Path Obstacle Avoidance Research Articles

MPC-Based Obstacle Avoidance Path Tracking Control for Distributed Drive Electric Vehicles

A path tracking controller based on front wheel steering angle and additional yaw moment control is designed to achieve safe obstacle avoidance of distributed drive electric vehicles. Using sixth-degree polynomial at a given time with anti-collision and anti-rollover conditions, the path planning of obstacle avoidance is proposed. The front wheel steering angle and additional yaw moment are output by the Model Predictive Control (MPC) controller. The wheel torque is distributed by the torque distribution controller. Through additional yaw moment and the vertical force ratio of the wheel, the obstacle avoidance path tracking control is realized. The co-simulation platform is established with Carsim/Simulink. The obstacle avoidance path, model predictive controller and torque distribution controller designed in this paper are simulated. The results show that obstacle avoidance path and tracking controller for the distributed drive electric vehicles effectively meet the requirements of safe obstacle avoidance.

Path planning and intelligent control of a soft robot arm based on gas-structure coupling actuators

A fully 3D-printed soft pneumatic robotic arm based on two types of gas-structure coupling actuators is designed for on-orbit servicing. The path planning algorithm and trajectory tracking control strategy of the arm are developed. A model-free closed-loop control system with a PID controller and an iterative learning controller is built to improve the performance speed for repeated tasks. An experiential knowledge database for an iterative learning controller is described. The effectiveness of the iterative learning controller is verified by comparative experiments. The obstacle avoidance path planning algorithm based on the A* algorithm is presented. The validity of the path planning algorithm and trajectory tracking control strategy are verified by obstacle avoidance path planning experiments. The experimental results show that intelligent motion control and obstacle avoidance of the fully 3D-printed soft robotic arms are realized within an acceptable error.

Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

Background: With the development of robotics, more and more robots are being used in industrial production. However, as the production environment surrounding the robots becomes increasingly complex, there is a need for more intelligent industrial robots. For improving the intelligence of industrial robots, the most important thing is to ensure that they can carry out safe industrial production activities in a complex production environment. Therefore, studying the autonomous dynamic obstacle avoidance path planning of industrial robots in complex environments is of great significance for improving the intelligence of industrial robots and the application of human-machine collaboration. Objective: The main purpose of this paper is to improve the traditional artificial potential field method. It aims to improve the disadvantages of the traditional artificial potential field method, such as falling into the local minimum and failing to reach the target. Secondly, the background difference method, which is based on binocular vision and Kalman filtering algorithm, is used, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the information on the position of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in a complex environment. Methods: This paper proposes an improved artificial potential field method. First of all, in order to solve the problem of not being able to achieve the goal, the method of modifying the repulsion field function, as proposed by Wang Huili and others, has been cited. In the traditional repulsion function, the relative distance between the robot and the target is introduced. Due to this, the target point is always the minimum point of the potential field. It is necessary to ensure that the robot can reach the target position smoothly. For the local minimum problem, by adding a gravitational increase factor β, the gravitational force received is greater than the repulsive force, thereby breaking the balance of the resultant force. Finally, for the traditional artificial potential field method that does not consider the path planning problem in the presence of dynamic obstacles, the velocity vector of the dynamic obstacle is brought into the potential field function to improve the traditional artificial potential field method. Results: The robot easily falls into the local optimum during path planning, and the improved artificial potential field method overcomes this shortcoming. Therefore, the manipulator can perform autonomous dynamic obstacle avoidance path planning in an environment with dynamic obstacles, finally reaching the target point safely. Conclusions: The industrial production field faces an increasing demand for intelligent industrial robots. This paper improves on the traditional artificial potential field method based on binocular vision so that the mechanical arm can avoid dynamic obstacles autonomously in the presence of dynamic obstacles, thereby improving the degree of intelligence of the robotic arm, enabling the technology to meet the needs of future industrial development and contribute to the development of future industrial robot technology.

Path Planning of Spacecraft Cluster Orbit Reconstruction Based on ALPIO

An adaptive learning pigeon-inspired optimization based on mutation disturbance (ALPIO) is proposed for solving the problems of fuel consumption and threat avoidance in spacecraft cluster orbit reconstruction. First, considering the constraints of maintaining a safe distance between adjacent spacecraft within the spacecraft cluster and of avoiding space debris, the optimal performance index for orbital reconfiguration is proposed based on the fuel consumption required for path planning. Second, ALPIO is proposed to solve the path planning. Compared with traditional pigeon-inspired optimization, ALPIO uses the initialization of chaotic and elite backward learning to increase the population diversity, using a nonlinear weighting factor and adjustment factor to control the speed and accuracy of prepopulation convergence. The Cauchy mutation was implemented in the map and compass operator to prevent the population from falling into local optima, and the Gaussian mutation and variation factor were utilized in the landmark operator to prevent the population from stagnating in the late evolution. Through simulation experiments using nine test functions, ALPIO is shown to significantly improve accuracy when obtaining the optimum compared with PSO, PIO, and CGAPIO, and orbital reconfiguration consumes less total fuel. The trajectory of path planning for ALPIO is smoother than those of other optimization methods, and its obstacle avoidance path is the most stable.

Research on Path Planning Method of Unmanned Boat Based on Improved DWA Algorithm

Aiming at the problem of local path planning for unmanned surface vehicles, the traditional dynamic window algorithm (DWA) has the problems of uneven obstacle avoidance path and poor adaptability to complex environments. This paper proposes a local path planning method based on improved DWA. The evaluation item that replaces the direction angle difference with the target distance is used to avoid the path from being unsmooth due to vibration. And the fuzzy logic control algorithm is introduced to dynamically adjust the weight value of the trajectory evaluation function to complete the path optimization. The introduction of the A ∗ algorithm is designed to preplan the map environment and extract key turning points as subtarget points of the local obstacle avoidance algorithm to enhance the path optimization ability. The simulation results show that the improved DWA algorithm can shorten the planned path length by 23.4% and the path smoothness by 34.8%, enabling USV to find a reasonable, efficient, and smooth path in complex environments.

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.

Corridor-Walker: Mobile Indoor Walking Assistance for Blind People to Avoid Obstacles and Recognize Intersections

Navigating in an indoor corridor can be challenging for blind people as they have to be aware of obstacles while also having to recognize the intersections that lead to the destination. To aid blind people in such tasks, we propose Corridor-Walker, a smartphone-based system that assists blind people to avoid obstacles and recognize intersections. The system uses a LiDAR sensor equipped with a smartphone to construct a 2D occupancy grid map of the surrounding environment. Then, the system generates an obstacle-avoiding path and detects upcoming intersections on the grid map. Finally, the system navigates the user to trace the generated path and notifies the user of each intersection's existence and the shape using vibration and audio feedback. A user study with 14 blind participants revealed that Corridor-Walker allowed participants to avoid obstacles, rely less on the wall to walk straight, and enable them to recognize intersections.

USV compliant obstacle avoidance based on dynamic two ship domains

Aiming at the problem that unmanned surface vehicle (USV) is easy to collide with each other in dynamic environment, a flexible obstacle avoidance path planning algorithm based on dynamic two ship domains is proposed. Firstly, a dynamic two ship collision risk model based on the information of encounter vessels is established, and the type of rendezvous situation is determined on the basis of the model. Secondly, according to different rendezvous situation types, the COLREGs rules are analyzed to select the avoidance action, and a virtual circular repulsion potential field is designed for the avoidance action to guide the USV to avoid obstacles flexibly. Meanwhile, the velocity elliptical repulsion potential field is constructed for emergency avoidance under the close dangerous emergency state. Finally, simulation examples of different traffic scenarios are given to verify the effectiveness and practicability of the scheme.

Obstacle Avoidance Path Planning Using the Elite Ant Colony Algorithm for Parameter Optimization of Unmanned Aerial Vehicles

Obstacle Avoidance Path Planning Using the Elite Ant Colony Algorithm for Parameter Optimization of Unmanned Aerial Vehicles

Path Planning of a Multifunctional Elderly Intelligent Wheelchair Based on the Sensor and Fuzzy Bayesian Network Algorithm

An intelligent wheelchair is a kind of service robot. The most critical part of its research field is the safe navigation control of a wheelchair, and path planning is the core part of the navigation control system. The use of a reasonable obstacle avoidance algorithm can not only ensure the safe navigation of wheelchair but also protect the personal safety of users. This paper analyzes the actual needs of the wheelchair following system according to the application scenarios and determines the way to follow the target. The sensor scheme and the wheelchair mobile platform are selected through the analysis of the following methods and the characteristics of each sensor, and the overall architecture of the wheelchair hardware system is designed. Then, the key components such as processor, laser ranging radar, signal strength positioning module, and ultrasonic sensor in the hardware system are selected, and the corresponding peripheral circuit is designed according to the interface type and working voltage of the sensor. In order to solve the problems of the complex calculation process, one-way reasoning, and poor accuracy of the original fault tree method, this paper designs an expert knowledge inference machine based on the Bayesian network. After completing the definition of component vulnerability, the inference engine can be activated, the construction of the directed acyclic network is completed based on the knowledge model mapping of the fault tree, the conditional probability table definition of the directed edge is completed, and the bidirectional probability inference of the Bayesian network can be started. Diagnostic inference obtains the posterior probability to characterize the system impact degree of the bottom event failure. Based on a comprehensive analysis of the research status and research trend of intelligent wheelchair obstacle avoidance path planning methods, this paper mainly studies the intelligent wheelchair path planning method. The environment modeling and path planning of global path planning for static obstacles and dynamic obstacles are realized, and a simulation platform for wheelchair obstacle avoidance path planning based on MATLAB is designed, and simulation experiments are carried out on this basis. The effectiveness of the studied algorithms and strategies in the dynamic obstacle environment is verified by simulation experiments.

Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops.

Motion Planning and Tracking Control of Autonomous Vehicle Based on Improved A ∗ Algorithm

The traditional A ∗ algorithm, applied to the motion planning of autonomous vehicles, easily causes high computational costs and excessive turning points generated in the planning path. In addition, the vehicle cannot track the path due to the unsmooth inflection point. To overcome these potential limitations, an improved A ∗ algorithm-based motion planning algorithm and a tracking control strategy based on model predictive control theory were proposed in this work. The method of expanding the search neighborhood is adopted to improve the planning efficiency of A ∗ algorithm. The artificial potential field method is also incorporated into the proposed A ∗ algorithm. The resultant force generated by each potential field is further introduced into the evaluation function of A ∗ algorithm to plan the driving path, which could be suitable for autonomous vehicles. The sharp nodes in the path are smoothed by cubic quasi-uniform B-spline curve. The tracking control strategy is designed based on model predictive control theory to realize the accurate tracking of the planned path. Typical obstacle avoidance conditions were selected for co-simulation test verification. The experimental results show that the proposed motion planning algorithm and tracking control strategy can effectively plan the obstacle avoidance path and accurately track the path in different environments.

Binocular-Vision-Based Obstacle Avoidance Design and Experiments Verification for Underwater Quadrocopter Vehicle

As we know, for autonomous robots working in a complex underwater region, obstacle avoidance design will play an important role in underwater tasks. In this paper, a binocular-vision-based underwater obstacle avoidance mechanism is discussed and verified with our self-made Underwater Quadrocopter Vehicle. The proposed Underwater Quadrocopter Vehicle (UQV for short), like a quadrocopter drone working underwater, is a new kind of Autonomous Underwater Vehicle (AUV), which is equipped with four propellers along the vertical direction of the robotic body to adjust its body posture and two propellers arranged at the sides of the robotic body to provide propulsive and turning force. Moreover, an underwater binocular-vision-based obstacle positioning method is studied to measure an underwater spherical obstacle’s radius and its distance from the UQV. Due to its perfect ability of full-freedom underwater actions, the proposed UQV has obvious advantages such as a zero turning radius compared with existing torpedo-shaped AUVs. Therefore, one semicircle-curve-based obstacle avoidance path is planned on the basis of an obstacle’s coordinates. Practical pool experiments show that the proposed binocular vision can locate an underwater obstacle accurately, and the designed UQV has the ability to effectively avoid multiple obstacles along the predefined trajectory.

The obstacle avoidance assistance control of multi-axle distributed heavy vehicles

Based on the artificial potential field theory, a hierarchical obstacle avoidance assisted driving framework is proposed in this paper, in order to solve the obstacle avoidance assisted driving problem of distributed heavy vehicles. Initially, a desired lateral position potential field (DLPPF) that reflects the driver’s driving intention is applied based on the constant turn rate and velocity (CTRV) model. And a real-time obstacle avoidance path can be obtained and further updated through a comprehensive consideration of the safety of obstacle avoidance and the obstacle repulsion potential field (ORPF). In addition, a differential drive assistance steering (DDAS) on the heavy vehicle steering bridge has been adopted to realize avoiding obstacles, and additional yaw moment to the vehicle’s non-steering axle has been utilized to ensure the maneuverability and stability of vehicle. Aiming to solve the lower layer control problem, a variable weight controller based on model predictive control (MPC) is designed to coordinate path tracking performance and dynamics control. Finally, through simulations and comparisons, the effectiveness of the obstacle avoidance strategy is verified.

Optimization of Dynamic Obstacle Avoidance Path of Multirotor UAV Based on Ant Colony Algorithm

In this paper, the real-time path avoidance problem of multirotor UAV in the case of sudden obstacles in two-dimensional environment is studied. The principle, model, and application of ant colony algorithm are analyzed. On this basis, the adaptive dynamic window ant colony algorithm is proposed, and the adaptive dynamic window method is designed; the heuristic function of adding obstacle detection factors and the double pheromone update strategy are made to the ant colony algorithm, and the improved ant colony algorithm is used to replan the path within the dynamic window that can be automatically adjusted to achieve the purpose of obstacle avoidance. A real-time simulation experiment of path planning was conducted by constructing an environment map in MATLAB. The simulation results show that with the continuous increase of the number of sudden obstacles, the real-time replacement path of multirotor UAV also gradually increases, and when approaching the obstacles, the replacement path is more dense, indicating that the adaptive window ant colony algorithm can be applied to dynamic path replacement, and the multirotor UAV can realize dynamic obstacle avoidance path optimization under the condition of sudden obstacles in a short time.

A Two-Stage Feature Point Detection and Marking Approach Based on the Labeled Multi-Bernoulli Filter.

In recent years, various algorithms using random finite sets (RFS) to solve the issue of simultaneous localization and mapping (SLAM) have been proposed. Compared with the traditional method, the advantage of the RFS method is that it can avoid data association, landmark appearance and disappearance, missed detections, and false alarms in Bayesian recursion. There are many problems in the existing robot SLAM methods, such as low estimation accuracy, poor back-end optimization, etc. On the basis of previous studies, this paper presents a labeled random finite set (L-RFS) SLAM method. We describe a scene where the sensor moves along a given path and avoids obstacles based on the L-RFS framework. Then, we use the labeled multi-Bernoulli filter (LMB) to estimate the state of the sensor and feature points. At the same time, the B-spline curve is used to smooth the obstacle avoidance path of the sensor. The effectiveness of the algorithm is verified in the final simulation.

Active obstacle avoidance method of autonomous vehicle based on improved artificial potential field

Aiming at the local minimum point problem in an artificial potential field based on a safe distance model, this article proposes an algorithm for active obstacle avoidance path planning and tracking of autonomous vehicles using an improved artificial potential field. First, a possible road operating condition in which the artificial potential field based on the safety-distance model falls into a local minimum point is studied. Subsequently, an improved artificial potential field method is proposed by introducing the second virtual target attraction potential field, which successfully overcomes the local minimum point problem. Second, a model for autonomous vehicle active obstacle avoidance path planning and tracking based on the improved artificial potential field is established. Finally, MATLAB/CarSim co-simulations were performed under the road conditions of constant- and variable-velocity obstacle vehicles. The simulation results demonstrate that the improved artificial potential field method can effectively solve the local minimum point problem of the artificial potential field based on the safe distance model. Additionally, the safety and stability of autonomous vehicle active obstacle avoidance are improved.

A Path Planning Strategy of Wearable Manipulators with Target Pointing End Effectors

End effectors like firearms, cameras and fire water guns can be classified as pointing end effectors. When installed on wearable manipulators, a new function can be given to the wearer. Different from gripper end effectors (GEEs), target pointing end effectors (TPEEs) have different working tasks, and the requirements for path planning are also different. There is very limited research on wearable manipulators with TPEEs. Meanwhile, manipulator with GEE path planning tends to be mature, but with a relatively low efficiency concerning its algorithm in solving high-dimensional problems. In this paper, a degree of freedom (DOF) allocation scheme and a path planning strategy (unlike manipulator with gripper end effector) were proposed for manipulators with a target pointing end effector in order to reduce the difficulty of path planning. Besides, this paper describes a new algorithm-dimension rapid-exploration random tree (dimension-RRT) to divide the manipulator DOFs into groups and unify DOFs groups by adding a fake time. The dimension-RRT was compared with the rapid-exploration random tree star algorithm (RRT*) in the same simulation environment; when there are 500 random points, the dimension-RRT time consumption is 0.556 of RRT* and the path length is 0.5 of RRT *. To quickly obtain a path that can avoid the human body, dynamic movement primitives (DMPs) were used to simulate typical spatial motion path and obstacle avoidance path efficiently.

Obstacle Avoidance Path Planning of Space Robot Based on Improved Particle Swarm Optimization

In order to meet security requirements of space on orbit service, an obstacle avoidance trajectory planning method using improved particle swarm optimization had been presented in this paper. On the basis of the actual overall structure of 7 degrees of freedom redundant space manipulator and the characteristics of obstacles, the envelope method was used to model the arm and obstacles, respectively. The limit conditions to avoid the collision between them were analyzed. Then, the fitness function under the symmetrical conditions of avoiding the collision and searching for the shortest trajectory was constructed. In addition, the obstacle avoidance trajectory planning was solved based on improved particle swarm optimization (IPSO). Finally, simulation experiments were carried out to prove its effectiveness and rationality, where there were symmetrical advantages in two aspects. It can be concluded that the presented method based on IPSO has strong robustness.

Intelligent network vehicle driving risk field modeling and path planning for autonomous obstacle avoidance

Autonomous obstacle avoidance and decision-making algorithms for intelligent connected vehicles in a complicated transportation environment are important studies on intelligent driving. However, it is difficult to adapt to a more complicated traffic environment based on safety distance and conventional potential field. Therefore, in this paper, a driving risk field model based on field theory is proposed involving transportation factors and vehicle conditions. A hidden Markov model was used to evaluate and determine the motion state of surrounding vehicles. A safe, feasible, and smooth collision-free path was planned by calculating the magnitude of the potential field forces on the longitudinal and lateral sides of the obstacle vehicles. The results showed that the method can effectively select a suitable path for obstacle avoidance in complex road conditions while satisfying safety and traffic laws.