Observability-based local path planning and obstacle avoidance using bearing-only measurements

With the robot gradually stepping into our daily life, more and more attention has been paid to the mobile ability of robot, and obstacle avoidance is a key problem. In this paper, the indoor environment is taken as the application scene, and the visual detection and local dynamic obstacle avoidance are studied respectively. Aiming at the shortcomings of the single external sensor lidar’s incomplete perception of obstacle information, this paper proposes an obstacle avoidance method based on machine vision for mobile obstacle detection under the ROS operating system. The method is improved based on YOLO-v4 in terms of vision, which can meet the real-time requirements of mobile terminal. Combined with dynamic window approach (DWA), the local obstacle avoidance algorithm is improved. In the process of local obstacle avoidance, visual detection information is integrated to increase the ability of local dynamic obstacle avoidance and improve the performance of robot local obstacle avoidance. Finally, the feasibility and validity of the algorithm are verified in the actual environment.

According to the requirements of real-time obstacle avoidance of unmanned vehicles, an improved artificial potential field local obstacle avoidance path planning algorithm is proposed. Considering the influence factors of vehicle kinematics and dynamics, an improved distance adjustment factor is added to the static obstacle potential field function to construct the water drop repulsion potential field, which improves the efficiency of path planning; The relative velocity function and relative acceleration function are added to the potential field function of dynamic obstacles, which solves the problem that the traditional potential field method has no solution under the dynamic obstacle avoidance condition. The Bessel curve is used to smooth the path, and finally a fast, efficient and collision-free optimal path is generated. Using the Prescan-CarSim-Matlab/Simulink joint simulation platform, the effectiveness of the proposed path planning algorithm is verified under the conditions of lane changing obstacle avoidance of static obstacles and deceleration obstacle avoidance of lateral dynamic obstacles. The simulation results show that compared with the traditional potential field method, the change of heading angle obtained by the improved potential field method is reduced by 84% and the stability of dynamic obstacle avoidance is improved.

Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

AGV is an important part of the printing wisdom factory. Navigation technology is one of the key technologies of AGV. Path planning, obstacle avoidance and mapping are the core of the realization of AGV navigation. In order to solve the AGV navigation problem in the printing wisdom factory, this paper studies the mapping and path planning of the AGV in the printing wisdom factory. First, the physical simulation environment of the printing wisdom factory is established based on gazebo. Second, the two-dimensional grid map of the physical simulation environment of the printing wisdom factory is constructed by using the graph optimization SLAM algorithm, and then design a combined navigation scheme combining A* algorithm and Dynamic Window Approach (DWA) was developed. Finally, the simulation results verify the accessibility of global path planning and the feasibility of local path avoidance and obstacle avoidance.

This article handles tracking multiple targets using bearing-only measurements in underwater noisy environments. For tracking multiple targets in underwater noisy environments, the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter provides good performance with its low computational load. Bearing-only measurements are passive and do not provide position information of a target. Note that the nonlinearity of the bearing-only measurements can be handled by Extended Kalman Filters (EKF) when applying the GM-PHD filter. However, range uncertainty of the target is large for bearing-only measurements. Thus, a single EKF leads to poor performance when it is applied in the GM-PHD. In this article, every bearing measurement gives birth to multiple target samples, which are distributed considering the feasible range of the passive sensor. Thereafter, every target sample is updated utilizing the measurement update step of the EKF. In this way, we run multiple EKFs associated to multiple target samples, instead of running a single EKF. To the best of our knowledge, our article is novel in tracking multiple targets in noisy environments, using the observer with bearing-only measurements. The effectiveness of the proposed GM-PHD is verified utilizing MATLAB simulations.

This article focuses on achieving efficient and safe navigation for robots in dynamic and unpredictable environments. We propose a hierarchical path planning framework that integrates global path planning with local dynamic obstacle avoidance. This framework aims to quickly plan a collision-free, shortest, and safest path for the robot while adapting the navigation path according to uncertainties in the operating environment. Global path planning is conducted using the improved gray wolf optimization algorithm (IGWO), trajectory tracking is achieved through a pure pursuit control algorithm, and a dynamic switching mechanism based on deep reinforcement learning (DRL) significantly enhances the navigation performance of the robotic system. The effectiveness of this approach has been verified through simulations and experiments. Simulation results indicate that in global path planning, the IGWO algorithm achieves faster convergence compared to algorithms, such as GWO-MP and GWO-CS. The planned path lengths are reduced by approximately 4.01% and 2.27%, respectively, and the fitness values are decreased by 4.78% and 1.9%, demonstrating superior path planning performance. For local dynamic obstacle avoidance, both single-robot and multirobot systems successfully avoided obstacles in multiple experiments. Finally, physical experiments conducted in various complex scenarios show that both single-robot and multirobot systems can effectively execute global planning and respond to unexpected obstacles. These results further demonstrate the method’s wide applicability and robust performance across diverse complex environments.

Path planning and obstacle avoidance involve planning a feasible path from the initial to goal conflgurations while avoiding obstacles. This paper presents a vision-based three dimensional navigation frame mapping and path planning technique for collision avoidance for Micro Air Vehicles (MAVs) using a forward-looking onboard camera. Using computer vision algorithms, a depth map representing the range and bearing to obstacles is obtained. Based on the depth map, we use the extended Kalman Filter (EKF) to estimate the range and azimuth to obstacles and the height of obstacles and use the joint compatibility branch and bound (JCBB) algorithm to associate the camera measurements with the existing obstacles. A three dimensional map, constructed in cylindrical coordinates, is then created in the navigation frame of the MAV. We employ the Rapidly-Exploring Random Tree (RRT) algorithm to flnd collision-free Dubins paths in the navigation frame. The simulation results show that the proposed technique is successful in solving path planning and multiple obstacles avoidance problems for MAVs.

Aiming at the problem of multi-objective path planning (MOPP) and dynamic obstacle avoidance of manipulator in dynamic environment, this paper proposes a solution based on hierarchical reinforcement learning (HRL) framework. Traditional path planning methods have some problems in dynamic scenes, such as poor real-time performance, difficult to balance multi-objective conflicts and insufficient adaptability to environmental changes. Therefore, this paper designs a two-tier architecture including global path planning layer and local obstacle avoidance layer, which is trained by Proximal policy optimization (PPO) and Soft Actor-Critic (SAC) algorithms respectively, and achieves collaborative optimization through an efficient information exchange mechanism between layers. At the same time, a multi-objective reward function based on dynamic weight adjustment strategy is introduced. Fuzzy logic is used to adaptively balance the relationship among path length, obstacle avoidance safety and energy consumption according to environmental complexity. Combined with Long Short-Term Memory (LSTM), the trajectory of obstacles is predicted, and the potential field method is further introduced to modify the obstacle avoidance reward, which improves the real-time response ability and robustness of the algorithm in dynamic environment. The experimental results show that the HRL-SAC-PPO method proposed in this paper shows superior performance in both static and dynamic scenarios. In the static scene, the success rate of this method reaches 100%, the average path length is shortened to 2.13m, no collision occurs, and the energy consumption is reduced to 1.12 kJ, which shows a good multi-objective optimization effect. In the dynamic scene, the trajectory error of obstacles predicted by LSTM is only 4.2%, and the safe distance between the robot arm and obstacles is improved by 35%, which significantly enhances the reliability of obstacle avoidance. In addition, the average decision delay of this method is only 11.3ms, and the peak delay is 23ms, which is much lower than that of the contrast algorithm, showing stronger real-time response ability. The ablation experiment further verified the key role of LSTM trajectory prediction, dynamic weight adjustment and layered structure on the overall performance. In the welding task verification of the real UR5 manipulator, the success rate of the system in dynamic environment is 95.3%, the average path length is 3.41m, and the maximum joint acceleration is only 0.87 radian/s², which is far below the safety threshold, indicating that the algorithm has good stability and obstacle avoidance ability in practical application. The comprehensive performance comparison shows that this method performs well in different industrial scenarios, and has stronger environmental adaptability and comprehensive path planning ability.

Multi-vessel formation shipping demonstrates significant potential for enhancing maritime transportation efficiency and economy. However, existing route planning systems inadequately address the unique challenges of formations, where traditional methods fail to integrate global optimality, local dynamic obstacle avoidance, and formation coordination into a cohesive system. Global planning often neglects multi-ship collaborative constraints, while local methods disregard vessel maneuvering characteristics and formation stability. This paper proposes GLFM, a three-layer hierarchical framework (global optimization–local adjustment-formation collaboration module) for intelligent route planning of transport ship formations. GLFM integrates an improved multi-objective A* algorithm for global path optimization under dynamic meteorological and oceanographic (METOC) conditions and International Maritime Organization (IMO) safety regulations, with an enhanced Artificial Potential Field (APF) method incorporating ship safety domains for dynamic local obstacle avoidance. Formation, structural stability, and coordination are achieved through an improved leader–follower approach. Simulation results demonstrate that GLFM-generated trajectories significantly outperform conventional routes, reducing average risk level by 38.46% and voyage duration by 12.15%, while maintaining zero speed and period violation rates. Effective obstacle avoidance is achieved, with the leader vessel navigating optimized global waypoints and followers maintaining formation structure. The GLFM framework successfully balances global optimality with local responsiveness, enhances formation transportation efficiency and safety, and provides a comprehensive solution for intelligent route optimization in multi-constrained marine convoy operations.

In this paper，a novel method for obtaining high-quality paths for self-driving cars in underground parking lots is proposed. Self-driving cars require fast and accurate planning of collisionless path. When the self-driving car arrives at the parking lot, the car downloads the layout from the intelligent system of the parking lot and is assigned a parking space, then the location of the designated parking space and the car are provided by the intelligent system. A global path is planned by the global algorithm according to the location of the parking space and the car as well as the layout. If dynamic or unknown obstacles are detected in the process of moving along the global path, the parameters of obstacles can be estimated by the obstacle-detection algorithm. According to obtained parameters, the local obstacle avoidance path can be planned by the behavioral dynamics method. After completing obstacle avoidance, then the car will return to the global path and continue to move toward the target parking space. Finally, the proposed method is simulated by MATLAB, and the results show that the car can safely park in the target parking space. This method simultaneously satisfies the smooth and the real-time requirements of path planning.

Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.

The local path planning of unmanned ships has high real-time requirements, and the main research methods such as dynamic window method, random tree method, artificial potential field method, ant colony algorithm, particle swarm algorithm and other methods are difficult to be directly applied to path planning. Aiming at the local collision avoidance problem of unmanned ships, this paper proposes a local collision avoidance method for unmanned ships based on particle swarm optimization algorithm. The algorithm introduces fuzzy mathematics theory to judge the collision risk of unmanned boats and obstacles, transforms the local obstacle avoidance problem of unmanned boats into the optimization problem of the speed and heading changes of small boats, and integrates maritime rules and unmanned boat dynamics. The constraint conditions further optimize the value range of the speed and heading, so that the optimal obstacle avoidance strategy can be calculated through the optimization of the particle swarm optimization algorithm. The simulation research simulates the complex environment at sea and selects typical encounter situations in the maritime rules for testing. The simulation results show that the proposed method can well adapt to obstacle avoidance situations such as ship encounters, overtaking, and cross encounters.

This paper proposes a track planning method based on optimized A* algorithm and a dynamic window method path planning method under sudden threats. Using a real geographic elevation data model, the global path of the aircraft is obtained by using the A* algorithm planning after the improvement of the Dubins curve. This paper analyzes the sudden dynamic threats and external obstacles that may be encountered by the aircraft during flight, and uses the DWA algorithm to achieve the dynamic local obstacle avoidance goal, and simulates the simulation to verify the feasibility of the proposed method.