A novel obstacle avoidance algorithm: “Follow the Gap Method”

This study presents the path-following control design for a group of autonomous underwater vehicles in a plane with obstacle and collision avoidance. A distributed backstepping controller is exploited for an autonomous vehicle to design a path-following controller. Then, the controller is applied for the path-following of a group of autonomous vehicle systems in the formation setting. The graph theory is used to model the interconnection between the vehicles. To avoid obstacles and collisions, a dynamic limit-cycle-based algorithm is proposed. It is assumed that each obstacle is involved an elliptical shape and if the collision or conflict of one vehicle to the surrounded environmental obstacles is detected, the obstacle avoidance algorithm will be active to prevent collisions. Finally, the introduced algorithm is implemented and simulated for a group of underwater autonomous vehicles in MATLAB software. The simulation results illustrate the effectiveness and performance of the proposed distributed controller apparently.

Real-time obstacle avoidance is the basis to ensure the safe operation of the autonomous underwater vehicle (AUV), and it also represents the intelligent level of AUV. The main factors that affect AUV obstacle avoidance are environment perception ability, obstacle avoidance decision-making ability, and trajectory tracking control ability. This paper starts from the perspective of obstacle avoidance decision-making ability, that is, obstacle avoidance algorithm. Firstly, we summarized the structure and influencing factors of AUV real-time obstacle avoidance. Then, we introduced in detail the research progress of AUV intelligent obstacle avoidance algorithms, including fuzzy logic algorithms, neural network algorithms, and reinforcement learning algorithms. And we analysed the improvement methods of each algorithm from three-dimensional underwater environment, ocean current, AUV motion characteristics, and dynamic obstacles. Finally, we prospected the development of the AUV intelligent obstacle avoidance algorithm.

A new consensus theory-based method for formation control and obstacle avoidance of UAVs

Navigation technology is conceivably an important requirement for real time application of Autonomous vehicle. This paper focuses on the application of one of the soft computing technique i.e. a fuzzy logic based controller on the autonomous vehicle for the challenging job of obstacle avoidance. The proposed fuzzy logic controller is having two input and one output. The distances from left and right sensor have been considered as the input, whereas, the rotation angle is considered as the output. A total of 36 set of rule base have been used in designing the fuzzy logic controllers with Graphical User Interface (GUI), that provides the information in which the vehicle moves.

In this paper, a simple path correction and obstacle avoidance method with a bipedal robot for the learning stratification and performance, using an ultrasonic sensor and electronic compass sensor, is proposed. This bipedal robot was comprised using the Lego NXT Intelligent Bricks. The proposed method is implemented on an autonomous humanoid robot (the ARSR). One ultrasonic sensor and one electronic compass sensor are installed on the ARSR to detect environmental information including obstacles, the distance to the obstacle, and the directional angle of the robot. Based on the obtained information, an obstacle avoidance and path correct method is proposed to decide the ARSR's behavior so that it can avoid obstacles and move effectively to the destination area. Obstacle avoidance experiments are carried out to confirm the effectiveness of the proposed method and confirm the learning stratification and performance.

Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-robot formation motion, a path-deformation mapping mechanism is proposed, which deeply integrates artificial potential field and affine transformation, and drives formation adaptive adjustment in real time through path information. By using the non-uniform scaling characteristics of the affine transformation, the limitation of traditional conformal transformation is broken through, and the unity of flexibility and integrity is realized. The effectiveness of the algorithm is verified by experiments, which provide a practical solution for cooperative obstacle avoidance of multi-robot systems in complex environments. In order to verify the performance of the algorithm, a numerical simulation is carried out, and an experimental platform composed of seven omnidirectional mobile robots is built for physical verification. The simulation and experimental results show that the formation can complete the obstacle avoidance task in the complex static obstacle environment, and the average formation tracking error is maintained below 0.05 m. Compared with the traditional local obstacle avoidance or formation switching method, this algorithm significantly improves the fluency of the obstacle avoidance process and the integrity of the formation while ensuring a success rate of 100% obstacle avoidance.

This paper presents a model predictive control-based obstacle avoidance algorithm for autonomous ground vehicles at high speed in unstructured environments. The novelty of the algorithm is its capability to control the vehicle to avoid obstacles at high speed taking into account dynamical safety constraints through a simultaneous optimization of reference speed and steering angle without a priori knowledge about the environment and without a reference trajectory to follow. Previous work in this specific context optimized only the steering command. In this paper, obstacles are detected using a planar light detection and ranging sensor. A multi-phase optimal control problem is then formulated to simultaneously optimize the reference speed and steering angle within the detection range. Vehicle acceleration capability as a function of speed, as well as stability and handling concerns such as preventing wheel lift-off, are included as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Simulation results show that the proposed algorithm is capable of safely exploiting the dynamic limits of the vehicle while navigating the vehicle through sensed obstacles of different sizes and numbers. It is also shown that the proposed variable speed formulation can significantly improve performance by allowing navigation of obstacle fields that would otherwise not be cleared with steering control alone.

Existing resource-intensive obstacle avoidance techniques are hard to be applied on the small-size Unmanned Aerial Vehicles (UAVs) that have limited sensing and computation capacity. Therefore, it is necessary to develop an obstacle recognition and avoidance scheme which works under resource-constrained environments. To this backdrop, this paper first presents an obstacle recognition model based on monocular vision feature points. Afterwards, an obstacle recognition algorithm is put forward, whose computational complexity is low. Then, an obstacle avoidance method is proposed to regulate the obstacle-avoidance path of a UAV until it arrives its destination. To evaluate the effectiveness of the presented algorithms, we design and implement a simulation platform on Objective Modular Network Testbed in C++ (OMNeT++), and conduct a series of experiments. Experimental results show that the proposed model and algorithms can effectively guide a micro UAV to its destination using only an embedded processor and 0.5kg extra load. Even in poor communication conditions, the UAV can independently avoid obstacles and reach the destination only by acquiring the destination coordinates from the ground station.

<div class="section abstract"><div class="htmlview paragraph">In this research, an optimal real-time trajectory planning method is proposed for autonomous ground vehicles in case of overtaking a moving obstacle. When an autonomous vehicle detects a moving vehicle ahead of it in a proper speed and distance and the braking is not efficient due to the lost of its kinematic energy, the autonomous vehicle decides to overtake the obstacle by performing a double lane-change maneuver. A two-phase nonlinear optimal problem is developed for generating the path for the overtaking maneuver. The cost function of the first phase is defined in such a way that the vehicle approaches the moving obstacle as close as possible. Besides, the cost function of the second phase is defined as the minimization of the sum of the vehicle lateral deviation from the reference path and the rate of steering angle during the overtaking maneuver while the lateral acceleration of the vehicle does not exceed a safe limit. For the real-time path planning of a vehicle with high speed, the result of solving the optimal path planning are stored in the look-up table form for offline use. A full nonlinear vehicle modeled in CarSim software is used for path tracking simulation. The simulation results show that the generated path for the autonomous vehicle satisfies all the vehicle dynamics constraints, therefore it is applicable for a real autonomous vehicle.</div></div>

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.

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.

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.

AUTONOMOUS FLIGHT CONTROL AND HARDWARE-IN-THE-LOOP SIMULATOR FOR A SMALL HELICOPTER

An amputee cannot directly use the perceptual visual information to control the movements and gait patterns of his worn prosthesis. In order to help an amputee walk and cross over obstacles smoothly, the paper proposes a vision-locomotion coordination control method for a powered lower-limb prosthesis (PLLP), in which a vision system is proposed to detect obstacles, and a complete vision-locomotion loop is then constructed. With deep learning techniques, the vision system can recognize common obstacles (e.g., garbage cans, bricks and boxes) and obtain the features of obstacles (e.g., distance from obstacles to the depth camera and height of obstacles). Through integrating the vision system into the locomotion control system, the PLLP can make obstacle avoidance decisions and use dynamic movement primitives with type-2 fuzzy models (T2FDMPs) to help amputees cross over obstacles simultaneously. Utilizing the type-2 fuzzy models, smooth trajectories for crossing over obstacles can be obtained. The experimental results show that the PLLP with visual information can switch an amputee’s gaits between level walking and obstacle avoidance adaptively, which demonstrates the effectiveness of the vision-locomotion coordination control system. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The paper is motivated by the challenge of obstacle avoidance of prostheses. Traditional prostheses cannot achieve autonomous obstacle avoidance because they lack of environmental perception capability. In addition, most prostheses always utilize use the human-robot interaction between amputees and prostheses to recognize the environments. However, due to noise and individual differences, recognition results are not accurate enough. We found that the integration of vision can improve the accuracy and efficiency of environmental recognition. Therefore, it is necessary to construct a complete vision and locomotion closed-loop. In the paper, a vision-locomotion coordination control is proposed, and to make the PLLP cross over obstacles smoothly, a novel trajectory shaping with fuzzy-based dynamic movement primitives is developed. The coordination control is partitioned into the obstacle detection, trajectory shaping and joint control, which can help the PLLP fulfill several obstacle avoidance tasks.

In this paper, a multi-agent formation control strategy with collision avoidance and obstacle avoidance in complex environments is proposed. Under a virtual leader structure, the multi-agent formation is realized through path planning and formation control. In path planning, a transitional region based on prediction horizon is used to replan reference trajectories and avoid obstacles in advance. The virtual-leader alternation strategy and feasible state searching strategy are adopted to avoid one side obstacles and crowded obstacles, respectively. Then the path planning optimization problem is formulated by mixed-integer quadratic programming (MIQP) to calculate trajectories collision-free with obstacles. In formation control, a distributed model predictive control (DMPC) algorithm is developed to form and maintain formation. In DMPC optimization problem, the non-convex obstacle avoidance constraints are replaced with deviation constraints and linear constraints to design terminal invariant set and reduce online computation burden. Under the whole formation control strategy, the tasks of obstacle avoidance and collision avoidance are both achieved and the agents maintain the formation as much as possible. Finally, the example with different obstacle situations is discussed to illustrate the effectiveness of the proposed strategy.