A real-time immune planning algorithm incorporating a specific immune mechanism for multi-robots in complex environments

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.

Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm

Unmanned surface vehicle (USV) path planning is a crucial technology for achieving USV autonomous navigation. Under global path planning, dynamic local obstacle avoidance has become the primary focus for safe USV navigation. In this study, a USV autonomous dynamic obstacle avoidance method based on the enhanced velocity obstacle method is proposed in order to achieve path replanning. Through further analysis of obstacles, the obstacle geometric model set in the conventional velocity obstacle method was redefined. A special triangular obstacle geometric model was proposed to reconstruct the velocity obstacle region. The collision time was predicted by fitting the previously gathered data to the detected obstacle’s distance, azimuth, and other relevant data. Then, it is combined with the collision risk to determine when obstacle avoidance should begin and end. In order to ensure safe driving between path points, the international maritime collision avoidance rules (COLREGs) are incorporated to ensure the accuracy of obstacle avoidance. Finally, through numerical simulations of various collision scenarios, it was determined that, under the assumption of ensuring a safe encounter distance, the maximum change rates of USV heading angle are optimized by 17.54%, 58.16%, and 28.63% when crossing, head-on, and overtaking, respectively. The results indicate that, by optimizing the heading angle, the enhanced velocity obstacle method can avoid the risk of ship rollover caused by an excessive heading angle during high-speed movement and achieve more accurate obstacle avoidance action in the event of a safety encounter.

To solve the problem of on-line dynamic planning of mobile robots in unknown environments, inspired by the mechanism of idiotypic network hypothesis, a hybrid immune network algorithm (HINA) is proposed. To improve the planning efficiency of immune network algorithm (INA) and realize optimal on-line dynamic obstacle avoidance, a new adaptive artificial potential field (AAPF) method is presented by using modified potential field. The vaccine is extracted according to the planning results based on AAPF method, and the instruction definition of robot is initialized through vaccine inoculation, which improve the planning efficiency of INA. When the robot meets with moving obstacles during the path planning, the AAPF method is used for the optimal dynamic obstacle avoidance. Simulation results are presented to verify the effectiveness of the proposed algorithm in unknown environments.

According to the requirements of real-time obstacle avoidance of unmanned vehicles, an improved artificial potential field local obstacle avoidance path planning algorithm is proposed. Considering the influence factors of vehicle kinematics and dynamics, an improved distance adjustment factor is added to the static obstacle potential field function to construct the water drop repulsion potential field, which improves the efficiency of path planning; The relative velocity function and relative acceleration function are added to the potential field function of dynamic obstacles, which solves the problem that the traditional potential field method has no solution under the dynamic obstacle avoidance condition. The Bessel curve is used to smooth the path, and finally a fast, efficient and collision-free optimal path is generated. Using the Prescan-CarSim-Matlab/Simulink joint simulation platform, the effectiveness of the proposed path planning algorithm is verified under the conditions of lane changing obstacle avoidance of static obstacles and deceleration obstacle avoidance of lateral dynamic obstacles. The simulation results show that compared with the traditional potential field method, the change of heading angle obtained by the improved potential field method is reduced by 84% and the stability of dynamic obstacle avoidance is improved.

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.

The Vertical Takeoff and Landing Aircraft (VTOL) is capable of both low-speed hovering and high-speed flight. This paper proposes an autonomous motion planning and anti-wind disturbance control algorithm for the VTOL, implemented spatial and temporal joint motion planning for VTOL, under the limitation of maximum flight speed of the aircraft, VTOL can achieve fully autonomous flight in complex environments, which can realize the autonomous flight of a single aircraft in a complex and dense environment, achieve real-time perception and avoidance of static and dynamic obstacles. The information generated by motion planning includes the position, velocity, and acceleration information of the aircraft, and proposes a wind disturbance estimation and active control strategy in a wind disturbance environment, accurate estimation of wind disturbance is achieved through a state observer, and the wind disturbance was suppressed through nonlinear error feedback control law, implemented wind disturbance estimation and suppression for aircraft in wind disturbance environments, and achieved complete autonomous motion planning and trajectory tracking of VTOL in a complex and wind disturbance environment. Our work can provide a basis for the collaborative control of multiple UAVs.

An intelligent based local path planning algorithm is proposed enabling a four-wheeled differential mobile robot to avoid both static and moving obstacles. The developed intelligent method named as feedback compensation neural network (FCNN) refers to improving traditional back propagation neural network (BPNN). With respect to local path planning, the structure of FCNN is composed of two BPNNs. The first BPNN (main NN) is properly trained, playing a dominant role in dynamic obstacle avoidance. Subsequently, the second BPNN (compensation NN) is trained online in order to obtain the compensation for the output. Moreover, the FCNN can predict the motion of dynamic obstacles according to the previous obstacle avoidance situation, even if the obstacle velocity continuously changes. In such a way, the robot collision avoidance performance is highly enhanced, especially in the presence of moving obstacles changing their velocities and heading. The effectiveness of the presented algorithm is illustrated by simulation results.

This paper analyzes the inapplicability of the traditional APF in the dynamic environment and the existence of local minimum. In this paper, a new potential field function is proposed. Combined with the fuzzy algorithm, the ratio of the absolute value of the difference between the gravitational force and the repulsive force of the UAV to the resultant force and their angle difference are taken as two fuzzy inputs. With the help of the fuzzy rules, the fuzzy output force is calculated to break the local minimum value.in the dynamic environment, the relative velocity repulsion potential field between UAV and dynamic obstacles is introduced to solve the problem of dynamic obstacle avoidance. The experimental simulation results show that the obstacle avoidance track drawn by the improved APF regulation in this paper is smoother and the obstacle avoidance track correlation data is better.

Background Collision detection is crucial in the design of robot planning algorithms. Efficient distance sensors can provide high-resolution environmental collision information to the robot's planning algorithm. However, this also leads to the robot obstacle avoidance performance being limited by the performance of the sensors. Therefore, it becomes a challenge to achieve efficient obstacle avoidance with low-resolution environmental information. Methods First, we use a self-developed capacitive array non-contact distance sensing flexible surface for sensing the proximity of colliding objects. Second, we designed an optimization-based dynamic obstacle avoidance planning algorithm, using only the minimum separation distance and penetration direction as obstacle avoidance information, and referring to the idea of stochastic gradient descent, using real-time collision avoidance information to do single-step optimization adjustment. Results We conducted the dynamic obstacle avoidance test experiment by connecting the electronic skin to the semi-physical prototype and the full physical prototype. The experiments show that efficient dynamic obstacle avoidance can be realized under the maximum effective range of only 5~7cm, and it has strong flexibility to avoid different shapes of dynamic obstacles in a non-contact manner, and finally arrive at the target position. Conclusions In this paper, an online obstacle avoidance planning algorithm designed based on an optimization method that is not limited to the shape of obstacles is proposed, and the effectiveness of the algorithm is verified by physical experiments in combination with a self-developed flexible distance sensing surface. It is of great significance for the safe operation of human-robot interaction in collaborative robots.

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

Aiming at the problem of multi-objective path planning (MOPP) and dynamic obstacle avoidance of manipulator in dynamic environment, this paper proposes a solution based on hierarchical reinforcement learning (HRL) framework. Traditional path planning methods have some problems in dynamic scenes, such as poor real-time performance, difficult to balance multi-objective conflicts and insufficient adaptability to environmental changes. Therefore, this paper designs a two-tier architecture including global path planning layer and local obstacle avoidance layer, which is trained by Proximal policy optimization (PPO) and Soft Actor-Critic (SAC) algorithms respectively, and achieves collaborative optimization through an efficient information exchange mechanism between layers. At the same time, a multi-objective reward function based on dynamic weight adjustment strategy is introduced. Fuzzy logic is used to adaptively balance the relationship among path length, obstacle avoidance safety and energy consumption according to environmental complexity. Combined with Long Short-Term Memory (LSTM), the trajectory of obstacles is predicted, and the potential field method is further introduced to modify the obstacle avoidance reward, which improves the real-time response ability and robustness of the algorithm in dynamic environment. The experimental results show that the HRL-SAC-PPO method proposed in this paper shows superior performance in both static and dynamic scenarios. In the static scene, the success rate of this method reaches 100%, the average path length is shortened to 2.13m, no collision occurs, and the energy consumption is reduced to 1.12 kJ, which shows a good multi-objective optimization effect. In the dynamic scene, the trajectory error of obstacles predicted by LSTM is only 4.2%, and the safe distance between the robot arm and obstacles is improved by 35%, which significantly enhances the reliability of obstacle avoidance. In addition, the average decision delay of this method is only 11.3ms, and the peak delay is 23ms, which is much lower than that of the contrast algorithm, showing stronger real-time response ability. The ablation experiment further verified the key role of LSTM trajectory prediction, dynamic weight adjustment and layered structure on the overall performance. In the welding task verification of the real UR5 manipulator, the success rate of the system in dynamic environment is 95.3%, the average path length is 3.41m, and the maximum joint acceleration is only 0.87 radian/s², which is far below the safety threshold, indicating that the algorithm has good stability and obstacle avoidance ability in practical application. The comprehensive performance comparison shows that this method performs well in different industrial scenarios, and has stronger environmental adaptability and comprehensive path planning ability.

Group animation technology is widely used in the fields of virtual reality, intelligent simulation and game development, the core of which lies in simulating the autonomous movement and interaction behaviour of large-scale intelligent bodies in complex environments. However, there is still room for optimising the motion coordination and environment adaptability of intelligent bodies. In order to improve the performance of group animation in complex environments, a group animation algorithm combining the firefly algorithm and the leader-follower mechanism is designed. By combining global optimisation and local cooperative control, the path planning and motion control of the intelligent body are optimised. The experimental results show that in a small-scale scene with a dataset size of 50, the path smoothness of the proposed algorithm under the study is 0.94, the average deviation of the intelligent body is reduced to 0.92m, and the growth rate of the path length is 8.30%. In a 50 × 25 smart body and obstacle environment, the algorithm’s path smoothness is improved to 0.91, the average deviation of the smart body is reduced to 1.36m, and the path growth rate is reduced to 10.7%. And the success rate of obstacle avoidance of the algorithm is still maintained at 0.92 when the size of the dataset is increased to 600.The study shows that the algorithm has better performance in path planning and obstacle avoidance in complex environments. The proposed algorithm provides an efficient, stable and adaptable solution for group animation control in virtual reality.

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.

Robot path planning is a cornerstone of precision agriculture, enabling safe and efficient operations for agricultural robots. However, complex field environments—characterized by static and dynamic obstacles, dense vegetation, and unstructured terrain—pose significant challenges to effective path planning. Conventional methods, such as A*, Dijkstra, and rapidly exploring random tree (RRT), exhibit limitations in efficiency and adaptability to dynamic conditions. To address these challenges, this study introduces AgriPath, a robust multi-objective path planning framework that integrates an improved convolutional neural network (CNN), an improved A* algorithm, and an improved whale optimization algorithm (IWOA) to optimize pathfinding, convergence efficiency, and obstacle avoidance in complex agricultural settings. Key innovations include an improved CNN leveraging causal convolution and multi-head self-attention mechanisms to improve temporal modeling for short-term trajectory prediction, augmented by Gaussian perturbations to enhance initial solution diversity; an improved A* algorithm incorporating dynamic heuristic functions based on Normalized Difference Vegetation Index (NDVI), combined with Kalman filtering, to bolster global path adaptability; IWOA employing non-linear convergence factors and differential evolution mechanisms to dynamically balance path length, smoothness, and planning time; and an improved Douglas–Peucker algorithm paired with cubic B-spline smoothing and navigation command modules to ensure path simplification and real-time execution. Experiments conducted in the Modern Agricultural Demonstration Zone at Chengdu, Sichuan Province, China, across simple, moderate, and complex scenarios, demonstrate that AgriPath outperforms advanced algorithms—SBREA*, Ant Colony A*, Orchard A*, and Greedy A*—in path length, smoothness, planning time, and dynamic obstacle avoidance success rate, indicative of superior multi-objective optimization balance. This study significantly enhances the efficiency and robustness of agricultural robot path planning, offering a more adaptive solution for autonomous navigation in precision agriculture while providing new theoretical and practical directions for the field of path planning.