A Hybrid Obstacle Avoidance Strategy Based on PSO in Source Location

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

In this paper, a novel obstacle avoidance algorithm for the multi-UAV flocking was proposed by combining artificial potential field and Dubins path planning method. For the multi-UAV flocking, a virtual-leader was considered, and its flight path was designed by Dubins path planning method, which can take into account the overload constraint of the UAV. In this way, the flight path of the whole multi-UAV flocking was roughly planned, so as to save obstacle avoidance time, and the flight path of the follower-UAVs was designed according to the artificial potential field method, which has a better performance in real-time obstacle avoidance, and the algorithm is relatively simple. The simulation results show that the obstacle avoidance algorithm designed by combining the two methods has a better obstacle avoidance ability.

Over the last decade, drones and UAVs have attracted great research interest mainly due to their abilities and their potential to be used in various applications and domains. One of the most important operations that Drones must perform efficiently concerns the navigation in real-world environments. This typically includes the ability of path planning and obstacle avoidance. It is crucial that drones have the ability to perform automatically and efficiently procedures related to the avoidance of objects while navigating in environments. In this work, we present a methodology for assisting a drone to navigate in unknown environments and avoid obstacles. The methodology is based on a training-by-human concept where the drone learns how to avoid obstacles by example cases that are provided to it and it is trained on them. The results are quite interesting and indicate that the methodology is efficient and can assist drones and robotics systems to learn how to navigate and avoid obstacles in environments.

This paper introduces a novel control strategy for robotic obstacle avoidance by integrating Gradient Tracking Integral Sliding Mode Control (GT-ISMC) with Artificial Potential Fields (APFs). This approach aims to enhance autonomous navigation's robustness and accuracy in dynamic and uncertain environments. Traditional obstacle avoidance methods struggle with local minima and lack sufficient robustness against external disturbances and model uncertainties. The proposed method employs APFs to create smooth repulsive forces to avoid obstacles and attract the robot to the target. The novelty lies in designing an integral sliding mode controller to track the APF gradients, improving robustness against disturbances and uncertainties in the robot's dynamics, reducing chattering and improving convergence performance. Stability analysis guarantees the convergence of the robot to the desired reference while evading the obstacle. Simulations and experimental results demonstrate that the GT-ISMC with APFs effectively navigates complex environments, offering superior robustness, faster convergence, and smoother trajectories compared to conventional PID, sliding mode, and Potential Field methods. This work contributes to the advancement of autonomous robotic control by providing a reliable and efficient method for real-time obstacle avoidance, with potential applications in mobile robotics and industrial automation. Highlights A novel control strategy for robotic obstacle avoidance by integrating Gradient Tracking Integral Sliding Mode Control (GT-ISMC) with Artificial Potential Fields (APFs) is proposed. The controller enhances the robustness and accuracy of autonomous navigation in dynamic and uncertain environments, where some motion constraints are considered. The proposed method employs APFs to generate smooth, continuous repulsive forces that guide the robot away from obstacles while attracting it toward the target. By incorporating gradient tracking into the sliding mode framework, the system benefits from reduced chattering and improved convergence performance.

The obstacle avoidance controller is a key autonomous component which involves the control of tractor system dynamics, such as the yaw lateral dynamics, the longitudinal dynamics, and nonlinear constraints including the speed and steering angles limits during the path-tracking process. To achieve the obstacle avoidance ability of control accuracy, an independent path re-planning controller is proposed based on ROS (Robot Operating System) nonlinear model prediction in this paper. In the design process, the obstacle avoidance function and an objective function are introduced. Based on these functions, the obstacle avoidance maneuvering performance is transformed into a nonlinear quadratic optimization problem with vehicle dynamic constraints. Moreover, the tractor dynamics maneuvering performance can be effectively adjusted through the proposed objective function. To validate the proposed algorithm, a ROS based tractor dynamics model and the SLAM (Simultaneous Localization and Mapping) are established for numerical simulations under different speed. The maximum obstacle avoidance deviation in the simulation is 0.242 m at 10 m/s, and 0.416 m at 30 m/s. The front-wheel rotation angle and lateral velocity are within the constraint range during the whole tracking process. The numerical results show that the designed controller can achieve the tractor obstacle avoidance ability with good accuracy under different conditions. Keywords: ROS, obstacle avoidance, nonlinear model prediction, agricultural tractor DOI: 10.25165/j.ijabe.20191206.4907 Citation: Liu Z D, Lv Z Q, Zheng W X, Zhang W Z, Cheng X X. Design of obstacle avoidance controller for agricultural tractor based on ROS. Int J Agric & Biol Eng, 2019; 12(6): 58–65.

A novel obstacle avoidance strategy is proposed for autonomous underwater vehicle (AUV) in unknown underwater environment according to detected outline of avoidance. The obstacle's outline avoidance algorithm (OOA) is adapt to irregular obstacles which is used in detouring obstacles according their outline detected by forward looking sonar (FLS). The largest polar angle algorithm (LPA) is used to produce avoidances' outline by changing detected irregular obstacle into convex polygon, which simplifies the obstacle avoidance process. Finally, simulations are carried out to demonstrate the performance of the strategy, where the obtained obstacle avoidance path is optimal or sub-optimal for AUV moving in unknown environment.

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.

Obstacle avoidance ability is the significant embodiment of the ground mobile robot, and the basic guarantee of the ground mobile robot to perform various tasks. Obstacle avoidance technologies are divided into two kinds, one is based on the global map and another is based on sensors respectively. This paper mainly aims at the local obstacle avoidance method based on sensors. The study of obstacle detection and obstacle avoidance are two inseparable parts in the research of obstacle avoidance ability. This paper proposes an efficient obstacle detection and obstacle avoidance algorithm based on 2-D lidar. A method is proposed to get the information of obstacles by filtering and clustering the laser-point cloud data. Also, this method generates the forward angle and velocity of robot based on the principle of minimum cost function. The obstacle detection and obstacle avoidance algorithm has advantages of a simple mathematical model and good real-time performance. The effectiveness of the proposed algorithm is verified on MATLAB simulation platform.

With the continuous expansion of international terrorist organizations, terrorists have been looking for ways to cause massive destruction, in which one of dangerous weapons is a radiological source. The explosion of radiological sources makes radioactive particles scattered over a vast area, causing radiation contamination. To avoid radiation disasters, there should be autonomous mobile robots that are equipped with radiation detectors to search any radiological sources in public places. Thus, the motivation of this paper is to find out any radiological sources in an unknown environment by an autonomous mobile robot. To estimate the accurate location of a radiological source, an artificial potential field is adopted where attractive forces are modeled by a particle filter using the model of radiation sensing model. To build up an accurate sensing model, we consider the physics of radiation and the angle of incidence of gamma ray to the radiological sensor. The simulation results show that the mobile robot is able to find the radiological source in an unknown environment.

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

Aiming at the problems of robot path planning with artificial potential field method, such as target unreachable and local minimum points, an improved artificial potential field method model is proposed. The obstacles in unknown environment are designed in the form of grid map, so that the robot can avoid obstacles and move towards the target through perception. Matlab GUI is used to set up a two-dimensional coordinate system environment including obstacles and target points, and the optimal trajectory movement simulation experiment is carried out from the initial position to the desired position in the plane map. Experiments show that the improved artificial potential field method can make the mobile robot navigate in unknown environment, avoid obstacles and find the appropriate path, so as to achieve the requirement of collision free. The simulation results are close to the expected results, which shows that this method can effectively improve the feasibility of path planning and the effectiveness of obstacle avoidance of mobile robot in unknown environment.

This paper discusses the importance, the complexity and the challenges of mapping mobile robot's unknown and dynamic environment, besides the role of sensors and the problems inherited in map building. These issues remain largely an open research problems in developing dynamic navigation systems for mobile robots. The paper presenst the state of the art in map building and localization for mobile robots navigating within unknown environment, and then introduces a solution for the complex problem of autonomous map building and maintenance method with focus on developing an incremental grid based mapping technique that is suitable for real-time obstacle detection and avoidance. In this case, the navigation of mobile robots can be treated as a problem of tracking geometric features that occur naturally in the environment of the robot. The robot maps its environment incrementally using the concept of occupancy grids and the fusion of multiple ultrasonic sensory information while wandering in it and stay away from all obstacles. To ensure real-time operation with limited resources, as well as to promote extensibility, the mapping and obstacle avoidance modules are deployed in parallel and distributed framework. Simulation based experiments has been conducted and illustrated to show the validity of the developed mapping and obstacle avoidance approach.

The use of unmanned aerial vehicle (UAV) has been recognized by the majority of people, and is regarded as a reliable tool to complete some tasks. As for the obstacle avoidance of coaxial rotor UAV (CR-UAV), a flight pattern map (Flight Situation Awareness Map: FSAM) that can sense the flight environment and an integrated control method (Improved Artificial Potential Field: IAPF) based on the common artificial potential field method with appropriate improvements to achieve obstacle avoidance (IAPF). Firstly, we construct the FSAM, which can map the environment information around the UAV on the FSAM. Then, based on the FSAM, the IAPF functions are established to achieve the obstacles avoidance. Cause the artificial potential field (APF) has a characteristic that cannot avoid: the problem of local minima, a rotating potential field is put forward to ensure that the CR-UAV has only one potential equilibrium point at the target point in the environment, and it will improve the ability of CR-UAV to avoid complex obstacles. At the end of this study, through data analysis, the performance of obstacle avoidance and the attitude stability of CR-UAV are good, the simulation results confirm that the approaches proposed in this paper can address the obstacles avoidance successfully.KeywordsCR-UAVImproved Artificial Potential Field (IAPF)Flight Situation Awareness Map (FSAM)Obstacles avoidance

With the booming development of logistics, manufacturing and warehousing fields, the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles (AGVs) has become the focus of scientific research. In this paper, an enhanced deep reinforcement learning (DRL) framework is proposed, aiming to empower AGVs with the ability of autonomous navigation and obstacle avoidance in the unknown and variable complex environment. To address the problems of time‐consuming training and limited generalisation ability of traditional DRL, we refine the twin delayed deep deterministic policy gradient algorithm by integrating adaptive noise attenuation and dynamic delayed updating, optimising both training efficiency and model robustness. In order to further strengthen the AGV's ability to perceive and respond to changes of a dynamic environment, we introduce a distance‐based obstacle penalty term in the designed composite reward function, which ensures that the AGV is capable of predicting and avoiding obstacles effectively in dynamic scenarios. Experiments indicate that the AGV model trained by this algorithm presents excellent autonomous navigation capability in both static and dynamic environments, with a high task completion rate, stable and reliable operation, which fully proves the high efficiency and robustness of this method and its practical value.

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.