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To address the shortcomings of traditional bionic algorithms in path planning, such as inefficient search processes, extended planning distances and times, and suboptimal dynamic obstacle avoidance, this paper introduces a fusion algorithm called NRBO-DWA. This algorithm is specifically applied to plan the path for a tube-changing robot in a knitting workshop. The process begins with spatial modeling based on the actual parameters of the workshop, followed by the development of a comprehensive, objective function for the robot in line with the relevant constraints. The NRBO algorithm is then integrated with the DWA algorithm to boost its dynamic obstacle avoidance capabilities, while a path correction mechanism is introduced to minimize unnecessary detours. Finally, a comparative experiment is designed to evaluate the algorithm against the GA, PSO, and SSA algorithms. Simulation results demonstrate that in a dynamically complex 3D environment, the NRBO-DWA algorithm outperforms in terms of higher 3D search efficiency, shorter total path length, and faster planning times.

The cultivation model for spindle-shaped apple trees is widely used in modern standard apple orchards worldwide and represents the direction of modern apple industry development. However, without an effective obstacle avoidance path, the robotic arm is prone to collision with obstacles such as fruit tree branches during the picking process, which may damage fruits and branches and even affect the healthy growth of fruit trees. To address the above issues, a three-dimensional path -planning algorithm for full-field fruit obstacle avoidance harvesting for spindle-shaped fruit trees, which are widely planted in modern apple orchards, is proposed in this study. Firstly, based on three typical tree structures of spindle-shaped apple trees (free spindle, high spindle, and slender spindle), a three-dimensional spatial model of fruit tree branches was established. Secondly, based on the grid environment representation method, an obstacle map of the apple tree model was established. Then, the initial pheromones were improved by non-uniform distribution on the basis of the original ant colony algorithm. Furthermore, the updating rules of pheromones were improved, and a biomimetic optimization mechanism was integrated with the beetle antenna algorithm to improve the speed and stability of path searching. Finally, the planned path was smoothed using a cubic B-spline curve to make the path smoother and avoid unnecessary pauses or turns during the harvesting process of the robotic arm. Based on the proposed improved ACO algorithm (ant colony optimization algorithm), obstacle avoidance 3D path planning simulation experiments were conducted for three types of spindle-shaped apple trees. The results showed that the success rates of obstacle avoidance path planning were higher than 96%, 86%, and 92% for free-spindle-shaped, high-spindle-shaped, and slender-spindle-shaped trees, respectively. Compared with traditional ant colony algorithms, the average planning time was decreased by 49.38%, 46.33%, and 51.03%, respectively. The proposed improved algorithm can effectively achieve three-dimensional path planning for obstacle avoidance picking, thereby providing technical support for the development of intelligent apple picking robots.

The Sine–Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm’s capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications.

This paper exploits the concept of nonlinear sliding surfaces to be used as a basis in the development of aerial path planning projects involving aerobatic three-dimensional path curves in the presence of disturbances. This approach can be used for any kind of unmanned aerial vehicle aimed at performing aerobatic maneuvers. Each maneuver is associated with a nonlinear surface on which an aerial vehicle could be driven to slide. The surface design exploits the properties of Viviani’s curve and the Hopf bifurcation. A vector form of the super twisting algorithm steers the vehicle to the prescribed surfaces. A suitable switching control law is proposed to shift between surfaces at different time instants. A practical stability analysis that involves the descriptor approach allows for determining the controller gains. Numerical simulations are developed to illustrate the accomplishment of the suggested aerobatic flight.

In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.

Unmanned Aerial Vehicles (UAVs) equipped with communication technologies have gained significant attention as a promising solution for providing wireless connectivity in remote, disaster-stricken areas lacking communication infrastructure. However, enabling UAVs to provide communications (e.g., UAVs acting as flying base stations) in real scenarios requires the integration of various technologies and algorithms. In particular 3D path planning algorithms are crucial in determining the optimal path free of obstacles so that UAVs in isolation or forming networks can provide wireless coverage in a specific region. Considering that most of the existing proposals in the literature only address path planning in a 2D environment, this paper systematically studies existing path-planning solutions in UAVs in a 3D environment in which optimization models (optimal and heuristics) have been applied. This paper analyzes 37 articles selected from 631 documents from a search in the Scopus database. This paper also presents an overview of UAV-enabled communications systems, the research questions, and the methodology for the systematic mapping study. In the end, this paper provides information about the objectives to be minimized or maximized, the optimization variables used, and the algorithmic strategies employed to solve the 3D path planning problem.

The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.

Path planning is widely used in many domains, and it is crucial for the advancement of map navigation, autonomous driving, and robot path planning. However, existing path planning methods have certain limitations for complex field scenes with undulating terrain and diverse landcover types. This paper presents an energy-efficient 3D path planning algorithm based on an improved A* algorithm and the particle swarm algorithm in complex field scenes. The evaluation function of the A* algorithm was improved to be suitable for complex field scenes. The slope parameter and friction coefficient were respectively used in the evaluation function to represent different terrain features and landcover types. The selection of expanding nodes in the algorithm depends not only on the minimum distance but also on the minimum consumption cost. Furthermore, the turning radius factor and slope threshold factor of vehicles were added to the definition of impassable points in the improved A* algorithm, so that the accessibility of path planning could be guaranteed by excluding some bends and steep slopes. To meet the requirements for multi-target path planning, the improved A* algorithm was used as the fitness function of the particle swarm algorithm to solve the traveling salesman problem. The experimental results showed that the proposed algorithm is capable of multi-target path planning in complex field scenes. Furthermore, the path planned by this algorithm is more passable and more energy efficient. In this experimental environment model, the average energy-saving efficiency of the path planned by the improved algorithm is 14.7% compared to the traditional A* algorithm. This would be beneficial to the development of ecotourism and geological exploration.

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments.