Obstacle Avoidance Path Planning Research Articles

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm.

Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot.

Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues.

Obstacle Avoidance Path Planning of a 4-DOF Weapon Arm Based on Improved RRT (RRT-H) Algorithm

To improve soldiers’ combat capability, weapon arms have a good development prospect. However, due to special work scenarios and tasks, new requirements are exerted on. Based on the fast-expanding random tree algorithm (RRT), path algorithm optimization (RRT-H) is proposed for the path planning of weapon arms. Overall path optimization is achieved by reducing the local path length with a closer path point planning to the obstacle. In a complex environment, the RRT-H algorithm can avoid local traps by guiding the new path extension direction and exploring multiple different paths in the map. The superiority of this algorithm is verified with 2D plane obstacle avoidance and pathfinding simulation experiments. Compared to RRT∗, RRT∗ smart, and information RRT∗, the RRT-H can obtain high-quality calculation results in a shorter time. After setting degrees of freedom (DOF) as that of variables, the algorithm is applied to the 4-DOF weapon arm, which confirms an effective reduction to the 4-DOF weapon arm’s motion costs.

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Obstacle avoidance path planning of 6-DOF robotic arm based on improved A* algorithm and artificial potential field method

AbstractMost studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

The crucial dynamic path planning of autonomous vehicles is achieved via obstacle avoidance path planning technology. The reduction of the tire adhesion coefficient on icy and snowy roads (ISRs) increases the difficulty of autonomous vehicles’ control. In this paper, the driving characteristics of vehicles on ISRs are established, and the artificial potential field function is introduced to avoid collision risk when planning a path. A dynamic path planning algorithm for autonomous vehicles based on the artificial potential field (APF) is established. The adjustment factor is added to the gravitational potential field, and a judgment coefficient is added to the repulsive potential field to improve the artificial potential field function, based on the low adhesion of vehicles on ISRs. Moreover, a path with a continuous curvature is generated to achieve the driving comfort and driving safety of the planned path via trajectory smoothing. By establishing the Carsim/Simulink co-simulation platform, the effectiveness of dynamic path planning for autonomous vehicles under different algorithms and different obstacle models is compared. The results show that the improved APF algorithm has an obvious effect on the smoothness of the path and the reduction of the curvature mutation and can generate a safe and efficient path on icy and snowy roads. The dynamic obstacle avoidance of the improved APF algorithm improves the pre-judgment accuracy of the collision risk assessment of autonomous vehicles and shows the superiority of the improved algorithm.

Research on obstacle avoidance path planning of UAV in complex environments based on improved Bézier curve

Obstacle avoidance path planning is considered an essential requirement for unmanned aerial vehicle (UAV) to reach its designated mission area and perform its tasks. This study established a motion model and obstacle threat model for UAVs, and defined the cost coefficients for evading and crossing threat areas. To solve the problem of obstacle avoidance path planning with full coverage of threats, the cost coefficients were incorporated into the objective optimization function and solved by a combination of Sequential Quadratic Programming and Nonlinear Programming Solver. The problem of path planning under threat full coverage with no solution was resolved by improving the Bézier curve algorithm. By introducing the dynamic threat velocity obstacle model and calculating the relative and absolute collision cones, a path planning algorithm under multiple dynamic threats was proposed to solve the difficulties of dynamic obstacle prediction and avoidance. Simulation results revealed that the proposed Through-out method was more effective in handling full threat coverage and dynamic threats than traditional path planning methods namely, Detour or Cross Gaps. Our study offers valuable insights into autonomous path planning for UAVs that operate under complex threat conditions. This work is anticipated to contribute to the future development of more advanced and intelligent UAV systems.

An Obstacle Avoidance Path Planning and Evaluation Method for Intelligent Vehicles Based on the B-Spline Algorithm.

To meet the real-time path planning requirements of intelligent vehicles in dynamic traffic scenarios, a path planning and evaluation method is proposed in this paper. Firstly, based on the B-spline algorithm and four-stage lane-changing theory, an obstacle avoidance path planning algorithm framework is constructed. Then, to obtain the optimal real-time path, a comprehensive real-time path evaluation mechanism that includes path safety, smoothness, and comfort is established. Finally, to verify the proposed approach, co-simulation and real vehicle testing are conducted. In the dynamic obstacle avoidance scenario simulation, the lateral acceleration, yaw angle, yaw rate, and roll angle fluctuation ranges of the ego-vehicle are ±2.39 m/s2, ±13.31°, ±13.26°/s, and ±0.938°, respectively. The results show that the proposed algorithm can generate real-time, available obstacle avoidance paths. And the proposed evaluation mechanism can find the optimal path for the current scenario.

Path planning algorithm based on Improved Artificial Potential Field method

The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.

The Quadruped Robot Uses the Trajectory Planned by DIACO to Complete the Obstacle Avoidance Task

The diffusion-improved ant colony optimization (DIACO) algorithm, as introduced in this paper, addresses the slow convergence speed and poor stability of the ant colony optimization (ACO) in obstacle avoidance path planning for quadruped robots. DIACO employs a nonuniformly distributed initial pheromone, which reduces the blind search time in the early stage. The algorithm updates the heuristic information in the transition probability, which allows ants to better utilize the information from the previous iteration during their path search. Simultaneously, DIACO adjusts the pheromone concentration left by ants on the path based on the map information and diffuses the pheromone within a specific range following the artificial potential field algorithm. In the global pheromone update, DIACO adjusts the pheromone on both the optimal path and the worst path generated by the current iteration, thereby enhancing the probability of ants finding the optimal path in the subsequent iteration. This paper designs a steering gait based on the tort gait to fulfill the obstacle avoidance task of a quadruped robot. The effectiveness of the DIACO algorithm and steering gait is validated through a simulation environment with obstacles constructed in Adams. The simulation results reveal that DIACO demonstrates improved convergence speed and stability compared to ACO, and the quadruped robot effectively completes the obstacle avoidance task using the path planning provided by DIACO in combination with the steering gait.

Intelligent Path Planning of Underwater Robot Based on Reinforcement Learning

As one of the commonly used vehicles for underwater detection, underwater robots are facing a series of problems. The real underwater environment is large-scale, complex, real-time and dynamic, and many unknown obstacles may exist in the underwater environment. Under such complex conditions and lack of prior knowledge, the existing path planning methods are difficult to plan, therefore they cannot effectively meet the actual demands. In response to these problems, a three-dimensional marine environment including multiple obstacles is established with the real ocean current data in this paper, which is consistent with the actual application scenarios. Then, we propose an N-step Priority Double DQN (NPDDQN) path planning algorithm, which potently realizes obstacle avoidance in the complex environment. In addition, this study proposes an experience screening mechanism, which screens the explored positive experience and improves its reuse rate, thus efficiently improving the algorithm stability in the dynamic environment. This paper verifies the better performance of reinforcement learning compared with a variety of traditional methods in three-dimensional underwater path planning. Underwater robots based on the proposed method have good autonomy and stability, which provides a new method for path planning of underwater robots. <i>Note to Practitioners</i>&#x2014;The goal of this study is to provide a new solution for obstacle avoidance in path planning of underwater robots, which is consistent with the dynamic and real-time demands of the real environment. Existing underwater path planning researches lack a consistent environment with the actual application, and therefore we firstly construct a three-dimensional ocean environment with real ocean current data to provide support for the algorithms. Additionally, most of the algorithms are pre-planning methods or require long-time calculation, and there is little research on obstacle avoidance. In the face of obstacle changes, underwater robots with poor adaptability will cause performance decline and even economic losses. The proposed algorithm learns through interaction with the environment, and therefore it does not require any prior experience, and has good adaptability as well as fast inference speed. Especially, in the dynamic environment, algorithm performance is difficult to guarantee due to less positive experience in exploration. The proposed experience screening mechanism improves the stability of the algorithm, so that the underwater robot maintains stable performance in different dynamic environments.

Obstacle Avoidance Path Planning of 7-DOF Redundant Manipulator Based on Improved Ant Colony Optimization

Background: Obstacle avoidance path planning is an important parameter of robot manipulators. Path planning can directly affect the working efficiency of the manipulator. Objective: This study aims to summarize the optimization design method from a large number of literature and propose a new optimization design method to make the planned obstacle avoidance path of the manipulator shorter and smoother. Methods: The forward and inverse kinematics of the redundant manipulator is solved. Secondly, the obstacle and the robot manipulator envelope are simplified for collision detection. Then, the ant colony algorithm is improved by adding an obstacle environment to the construction of the heuristic function and dynamically adjusting the heuristic function factor to make the shortest path distance planned by the algorithm. Finally, the worst ant colony is added to the pheromone update to avoid the algorithm falling into the local optimal solution. Results: Through experiments and comparative studies, the optimized design process shows that the path planned by the improved ant colony algorithm has obvious advantages of shorter path distance and smoother path distance, which verifies the rationality of the improved algorithm. Conclusion: This method optimizes the convergence speed of the ant colony algorithm and avoids the ant colony algorithm from falling into the local optimal solution, which is of great significance for improving the obstacle avoidance path planning problem of a redundant manipulator with a degree of freedom.

Path Avoidance System of Intelligent Robot Based on Computer Vision

At present, obstacle avoidance technology is widely used in the military field, scientific detection, traffic control, industrial manufacturing, medical service, and many other fields, and various machines with autonomous navigation and obstacle avoidance capabilities have been used to varying degrees, thus replacing some human daily production activities and bringing many conveniences to people’s lives. The key to autonomous obstacle avoidance is to obtain the direction information of the obstacle in its direction of advance in real time, which is also the precondition of obstacle avoidance system in obstacle avoidance path planning. In this paper, an intelligent robot path avoidance system is designed based on computer vision (CV) theory. The binocular vision ranging experiment verifies that the system has a less ranging error and can effectively detect the location of obstacles within a certain range, and the success rate of intelligent robot avoidance of obstacles reaches more than 96%, achieving a good avoidance effect.

Research on Tractor Optimal Obstacle Avoidance Path Planning for Improving Navigation Accuracy and Avoiding Land Waste

Obstacle avoidance operations of tractors can cause parts of land to be unavailable for planting crops, which represents a reduction in land utilization. However, land utilization is significant to the increase in agricultural productivity. Traditional obstacle avoidance path planning methods mostly focus on automatic tractor navigation with small errors, ignoring the decrease in land utilization due to obstacle avoidance operations. To address the problem, this paper proposed an obstacle avoidance path planning method based on the Genetic Algorithm (GA) and Bezier curve. In this paper, a third-order Bezier curve was used to plot the obstacle avoidance path, and the range of control points for the third-order Bezier curve was determined according to the global path and the location of the obstacle. To target the navigation error and land utilization problems, GA was used to search for the optimal point from the selection range of the control point under multiple constraints for automatic tractor navigation such as the obstacle collision avoidance, the minimum turning radius, and the maximum turning angle. Finally, the optimal obstacle avoidance path was determined based on the selected control points to minimize the navigation error and maximize land utilization. The algorithm proposed in this paper was compared with existing methods and the results showed that it has generally favorable performance on obstacle avoidance path planning.

An obstacle avoidance path planner for an autonomous tractor using the minimum snap algorithm

Autonomous tractors use a GNSS-based approach combined with other sensors to provide higher efficiency and minimize human intervention. The basis of field automated navigation is the agricultural route planning of the entire field. One of the main aspects of agricultural route planning is obstacle avoidance path planning, which can provide a safe continuous trajectory for autonomous tractors to pass obstacles. In this study, a collision-free path planning method was proposed using the minimum snap algorithm to enable the tractor to safely navigate from one point to another. To authenticate the proposed algorithm, a tripartite experimental scheme was designed, encompassing a ROS-based simulation, robot platform validation, and a field test. Simulation results demonstrate that the collision-free path generated by the proposed algorithms can effectively guide the tractor to follow the given path with a lateral error of 1.75 cm. For the robot platform validation test, the mean lateral deviations were 2.63 cm, 5.01 cm, and 6.80 cm at speeds of 0.5 m/s, 0.8 m/s, and 1.0 m/s, respectively. The feasibility of using the developed algorithms was followed by a field test with a 162-KW CVT autonomous tractor. The mean lateral deviations were 5.17 cm, 5.13 cm, and 6.10 cm at speeds of 0.6 m/s, 0.8 m/s, and 1.0 m/s, respectively. The test results showed that the model could provide a safe and stable collision-free path for agricultural machines operating in the field.

Obstacle-avoidance path planning based on the improved artificial potential field for a 5 degrees of freedom bending robot

Abstract. Path planning is a key technique used in the operation of bending robots. In this paper, an obstacle-avoidance path-planning method of a 5 degrees of freedom (5 DOF) bending robot based on improved artificial potential field is proposed. Firstly, a connecting-rod coordinate system of the 5 DOF Cartesian bending robot is established to determine an equation of motion trajectory of the bending robot. Secondly, in view of the problem of the local minimum in the artificial potential field (APF) method and the failure of path planning, an improved APF path-planning method based on a rapidly exploring random tree (RRT) algorithm is proposed, which reduces the length of the path and enhances path smoothness. Finally, through simulation and obstacle-avoidance experiments on the path of a mechanical arm, effective path planning based on the improved APF method is verified. The experimental results show that the proposed path-planning method can plan an optimal path and meet the technical requirements of bending robot operations.

Obstacle-Avoidance Path-Planning Algorithm for Autonomous Vehicles Based on B-Spline Algorithm

To solve the problem of the real-time path-planning of autonomous vehicles for obstacle avoidance on structured roads, a path-planning approach based on the B-spline algorithm is proposed in this paper. Firstly, the mechanism of driver path planning is analyzed, and a dynamic risk-identification model based on the support vector machine is proposed. It combines the driver’s risk perception characteristics and a risk model. Then, the B-spline algorithm model is improved based on the risk-identification model. Furthermore, road features, road constraints and dynamic constraints are considered to further optimize the planning algorithm. To verify the path-planning approach proposed in this paper, a co-simulation experiment based on CarSim/Simulink is conducted. Results show that the improved algorithm is effective in static and dynamic obstacles avoidance. The algorithm can generate collision-free obstacle avoidance paths, and the paths meet the real-time requirements and dynamic constraints of obstacle avoidance scenarios. In addition, the proposed algorithm optimizes the path according to the driver’s operating characteristics, which can further improve the safety and comfort of autonomous vehicles.

Obstacle avoidance planning of autonomous vehicles using deep reinforcement learning

Obstacle avoidance path planning in a dynamic circumstance is one of the fundamental problems of autonomous vehicles, counting optional maneuvers: emergency braking and active steering. This paper proposes emergency obstacle avoidance planning based on deep reinforcement learning (DRL), considering safety and comfort. Firstly, the vehicle emergency braking and lane change processes are analyzed in detail. A graded hazard index is defined to indicate the degree of the potential risk of the current vehicle movement. The longitudinal distance and lateral waypoint models are established, including the comfort deceleration and stability coefficient considerations. Simultaneously, a fuzzy PID controller is installed to track to satisfy the stability and feasibility of the path. Then, this paper proposes a DRL process to determine the obstacle avoidance plan. Mainly, multi-reward functions are designed for different collisions, corresponding penalties for longitudinal rear-end collisions, and lane-changing side collisions based on the safety distance, comfort reward, and safety reward. Apply a special DRL method-DQN to release the planning program. The difference is that the long and short-term memory (LSTM) layer is utilized to solve incomplete observations and improve the efficiency and stability of the algorithm in a dynamic environment. Once the policy is practiced, the vehicle can automatically perform the best obstacle avoidance maneuver in an emergency, improving driving safety. Finally, this paper builds a simulated environment in CARLA and is trained to evaluate the effectiveness of the proposed algorithm. The collision rate, safety distance difference, and total reward index indicate that the collision avoidance path is generated safely, and the lateral acceleration and longitudinal velocity satisfy the comfort requirements. Besides, the method proposed in this paper is compared with traditional DRL, which proves the beneficial performance in safety and efficiency.

Online obstacle avoidance path planning and application for arc welding robot

• The system of online collision-free path planning for the arc welding robot is established. • An improved lazy-PRM algorithm with repulsion field is proposed to solve the path planning. • The collision-free path of the 7-DOF arc welding robot can be planned online. • The path quadratic optimization strategy is proposed to fine-tuning the pose and position of the welding gun. • Fast trajectory planning for each workpiece at any position during the welding process. The offline path planning problem for arc welding robots becomes increasingly difficult to achieve with the increasing product complexity and the open work environment, such as the welding tasks in ship formation. Most research considers the path planning problem without time limitations or other constraints that conform to the actual production process. During the welding process, the placement of workpieces seriously affects the efficiency of finding feasible paths for the welding robots for weld tasks that cannot be repeated accurately. Thus, in the flow welding workshop, the improved lazy Probabilistic Roadmap (PRM) algorithm based online collision-free path planning method is proposed to finish the welding tasks of general stiffened plates at an arbitrary position on the workbench within the robot's reach. The work environment is built with the grid method to reduce the computational dimension of collision detection. The local path planner is implemented by the lazy-PRM algorithm with the repulsive force field to rapidly search the collision-free path for the welding robot with specific safe distances. The robot posture adjustment strategy is also applied to the collision detection process to adjust some poses of the robot at candidate paths to be feasible. The improved path planning method is tested on both the simulated environment and actual application scenario and proved to be suitable for the path planning problem of the arc welding robot. The results of this study have been integrated into a computer approach, which can accelerate the motion planning process and reduce human efforts.

Obstacle avoidance path planning of mobile robot based on improved DWA

A new dynamic window algorithm is proposed to solve the problem that the optimal trajectory is difficult to plan and the trajectory is not smooth due to the dense obstacles in the complex workshop path planning task. The algorithm sets the initial direction angle, and then improves the evaluation function through the direction angle change factor to improve the efficiency and flexibility of the algorithm; The fuzzy controller is used to adjust the coefficient of the evaluation function, which improves the adaptability of the mobile robot to the complex workshop environment. The MATLAB simulation results show that the improved DWA can plan a more reasonable and smooth running path in the area with dense obstacles, and has better obstacle avoidance effect while ensuring the safety of the robot.