employs a direction cost function and a depth-based steering mechanism to prevent the robot from being trapped in local minima. Our planning algorithm is theoretically proven to be complete in convex obstacle scenarios. To validate the effectiveness of our DEpth-based both Sampling and Steering (DESS) approaches, we conducted experiments in simulated environments where a quadrotor flew through cluttered regions with multiple various-sized obstacles. The experimental results show that DESS significantly reduces computation time in local planning compared to the uniform sampling method, resulting in the planned trajectory with a lower minimized cost. More importantly, our success rates for navigation to different destinations in testing scenarios are improved considerably compared to the fixed-yawing approach.

Hybrid Layer of Improved Interfered Fluid Dynamic System and Nonlinear Model Predictive Control for Navigation and Control of Autonomous Underwater Vehicles

This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists of two layers; the upper layer utilizes the Improved Interfered Fluid Dynamic System (IIFDS) for path planning, while the lower layer employs Nonlinear Model Predictive Control (NMPC) for trajectory tracking. Extensive simulation experiments are conducted to determine optimal parameters for both static and dynamic obstacle scenarios. Additionally, real-world testing is performed using the BlueRov2 platform, incorporating multiple dynamic and static obstacles. The proposed approach achieves real-time control at a frequency of 100 Hz and exhibits impressive path tracking accuracy, with a root mean square (RMS) of 0.02 m. This research provides a valuable framework for navigation and control in practical applications.

Multi-strategy enhanced grey wolf algorithm for obstacle-aware WSNs coverage optimization

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability.