Moving Obstacle Avoidance Research Articles

A fast collision detection method based on point clouds and stretched primitives for manipulator obstacle-avoidance motion planning

A fast collision detection method based on point clouds and stretched primitives for manipulator obstacle-avoidance motion planning

Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm

Dual-Arm Obstacle Avoidance Motion Planning Based on Improved RRT Algorithm

Precise Obstacle Avoidance Movement for Three-Wheeled Mobile Robots: A Modified Curvature Tracking Method

This paper proposes a precise motion control strategy for a three-wheeled mobile robot with two driven rear wheels and one steered front wheel so that an obstacle avoidance motion task is able to be well implemented. Initially, the motion laws under nonholonomic constraints are expounded for the three-wheeled mobile robot in order to facilitate the derivation of its dynamic model. Subsequently, a prescribed target curve is converted into a speed target through the nonholonomic constraint of zero lateral speed. A modified dynamical tracking target that is aligned with the dynamic model is then developed based on the relative curvature of the prescribed curve. By applying this dynamical tracking target, path tracking precision is enhanced through appropriate selection of a yaw motion speed target, thus preventing speed errors from accumulating during relative curvature tracking. On this basis, integral sliding mode control and feedback linearization methods are adopted for designing robust controllers, enabling the accurate movement of the three-wheeled mobile robot along a given path. A theoretical analysis and simulation results corroborate the effectiveness of the proposed trajectory tracking control strategy in preventing off-target deviations, even with significant speed errors.

Distributed Adaptive Formation Tracking for a Class of Uncertain Nonlinear Multiagent Systems: Guaranteed Connectivity Under Moving Obstacles.

This article explores a guaranteed network connectivity problem during moving obstacle avoidance within a distributed formation tracking framework for uncertain nonlinear multiagent systems with range constraints. We investigate this problem based on a new adaptive distributed design using nonlinear errors and auxiliary signals. Within the detection range, each agent regards other agents and static or dynamic objects as obstacles. The nonlinear error variables for formation tracking and collision avoidance are presented, and the auxiliary signals in formation tracking errors are introduced to maintain network connectivity under the avoidance mechanism. The adaptive formation controllers using command-filtered backstepping are constructed to ensure closed-loop stability with collision avoidance and preserved connectivity. Compared with the previous formation results, the resulting features are as follows: 1) the nonlinear error function for the avoidance mechanism is considered an error variable, and an adaptive tuning mechanism for estimating the dynamic obstacle velocity is derived in a Lyapunov-based control design procedure; 2) network connectivity during dynamic obstacle avoidance is preserved by constructing the auxiliary signals; and 3) owing to neural networks-based compensating variables, the bounding conditions of time derivatives of virtual controllers are not required in the stability analysis.

Time-Varying Formation Control With Moving Obstacle Avoidance for Input-Saturated Quadrotors With External Disturbances

Time-Varying Formation Control With Moving Obstacle Avoidance for Input-Saturated Quadrotors With External Disturbances

Obstacle avoidance motion planning for space redundant manipulator based on improved RRT algorithm

In addressing the challenge of obstacle-avoidance motion planning for space redundant manipulators operating in environments with obstacles, an improved method based on a rapidly exploring random tree (RRT) algorithm is proposed. The method first adopts a target probabilistic sampling strategy to enhance the target orientation. Secondly, an artificial potential field is constructed to steer the growth of the node tree in joint space. Subsequently, a collision detection model between the manipulator and obstacles is established based on the bounding capsule and bounding sphere in Cartesian space. Finally, the paths are pruned and smoothed with a cubic Bezier curve. The simulation results validate the method’s capacity to effectively plan a collision-free path for the space redundant manipulator and the joint motion trajectory is smooth.

Toward the Intelligent, Safe Exploration of a Biomimetic Underwater Robot: Modeling, Planning, and Control.

Safe, underwater exploration in the ocean is a challenging task due to the complex environment, which often contains areas with dense coral reefs, uneven terrain, or many obstacles. To address this issue, an intelligent underwater exploration framework of a biomimetic robot is proposed in this paper, including an obstacle avoidance model, motion planner, and yaw controller. Firstly, with the aid of the onboard distance sensors in robotic fish, the obstacle detection model is established. On this basis, two types of obstacles, i.e., rectangular and circular, are considered, followed by the obstacle collision model's construction. Secondly, a deep reinforcement learning method is adopted to plan the plane motion, and the performances of different training setups are investigated. Thirdly, a backstepping method is applied to derive the yaw control law, in which a sigmoid function-based transition method is employed to smooth the planning output. Finally, a series of simulations are carried out to verify the effectiveness of the proposed method. The obtained results indicate that the biomimetic robot can not only achieve intelligent motion planning but also accomplish yaw control with obstacle avoidance, offering a valuable solution for underwater operation in the ocean.

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.

Obstacle Avoidance Model of Two-Wheeled Vehicles for Nonrigid Obstacles Based on Social Forces

To describe the microtrajectory change behavior of two-wheeled vehicles when encountering nonrigid obstacles, this paper proposes an obstacle avoidance model of two-wheeled vehicles for nonrigid obstacles based on the improved social force method. In the model, the obstacle avoidance characteristics of two-wheeled vehicles for nonrigid obstacles and the safety operation requirements of two-wheeled vehicles are considered. The calculation methods of the minimum boundary distance of avoidance and the boundary avoidance force range edge function are proposed based on the ultimate turning angle and boundary avoidance characteristics, respectively. The speed control and direction control behavior of the rider to the vehicle is abstracted as the form of social force, and the two-wheeled vehicle/rider individual is subjected to the avoidance force and correction force perpendicular to the direction of the longitudinal axis of the vehicle body. The centrifugal trajectory obstacle avoidance motion obeying the Newtonian mechanics is studied based on the characteristics of the vehicle body turning angle change. Different traffic environments are constructed by MATLAB software and simulated according to the model running logic, and sensitivity analysis is performed based on the actual data collected in the survey. The simulation results show that, under the same conditions, compared with μi = 50N, when μi = 200N, the X-axis displacement of the simulated vehicle in the avoidance process is shortened by 26.31%, and the body deflection angle increases by 50.75% at the end moment of the avoidance process. Compared with electric motorcycles, the displacement of the X-axis in the avoidance process of the electric bicycle is shortened by 16.92%, and the body deflection angle at the end moment of the avoidance process is increased by 13.68%. The simulation results show that the model can well describe the trajectory change behavior characteristics of two-wheeled vehicles/riders encountering nonrigid obstacles in real situations, and the obstacle avoidance model of two-wheeled vehicles proposed in this paper is considered to be traffic justified.

Dual-Loop Control of Cable-Driven Snake-like Robots

Snake-like robots, which have high degrees of freedom and flexibility, can effectively perform an obstacle avoidance motion in a narrow and unstructured space to complete assignments efficiently. However, accurate closed-loop control is difficult to achieve. On the one hand, this is because adding too many sensors to the robot will significantly increase its mass, size, and cost. On the other hand, the more complex structure of the hyper-redundant robot also challenges the more elaborate closed-loop control strategy. For these reasons, a cable-driven snake-like robot, which is compact and low cost, with force transducers and angle sensors, is designed in this article. The simpler and more direct kinematic model is studied, which applies to a widely used kinematics algorithm. Based on the kinematic model, the inverse dynamics are resolved. Finally, this article analyzes the sources of the motion errors and achieves dual-loop control through force-feedback and pose-feedback. The experiment results show that the robot’s structure and dual-loop control strategy function with high accuracy and reliability, meeting the requirements of engineering applications and high-precision control.

Dexterous obstacle-avoidance motion control of Rope Driven Snake Manipulator based on the bionic path following

The RDSM (Rope Driven Snake Manipulator) is a hyper-redundant robot operating in narrow and confined environments, such as aerospace, nuclear industry and etc. Inspired from the bionic following motion, an obstacle-avoidance control method of the RDSM in uncharted environments is proposed without the information of global obstacles. First, the kinematic model of a 17-DOF RDSM is established considering the mapping among the joint angle, rope length and end posture. Then, the motion path and inverse kinematic iterative algorithm of the RDSM in multi-obstacle environments is deduced using the piecewise Bézier curves. The motion control method of the RDSM is designed based on the model reference adaptive control, guaranteeing the joint error and end posture error asymptotically stable. Finally, the proposed obstacle avoidance method is verified with the simulation and experiment. As the result shown, the RDSM avoids obstacles on a path with a minimum curvature radius of 1/8 arm length, and the motion control precision of the end posture is within ±2.5mm/±0.5∘.

Research on Real-Time Obstacle Avoidance Motion Planning of Industrial Robotic Arm Based on Artificial Potential Field Method in Joint Space

The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles.

Research on obstacle avoidance motion planning method of manipulator in complex multi scene

In order to improve the efficiency and success rate of obstacle avoidance motion planning of industrial manipulator in complex multi scenes, a collision detection model between manipulator and obstacles based on cylinder and sphere bounding box is established, and an improved RRT* algorithm based on heuristic probability fusion artificial potential field method(P-artificial potential field RRT*, PAPF-RRT*) is proposed. The probability target bias and random sampling point optimization strategy are introduced into the sampling, and the location optimization constraints are applied to the sampling points to enhance the sampling guidance and quality. In order to change the expansion direction of the traditional new node and the local optimization problem in special environment, the target gravity, obstacle repulsion and adaptive step size of the artificial potential field method are combined, so that the algorithm can guide the expansion direction and step size of the new node in real time within the resultant force range generated by APF, reducing excessive exploration and the expansion of the collision region. The Cubic B-spline is used to interpolate and optimize the planned path to reduce the complexity of the path and improve the flexibility of the path. The simulation results in two-dimensional and three-dimensional multi scenes show that the present algorithm reduces the average path search time by 31.22% and shortens the path length by 17.32% comparing with the traditional RRT* algorithm. The visual simulation results show that the present algorithm can make the manipulator successfully avoid obstacles and run to the target point quickly and smoothly.

Optimal Parking Path Planning and Parking Space Selection Based on the Entropy Power Method and Bayesian Network: A Case Study in an Indoor Parking Lot

According to the vehicle dynamics model and the requirements of reliable safety and minimal time, the path planning problem of parking in different types of parking spaces is solved by obstacle avoidance analysis and motion analysis in the case of the optimal solution, and the parking trajectory from the initial position to the designated parking space is obtained. In the static situation, different parking spaces in the parking space are occupied; analyze the parking space type, parking space left and right occupancy situation, and the distance between the vacant parking space and the starting point location of unoccupied cars; and establish the attribute information matrix R0 of the vacant parking space and calculate the KMO value of the matrix R0. This is completed to determine the weak correlation between the attributes of the vacant parking space and use the matrix R0 as the original evaluation matrix of the entropy weight method, using the entropy weight method to calculate the three attributes of parking space type, parking space left and right occupancy situation, and distance between starting point and parking space. These results are weighted in the optimal parking space selection process, the difficulty score of the vacant parking space is determined, and the optimal parking space is determined through the ranking of the scores. In the dynamic case, the number of parking spaces and parking space usage will change over time, with the help of the Bayesian network, the existing parking spaces and number of spaces in the parking lot at the previous moment are learned according to the computer clock, which can be used to reason about the number of parking spaces and parking space availability in the parking lot at the next moment. The weights of the three attributes of parking space type, parking space left and right situation, and distance between the starting point and parking space are updated in the case of a dynamic change of parking space, and then the parking difficulty score of a new vacant parking space using the entropy weight method is used to select the optimal parking space in the dynamic situation. The optimized parking path planning and parking space selection method could contribute to enhancing parking efficiency for the sustainable management of indoor parking lots.

A Predictable Obstacle Avoidance Model Based on Geometric Configuration of Redundant Manipulators for Motion Planning

When a manipulator works in dynamic environments, it may be affected by obstacles and may cause danger to people around. This requires the manipulator to be able to plan the obstacle avoidance motion in real time. Therefore, the problem solved in this paper is dynamic obstacle avoidance with the whole body of the redundant manipulator. The difficulty of this problem is how to model the manipulator to reflect the motion relationship between the manipulator and the obstacle. In order to describe accurately the occurrence conditions of the collision, we propose the triangular collision plane, a predictable obstacle avoidance model based on the geometric configuration of the manipulator. Based on this model, three cost functions, including the cost of the motion state, the cost of a head-on collision, and the cost of the approach time, are established and regarded as optimization objectives in the inverse kinematics solution of the redundant manipulator combined with the gradient projection method. The simulations and experiments on the redundant manipulator and the comparison with the distance-based obstacle avoidance point method show that our method improves the response speed of the manipulator and the safety of the system.

An Obstacle-Avoidance Motion Planning Method for Redundant Space Robot via Reinforcement Learning

On-orbit operation tasks require the space robot to work in an unstructured dynamic environment, where the end-effector’s trajectory and obstacle avoidance need to be guaranteed simultaneously. To ensure the completability and safety of the tasks, this paper proposes a new obstacle-avoidance motion planning method for redundant space robots via reinforcement learning (RL). First, the motion planning framework, which combines RL with the null-space motion for redundant space robots, is proposed according to the decomposition of joint motion. Second, the RL model for null-space obstacle avoidance is constructed, where the RL agent’s state and reward function are defined independent of the specific information of obstacles so that it can adapt to dynamic environmental changes. Finally, a curriculum learning-based training strategy for RL agents is designed to improve sample efficiency, training stability, and obstacle-avoidance performance. The simulation shows that the proposed method realizes reactive obstacle avoidance while maintaining the end-effector’s predetermined trajectory, as well as the adaptability to unstructured dynamic environments and robustness to the space robot’s dynamic parameters.

Obstacle Avoidance Motion Using Elliptical Potential Field for Autonomous Robot

Obstacle Avoidance Motion Using Elliptical Potential Field for Autonomous Robot

Numerical investigation of novel 3D-SPA for gripping analysis in multi-environment

The soft pneumatic actuator (SPA) has strong flexibility and good compliance, allowing its assembly into a multi-functional soft gripper that can be applied to object grasping and handling, post-disaster rescue, rehabilitation training, and in other fields. However, many actuators have limited grasping modes and can only bend in a single direction. And most of the soft grippers are studied in free space for grasping. Herein, a novel 3D-SPA with internal bellows chambers, inspired by the structure of the bird claws and the human fingers, is presented. Based on Yeoh hyperelasticity theory and virtual work principle, the mechanical theoretical model between different input air pressure Pin and the bending angle θ of the actuator is established, and the spatial conversion relationship of obstacle avoidance motion is given. The influence of the length and number of the chamber, the radius of the circular section, the shape of the cross section and the length of the joint on the bending performance and stress distribution of the actuator are studied in detail, respectively. Two soft grippers with different structures, composed of 3D-SPA, have a variety of bending configurations on different planes, which improves the inclusiveness of grasping objects with different shapes and sizes. Finally, the process of object grasping in free space and obstacle avoidance grasping and handling in obstacle space are studied respectively. The validity of the proposed mechanical model and spatial transformation relationship are verified. The investigation results reveal that the developed scheme assists the robot’s obstacle avoidance and grasping, which can be integrated into industrial robotic arms and AGVs for mobile operations and applied to rehabilitation training to help patients gradually recover hand joint function.

Fast cooperative obstacle-avoidance path planning for multiple space manipulators

For a space manipulator with multiple links, joints, and degrees of freedom, it is necessary to comprehensively consider the motion state of each link to make the manipulator reach the target position. In this regard, this study proposes a fast cooperative obstacle-avoidance path planning method for multiple space manipulators. The proposed method can quickly determine the obstacle-avoidance path of each link in multiple space manipulators with multiple degrees of freedom. In this method, first, the geometric relationship between the space manipulator and the target is used to constrain the range of motion of each link in the space manipulator. Thereafter, the Bezier shape-based (SB) method is used to generate the obstacle-avoidance motion path of each of these links. By comparing the results of the Bezier SB method, the finite Fourier series (FFS) SB method, and the Gauss pseudospectral method (GPM), the effectiveness and superiority of the proposed method are verified. The simulation results show that the Bezier SB method yields better results with shorter computation time than the FFS SB method. The results obtained using the Bezier SB method are employed as the initial values for the GPM. And the computation time for the Bezier SB method is less than 2.5% that of the GPM, while yielding results with a performance index difference of approximately 2%. Thus, the proposed method is proved to be considerably effective for quickly determining the cooperative obstacle-avoidance path of multiple manipulators in complex on-orbit service tasks.

A circle grid-based approach for obstacle avoidance motion planning of unmanned surface vehicles

Aiming at an obstacle avoidance problem with dynamic constraints for Unmanned Surface Vehicle (USV), a method based on Circle Grid Trajectory Cell (CGTC) is proposed. Firstly, the ship model and standardization rules are constructed to develop and constrain the trajectory, respectively. Secondly, by analyzing the properties of the circle grid, the circle grid tree is produced to guide the motion of the USV. Then, the kinematics and dynamics of the USV are considered through the on-line trajectory generator by designing a relational function that links the rudder angle, the heading angle, and the central angle of the circle grid. Finally, obstacle avoidance is achieved by utilizing the on-line trajectory generator to choose a safe, smooth, and efficient path for the USV. The experimental results indicate that the proposed method can avoid both static and dynamic obstacles, showing better performance in terms of distance cost and steering cost comparing with the related methods, and our method only takes 50% steering cost of the grid-based method; the collision avoidance path not only conforms to the USV dynamic characteristics but also provides a reference of steering command.