Near-optimal Path Research Articles

UAV Path Planning Based on Butterfly Optimization Algorithm in Three-Dimensional Space

The BOA is a novel optimization algorithm, which is inspired by the butterfly and enables the searching for the best solutions in a respective search area. The algorithm can be set to targeted goals like the amount of distance needed to cover, or/and the presence of an obstacle or the completion of the particular mission objectives. I applied the BOA to generate paths of UAVs on a three-dimensional space and considered the objectives of collision urgency, energy consumption, and near-optimal path planning. Specifically for the assessment of the algorithm, I simulated the application of MATLAB and apply multiple scenarios both on two-dimensional and on three-dimensional environments. I also benchmarked the BOA with two other algorithms including the Ant Colony Optimization and Particle Swarm Optimization (PSO). The results proved that the BOA performed better than the GA in terms of cost function and the time required to arrive at the optimal solution especially in 3D solid terrain. By analyzing the simulation results, the flexibility of the BOA in a 3D environment is evident when new changes take place in the environment. Moreover, the algorithm showed rather swift reaction in terms of path acting in response to various unexpected obstacles. The proposed BOA is viable for the path planning of UAVs in three-dimensional space and effective compared to the other optimization algorithms.

Path Planning Algorithm for Manipulators in Complex Scenes Based on Improved RRT.

Aiming at the problems of a six-degree-of-freedom robotic arm in a three-dimensional multi-obstacle space, such as low sampling efficiency and path search failure, an improved fast extended random tree (RRT*) algorithm for robotic arm path planning method (abbreviated as HP-APF-RRT*) is proposed. The algorithm generates multiple candidate points per iteration, selecting a sampling point probabilistically based on heuristic values, thereby optimizing sampling efficiency and reducing unnecessary nodes. To mitigate increased search times in obstacle-dense areas, an artificial potential field (APF) approach is integrated, establishing gravitational and repulsive fields to guide sampling points around obstacles toward the target. This method enhances path search in complex environments, yielding near-optimal paths. Furthermore, the path is simplified using the triangle inequality, and redundant intermediate nodes are utilized to further refine the path. Finally, the simulation experiment of the improved HP-APF-RRT* is executed on Matlab R2022b and ROS, and the physical experiment is performed on the NZ500-500 robotic arm. The effectiveness and superiority of the improved algorithm are determined by comparing it with the existing algorithms.

Optimizing Sampling Points and Path Planning for Soil Monitoring in Agricultural Land

Current soil mixing sampling surveys face several challenges. These challenges include significant variations in the size and shape of farmland plots, which complicate the sampling process. Additionally, the procedures for establishing mixing points are often cumbersome and non-standardized. Furthermore, when the number of mixing points is large and the plot area is extensive, sampling efficiency is significantly reduced. In this study, an automated method for establishing the layout of sampling points and a threshold-based mixed path planning algorithm are proposed. These approaches are designed to automatically arrange sampling points within delineated sampling units according to various sampling methods. Additionally, real-time path planning is conducted on the basis of the initial position of the sampling vehicle. The experimental results indicate that the maximum relative error in estimating farmland area is 0.22%, which is less than 666.67 m2. The average deviation between the locations of the sampling points generated by the sampling point layout algorithm and those established via related software is 0.0336 m. Furthermore, the threshold-based hybrid algorithm consistently yields optimal or near-optimal paths while reducing the path planning time, with an average duration of 1.0783 s for path planning. This study provides reliable technical support for standardizing the sampling point layout and enhancing the soil sampling efficiency.

Cooperative Mission Planning Using Rollout Policy Optimization

This paper proposes a novel cooperative mission planning for uncrewed aerial systems (UAS), using roll-out policy optimization and a blend of straight and double-Archimedean-spiral (DAS) paths. Typical mission tasks using UAS, possibly multiple, include visiting distinct locations/waypoints and covering specific areas of interest (AOIs). For the UAS, to guarantee the coverage of the AOI, the algorithm generates a DAS path that even accounts for the sensor’s field-of-view constraints. A scalar spiral density parameter in the algorithm effectively provides customized adjustments to the length of the DAS over an AOI while developing mission plans. A lower and upper bound value for the spiral density parameter and the dependency of the parameter on the coverage of an AOI are derived. This scalar parameter gives the users potential flexibility in the design of mission plans. Roll-out policy optimization minimizes the overall path length for the cooperative UAS mission and, as a direct consequence, minimizes time and fuel. This method leads to finding a near-optimal path in that the computation time does not grow exponentially with the number of UAS. Results from the simulation studies with several cooperative mission scenarios, including multiple UASs visiting several waypoints and AOIs starting from identical and distinct initial locations, demonstrate the efficacy of the proposed concept. The results from a data-based complex test case using the LiDAR data set further demonstrate the adaptability of the proposed mission planning approach in a real-world setting.

An efficient path planning approach for autonomous multi-UAV system in target coverage problems

PurposeThe purpose of this paper is to present an efficient path planning method for the multi-UAV system in target coverage problems.Design/methodology/approachAn enhanced particle swarm optimizer (PSO) is used to solve the path planning problem, which concerns the two-dimensional motion of multirotor unmanned aerial vehicles (UAVs) in a three-dimensional environment. Enhancements include an improved initial swarm generation and prediction strategy for succeeding generations. Initial swarm improvements include the clustering process managed by fuzzy c-means clustering method, ordering procedure handled by ant colony optimizer and design vector change. Local solutions form the foundation of a prediction strategy.FindingsNumerical simulations show that the proposed method could find near-optimal paths for multi-UAVs effectively.Practical implicationsSimulations indicate the proposed method could be deployed for autonomous multi-UAV systems with target coverage problems.Originality/valueThe proposed method combines intelligent methods in the early phase of PSO, handles obstacle avoidance problems with a unique approach and accelerates the process by adding a prediction strategy.

Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy

Mobile robot path planning based on bi-population particle swarm optimization with random perturbation strategy

TargetTree-RRT*: Continuous-Curvature Path Planning Algorithm for Autonomous Parking in Complex Environments

Rapidly-exploring random tree (RRT) has been studied for autonomous parking as it quickly finds an initial path and is easily scalable in complex environments. However, the planning time increases by searching for the path in narrow parking spots. To reduce the planning time, the target tree algorithm, which substitutes a parking goal in RRT with a set (target tree) of backward parking paths, was proposed. However, as it consists of circular and straight paths, it deteriorates parking accuracy because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the TargetTree-RRT* algorithm for complex environments. First, a target tree is designed using clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to initialize a proper target tree that considers obstacles. By integrating with optimal-variant RRT and searching for the shortest path, the proposed TargetTree-RRT* algorithm obtains a near-optimal path as the sampling time increases. Experiment results in real environments showed that the vehicle parked more accurately, and continuous-curvature paths were obtained more quickly and with higher success rates than those acquired using other sampling-based and other types of planning algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work was motivated by the need to develop a fast and practical path planning algorithm for autonomous parking, not only in simple environments but also in complex environments. Sampling-based planning algorithms have been studied in this area, with the advantages of rapid planning of an initial path and easily reflecting vehicle’s constraints. Nevertheless, in cluttered environments, it needs considerable time to obtain a path for the autonomous vehicle easy for tracking and precise parking. To address the aforementioned issue, this article presents the TargetTree-RRT* algorithm. It finds a path by replacing a parking goal with a set of pre-defined continuous-curvature paths considering cluttered environments. TargetTree-RRT* achieves better performance for real parking scenarios and rapidly obtains a near-optimal parking path, compared to other parking path planners. The proposed algorithm is not limited to autonomous vehicles. It can be applied to other unmanned vehicles or robots, such as autonomous underwater vehicles (AUVs). In any application where the goal of path planning can be replaced with predefined standardized paths, the proposed algorithm can be extended.

NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-and-conquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven owing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the state-of-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints.

Autonomous hierarchy creation for computationally feasible near-optimal path planning in large environments

Autonomous hierarchy creation for computationally feasible near-optimal path planning in large environments

RRT path planning algorithm with enhanced sampling

Due to the low efficiency and low path quality of the RRT algorithm, this paper introduces a sampling-enhanced RRT(SE-RRT) algorithm with four improvements to the RRT algorithm. In the path search phase, the gravitational coefficient is added to improve the efficiency of finding the feasible path without losing randomness. In the path optimization phase, turning points are selected from nodes of the original path by global rewiring. Then the sampling space is established around the turning points for local re-optimization. Finally, a near-optimal path is obtained and can be iterated to the optimal path according to the demand. In the code implementation phase, efficient path collision detection is added to prevent local paths from crossing obstacles.

Data-Driven Decision Making and Near-Optimal Path Planning for Multiagent System in Games

Data-Driven Decision Making and Near-Optimal Path Planning for Multiagent System in Games

Efficient Multi-UAV Path Planning for Collaborative Area Search Operations

Efficient UAV path-planning algorithms significantly improve inspection efficiency and reduce costs. However, due to the limitation of battery capacity, the endurance of existing UAVs is limited, making it difficult for them to directly undertake information collection, cruising, and inspection tasks over large work areas. This paper considers the problem of path allocation for multiple UAVs to minimize work time and reports research on multi-UAV (unmanned aerial vehicle) multi-task long-duration operation path planning. We propose a multi-UAV collaborative search algorithm based on the greedy algorithm (MUCS-GD) and a multi-UAV collaborative search algorithm based on the binary search algorithm (MUCS-BSAE), and later apply two UAV collaborative search algorithms to five UAV flight paths: (1) a snake curve path, (2) a “square wave signal” curve path, (3) a Peano curve path, (4) a Hilbert curve path, and (5) a Moore curve path, and compare the simulation results. We found that the performance of MUCS-BSAE was better than that of MUCS-GD in all of the above flight paths. In addition, the path with the “square wave signal” curve was the near-optimal path among all the flight paths. Finally, we improved the MUCS-BSAE applied on the “square wave signal” curve path and obtained an improved collaborative search algorithm for multiple UAVs based on the binary search algorithm (IMUCS-BSAE), which further reduced the working time of the UAV.

Long-Strip Target Detection and Tracking with Autonomous Surface Vehicle

As we all know, target detection and tracking are of great significance for marine exploration and protection. In this paper, we propose one Convolutional-Neural-Network-based target detection method named YOLO-Softer NMS for long-strip target detection on the water, which combines You Only Look Once (YOLO) and Softer NMS algorithms to improve detection accuracy. The traditional YOLO network structure is improved, the prediction scale is increased from threeto four, and a softer NMS strategy is used to select the original output of the original YOLO method. The performance improvement is compared totheFaster-RCNN algorithm and traditional YOLO methodin both mAP and speed, and the proposed YOLO–Softer NMS’s mAP reaches 97.09%while still maintaining the same speed as YOLOv3. In addition, the camera imaging model is used to obtain accurate target coordinate information for target tracking. Finally, using the dicyclic loop PID control diagram, the Autonomous Surface Vehicle is controlled to approach the long-strip target with near-optimal path design. The actual test results verify that our long-strip target detection and tracking method can achieve gratifying long-strip target detection and tracking results.

Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey

Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Optimization of path planning refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOOs present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. The purpose of this paper is to present an overview of mobile robot navigation strategies employed to find the path that has the minimum number of criteria (shortest, smoothness, and safest) so far. Here, multi objective approaches are discussed in detail in order to identify research gaps. In addition, it is important to understand how path planning strategies are developed under various environmental circumstances.

Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion

Collision-free navigation of mobile robots is a challenging task, especially in unknown environments, and various studies have been carried out in this regard. However, the previous studies have shortcomings, such as low performance in cluttered and unknown environments, high computational costs, and multiple controller models for navigation. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) and global positioning system (GPS) for control and navigation to overcome these problems. The proposed method automates the navigation of a mobile robot while averting obstacles in unknown and densely cluttered environments. Furthermore, the mobile robots’ global path planning and steering are controlled using GPS and heading sensor data fusion to achieve the target coordinates. A fuzzy inference system (FIS) is adopted to model obstacle avoidance where distance sensors data is converted into fuzzy linguistics. Moreover, a type-1 Takagi–Sugeno FIS is used to train a five-layered neural network for the local planning of the robot, and ANFIS parameters are tuned using a hybrid learning method. In addition, an algorithm is designed to generate a dataset for testing and training the ANFIS controller. All the testing and training are conducted in MATLAB, while simulations are carried out using CoppeliaSim. Comprehensive experiments are performed to validate the robustness of the proposed method. The results of the experiments show that the proposed approach outperforms various state-of-the-art neuro-fuzzy, CS-ANFIS, multi-ANFIS, and hybrid ANFIS navigation and obstacle avoidance methods in finding a near-optimal path in unknown environments.

Flexible networked rural electrification using levelized interpolative genetic algorithm

• Better electrification methods are needed for achieving SDG7. • MAA* algorithm is efficient but, like other methods, provides only a single optimal path. • LIGA, a modified genetic algorithm, has been developed to identify family of coarse paths. • LIGA and MAA* are used in conjunction to identify family of near-optimal paths. • This hybrid method provides high flexibility for optimal network design and implementation. Networked rural electrification is an alternative approach to accelerate rural electrification. Using satellite photos and GIS tools, an electrical distribution network is used to connect villages and properly located generation facilities together to reduce electrification cost. To design the network, optimal paths connecting all node-pairs are identified, followed by finding a network topology that minimizes cost. Earlier work has illustrated that A* (A-star, an optimal path-finding algorithm) is inefficient for this application due to the complex topography in rural areas. The multiplier-accelerated A* (MAA*) algorithm overcomes key performance issues, but, like A*, produces only one path connecting each node-pair. Relying on one path increases project risk because adverse conditions, such as inaccurate GIS estimation, unexpected soil conditions, land-rights disputes, political issues, etc. can occur during implementation. In this paper, a hybrid path-finding method combining genetic algorithm and A* / MAA* algorithm is proposed. The proposed method provides a family of near-optimal paths instead of a single optimal path for routing. A family of paths allows a project implementer to quickly adapt to unexpected situations as new information becomes available, and flexibly change network topology before or during implementation with minimal impact on project cost.

ALBRL: Automatic Load-Balancing Architecture Based on Reinforcement Learning in Software-Defined Networking

Due to the rapid development of network communication technology and the significant increase in network terminal equipment, the application of new network architecture software-defined networking (SDN) combined with reinforcement learning in network traffic scheduling has become an important focus of research. Because of network traffic transmission variability and complexity, the traditional reinforcement-learning algorithms in SDN face problems such as slow convergence rates and unbalanced loads. The problems seriously affect network performance, resulting in network link congestion and the low efficiency of inter-stream bandwidth allocation. This paper proposes an automatic load-balancing architecture based on reinforcement learning (ALBRL) in SDN. In this architecture, we design a load-balancing optimization model in high-load traffic scenarios and adapt the improved Deep Deterministic Policy Gradient (DDPG) algorithm to find a near-optimal path between network hosts. The proposed ALBRL uses the sampling method of updating the experience pool with the SumTree structure to improve the random extraction strategy of the empirical-playback mechanism in DDPG. It extracts a more meaningful experience for network updating with greater probability, which can effectively improve the convergence rate. The experiment results show that the proposed ALBRL has a faster training speed than existing reinforcement-learning algorithms and significantly improves network throughput.

A New Fast Ant Colony Optimization Algorithm: The Saltatory Evolution Ant Colony Optimization Algorithm

Various studies have shown that the ant colony optimization (ACO) algorithm has a good performance in approximating complex combinatorial optimization problems such as traveling salesman problem (TSP) for real-world applications. However, disadvantages such as long running time and easy stagnation still restrict its further wide application in many fields. In this study, a saltatory evolution ant colony optimization (SEACO) algorithm is proposed to increase the optimization speed. Different from the past research, this study innovatively starts from the perspective of near-optimal path identification and refines the domain knowledge of near-optimal path identification by quantitative analysis model using the pheromone matrix evolution data of the traditional ACO algorithm. Based on the domain knowledge, a near-optimal path prediction model is built to predict the evolutionary trend of the path pheromone matrix so as to fundamentally save the running time. Extensive experiment results on a traveling salesman problem library (TSPLIB) database demonstrate that the solution quality of the SEACO algorithm is better than that of the ACO algorithm, and it is more suitable for large-scale data sets within the specified time window. This means it can provide a promising direction to deal with the problem about slow optimization speed and low accuracy of the ACO algorithm.

Hybrid approach for multi-objective optimization path planning with moving target

Path planning algorithms are the most significant area in the robotics field. Path planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Path planning optimization refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. Several objectives are considered as part of this study, including path security, length, and smoothness, when planning paths for autonomous mobile robots in a dynamic environment with a moving target. Particle swarm optimization (PSO) algorithms are combined with bat algorithms (BA) to make a balance between exploration and exploitation. PSO algorithms used to optimize two important parameters of the bat algorithm. The proposed solution is tested through several simulations based on varying scenarios. The results demonstrate that mobile robots can travel clearly and safely along short paths and smoothly, proving this method's efficiency.

An Algorithm to Warm Start Perturbed (WASP) Constrained Dynamic Programs

Receding horizon optimal control problems compute the solution at each time step to operate the system on a near-optimal path. However, in many practical cases, the boundary conditions, such as external inputs, constraint equations, or the objective function, vary only marginally from one time step to the next. In this case, recomputing the optimal solution at each time represents a significant burden for real-time applications. This paper proposes a novel algorithm to approximately solve a perturbed constrained dynamic program that significantly improves the computational burden when the objective function and the constraints are perturbed slightly. The method hinges on determining closed-form expressions for first-order perturbations in the optimal strategy and the Lagrange multipliers of the perturbed constrained dynamic programming problem. This information can be used to initialize any algorithm (such as the method of Lagrange multipliers, or the augmented Lagrangian method) to solve the perturbed dynamic programming problem with minimal computational resources.