Efficient Path Planning Research Articles

Autonomous UAV Path-Planning Optimization Using Metaheuristic Approach for Predisaster Assessment

In this article, different state-of-the-art metaheuristic algorithms are analyzed to incorporate the collision-free path-planning approach for UAVs in a disaster situation. The efficient path planning is used to identify the main cause of disaster like bushfires that are badly affecting the forest ecosystem throughout the globe. This novel approach is a first step toward a predisaster assessment and possibilities to save the survivors in minimal time. Different metaheuristic algorithms, such as PSO, GWO, WOA, BMO, and DGBCO, are compared for UAV path optimization capability. In order to test the robustness of our proposed model, four different scenarios are presented which include general environment, condense environment, maze environment, and dynamic environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for a UAV to reach the destination. The exploration and exploitation groups working simultaneously in a dynamic environment make it effective for UAV path planning using the DGBCO algorithm. Based on the parameters selected, the DGBCO algorithm outperforms other algorithms and achieves 24.5% less transportation cost and 13.3% less computational time. Hence, DGBCO can be efficiently applied for UAV path-planning optimization in any of the aforementioned environments.

Efficient time-optimal path planning of AUV under the ocean currents based on graph and clustering strategy

Path planning is particularly important for Autonomous Underwater Vehicles (AUVs) in the marine environments. Due to the existence of flow fields, many conventional path planning methods are not effective. As the flow distributions vary slowly in large spatial scales, this paper considers the influence of static non-uniform vector field and studies the time-optimal path planning of AUV. The method proposed in this paper is called GK-OPM (Graph and K-means based Optimization Method). GK-OPM generates initial paths for minimum-time path optimization using digraph and K-means method, which benefit optimality and efficiency, respectively. To discuss the parameters in GK-OPM and compare the performance with genetic algorithm (GA) and particle swarm optimization (PSO), Monte Carlo simulation is carried out under the ocean currents simulated by the Lamb-vertex function. The feasibility is verified on discrete ocean data and three-dimensional environments, too. The results show that GK-OPM has about 6–8 times better efficiency than GA and PSO and maintains optimality at the same time.

RRT*-based Path Planning for Continuum Arms.

Continuum arms are bio-inspired devices that exhibit continuous, smooth bending and generate motion through structural deformation. Rapidly-exploring random trees (RRT) is a traditional approach for performing efficient path planning. RRT approaches are usually based on exploring the configuration space (C-Space) of the robot to find a desirable work space (W-Space) path. Due to the complex kinematics and the highly non-linear mapping between the C-Space and W-Space of continuum arms, a high-quality path in the C-Space (e.g., a linear path) may not correspond to a desirable path/movement in the W-Space. Consequently, the C-Space RRT approaches that are based on C-Space cost functions do not lead to reliable and effective path planning when applied to continuum arms. In this paper, we propose a RRT* path planning approach for continuum arms that is based on exploring the W-Space of the robot as opposed to its C-Space. We show the successful applications of the proposed W-Space RRT* path planner in performing path planning with obstacle avoidance and in performing trajectory tracking. In all the aforementioned tasks, the quality of the paths generated by the proposed planner is superior to that of previous approaches and to its counterpart C-Space based RRT* approach, while the paths are generated in substantially less time.

Deep Reinforcement Learning for Intelligent Dual-UAV Reconnaissance Mission Planning

The reconnaissance of high-value targets is prerequisite for effective operations. The recent appreciation of deep reinforcement learning (DRL) arises from its success in navigation problems, but due to the competitiveness and complexity of the military field, the applications of DRL in the military field are still unsatisfactory. In this paper, an end-to-end DRL-based intelligent reconnaissance mission planning is proposed for dual unmanned aerial vehicle (dual UAV) cooperative reconnaissance missions under high-threat and dense situations. Comprehensive consideration is given to specific mission properties and parameter requirements through the whole modelling. Firstly, the reconnaissance mission is described as a Markov decision process (MDP), and the mission planning model based on DRL is established. Secondly, the environment and UAV motion parameters are standardized to input the neural network, aiming to deduce the difficulty of algorithm convergence. According to the concrete requirements of non-reconnaissance by radars, dual-UAV cooperation and wandering reconnaissance in the mission, four reward functions with weights are designed to enhance agent understanding to the mission. To avoid sparse reward, the clip function is used to control the reward value range. Finally, considering the continuous action space of reconnaissance mission planning, the widely applicable proximal policy optimization (PPO) algorithm is used in this paper. The simulation is carried out by combining offline training and online planning. By changing the location and number of ground detection areas, from 1 to 4, the model with PPO can maintain 20% of reconnaissance proportion and a 90% mission complete rate and help the reconnaissance UAV to complete efficient path planning. It can adapt to unknown continuous high-dimensional environmental changes, is generalizable, and reflects strong intelligent planning performance.

Optimal energy efficient path planning of UAV using hybrid MACO-MEA* algorithm: theoretical and experimental approach.

Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.

An intelligent optimize path planner for efficient mobile robot path planning in a complex terrain

An intelligent optimize path planner for efficient mobile robot path planning in a complex terrain

Decision-Making and Path Planning for Highway Autonomous Driving Based on Spatio-Temporal Lane-Change Gaps

Safe and efficient decision-making and path planning is a challenging problem for autonomous driving in highway because of numerous dynamic vehicles around the ego-vehicle. For ego-vehicle driving on structured roads, behavioral decisions tend to pay more attention to the relative distribution of its surrounding vehicles, while path planning based on the decision requires more detailed consideration of vehicle model and lane curvature. Therefore, these two modules are separated in our article and executed successively in different coordinate systems. Specifically, we create a decision topology for lane changing or following decision based on spatio-temporal lane-change gaps in relative moving coordinate, which consists of the node representing the gap and the edges representing the connectivity between two gaps. Combining the decision topology with the behaviors of surrounding dynamic vehicles, a local cost map is generated and the target decision gap is selected. Once the decision is done, a smooth and safe trajectory from the ego-vehicle to the target decision gap, that satisfies the constraints of acceleration and curvature, is obtained in the geodetic coordinate through polynomial trajectory generation. The real-time performance and effectiveness of this method were verified in the dynamic high-speed interaction scenarios.

A Path Planning Method for Ship Collision Avoidance Considering Spatial–Temporal Interaction Effects

Efficient and reliable path planning is crucial for smart ships when avoiding collisions with static and dynamic obstacles in complex marine environments. This research proposes a novel path planning method based on the fast marching method to specifically assist with safe navigation for autonomous ships. At the very beginning, a unified representation is specially produced to describe the path planning space based on the parametric fast marching speed function. In addition, the spatial–temporal interaction effects of dynamic obstacles are considered and integrated into the construction of planning space. Subsequently, a path optimization strategy is put forward based on the trajectory prediction of dynamic objects. Particularly, the effectiveness of the method has been validated and evaluated through a number of simulations, which proves that such a method is practical in narrow and crowded waterways.

Path planning of a 5-dof robotic arm based on BiRRT-APF algorithm considering obstacle avoidance

In robotic applications, an efficient path planning in a prescribed environment is the central issue, which is a challenging task for multiple degree-of-freedom articulated robotic arms. In this light, the path planning for robotic manipulators based on the combined artificial potential field method and bidirectional rapidly exploring random tree (BiRRT-APF) algorithm is proposed in this paper, aiming to handle the problems of highly possible randomness and low search efficiency. In order to improve the attraction function to speed up the efficiency of searching path, the characteristics of the Bi-RRT algorithm (i.e., bi-directional growth of random trees), is integrated to reduce the search time. Subsequently, a path construction strategy is proposed to remove the redundant nodes of the path, for the increased smoothness of the path and reduced memory storages of the algorithm. The effectiveness of the proposed path planning algorithm is illustrated with a 5-dof robotic arm, and is verified with MATLABTM and CoppeliaSimTM co-simulation, through a comparative study among different algorithms. The results show that the proposed BiRRT-APF algorithm can decrease the randomness of node growth and reduce the number of algorithm iterations, resulting in faster convergence of a smooth path for reduced computational burden.

Fast path planning algorithm for large-aperture aspheric optical elements based on minimum object depth and a self-optimized overlap coefficient

In the procedure of surface defects detection of large-aperture aspheric optical elements, it is necessary to scan the surface of the element to achieve full coverage inspection. Since the curvature of the aspherical element is constantly changing from the center to the edge, it is of great difficulty to carry out efficient path planning. In addition, the machine vision system is a microscopic system with limited depth of field, and the sub-aperture imaging of aspherical elements has a visual depth along the object side. When the object depth is greater than the depth of field, out-of-focus blur will generate, so the object depth needs to be as small as possible. In response to these problems, this paper proposes a fast path planning algorithm based on the minimum object depth of a sub-aperture. To ensure minimum object depth, the machine vision system collects images along the normal direction of the sub-aperture plane. To address the problem of the surface curvatures of aspheric elements being different and the overlap coefficient difficult to determine, this paper proposes an image processing based overlap coefficient self-optimization algorithm. When scanning with full coverage of elements, there is only one connected domain in the horizontal projection image of all sub-apertures. According to this premise, the overlap coefficient is optimized through an image processing method to obtain a local optimal path planning strategy. According to the obtained path planning strategy, combining the component parameters and mechanical structure, the mapping matrix of the path planning algorithm transplanted to the detection system is calculated. Through computer programming, automatic sub-aperture acquisition is realized, and the self-edited sub-aperture stitching program is applied to reconstruct the collected sub-apertures. Our algorithm can complete path planning within 5 s, and the experimental results show that the maximum stitching misalignment error of the collected sub-apertures is no more than four pixels, and the average is one pixel. The reconstruction accuracy satisfies the needs of subsequent image processing and digital quantization.

Potential Function-Based Control Using Neutral Networks in Uncertain WSNs With Mobile Collectors

In wireless sensor networks (WSNs), sink node mobility has been considered an efficient way to alleviate excessive data forwarding at nodes close to the sink node. Since the mobile sinks (or collectors) move around the network collecting data from the sensor nodes, the need for multi-hop forwarding of sensor data is reduced. In WSNs with mobile collectors, efficient path planning of mobile collectors is a major challenge where many algorithms have been proposed. However, they do not consider tracking performance degradation during flight due to environmental factors such as wind, waves, and obstacles. Tracking control algorithms have been proposed to correct the paths of mobile collectors, but existing work do not consider the possibility of collisions between multiple collectors. In this paper, we propose a distributed control approach that can improve tracking performance of mobile collectors while avoiding collisions in unknown and uncertain WSN environments. The proposed algorithm uses a potential field of attraction and repulsion to control individual mobile collector input, which enables mobile collectors to adjust their trajectories with online learning in a distributed fashion. From the theoretical analysis, the upper bound of tracking error and the conditions of the system parameters to guarantee the desired tracking error set by the operator are derived. The simulation results show that the proposed method significantly outperforms existing methods in terms of tracking error, collision rate, system stability, and convergence.

Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.

An Efficient Path Planning Strategy in Mobile Sink Wireless Sensor Networks

Wireless sensor networks (WSNs) are considered the backbone of the Internet of Things (IoT), which enables sensor nodes (SNs) to achieve applications similarly to human intelligence. However, integrating a WSN with the IoT is challenging and causes issues that require careful exploration. Prolonging the lifetime of a network through appropriately utilising energy consumption is among the essential challenges due to the limited resources of SNs. Thus, recent research has examined mobile sinks (MSs), which have been introduced to improve the overall efficiency of WSNs. MSs bear the burden of data collection instead of consuming energy at the routeing by SNs. In a network, some areas generate more data through SNs that contain frequent, urgent messages. These messages carry sensitive data that must be delivered immediately to user applications. Collecting such messages via MSs, especially on a large scale, increases delays, which are not tolerable in some real applications. This issue has not been studied much. Thus, the present study utilises the advantages of the priority parameter to concentrate on these areas and proposes a new model named ‘energy efficient path planning of MS-based area priority’ (EEPP-BAP). This method involves non-urgent and urgent messages. It is comprised of four procedures. Initially, after SNs are distributed randomly in a wide monitoring field, the monitoring field is partitioned into equal zones according to priority, either differently or equally. Next is clustering based on the cluster head (CH) selected to perform the particle swarm optimisation algorithm (PSO). Then, the MS moves first to the zones with higher priority and less distance to perform the brain storm optimisation algorithm. Finally, for urgent messages from the other zones at which the MS continues, the proposed approach establishes a routeing technique using multi-hop communication based on the MS position and using PSO. The proposed solution is aimed at delivering urgent messages to MSs free of latency and with minimal packet loss. The simulation results proved that the EEPP-BAP method can improve network performance compared with other models based on different parameters that have been used to construct the controlled movement of MSs in large-scale environments involving urgent messages. The proposed method increased the average lifetime of SNs to 206.6% on average, reduced the average end-to-end delay to 7.1%, and increased the average packet delivery ratio to 36.9%.

Efficient Reliability-Based Mission Planning of Off-Road Autonomous Ground Vehicles Using an Outcrossing Approach

Abstract Reliability-based mission planning of off-road autonomous ground vehicles (AGVs) aims to identify an optimal path under uncertain and deformable terrain environment, while satisfying specific mission mobility reliability (MMR) constraints. The repeated evaluation of MMR during path planning poses computational challenges for practical applications. This paper presents an efficient reliability-based mission planning using an outcrossing approach that has a similar computational complexity compared to deterministic mission planning. A Gaussian random field is employed to represent the spatially dependent uncertainty sources in the terrain environment. The latter are then used in conjunction with a vehicle mobility model to generate a stochastic mobility map. Based on the stochastic mobility map, outcrossing rate maps are generated using the outcrossing concept which is widely used in time-dependent reliability. Integration of the outcrossing rate map with a rapidly exploring random tree (RRT*) algorithm allows for efficient path planning of AGVs subject to MMR constraints. A reliable RRT* algorithm using the outcrossing approach (RRT*-OC) is developed to implement the proposed efficient reliability-based mission planning. Results of a case study with two scenarios verify the accuracy and efficiency of the proposed algorithm.

Visibility graph-based path-planning algorithm with quadtree representation

In recent years, unmanned surface vehicles (USVs) have been widely used in various applications. To fulfill target missions, USVs must be able to plan their path to the destination. Although many path-planning methods have been developed, most are inappropriate for application in complex marine environments. In this study, we propose a novel path-planning algorithm, which combines the merits of quadtree representation and visibility graphs, for efficient path planning of USVs in complex marine environments. Quadtree representation reduced the computation time and memory consumption. Additionally, the wind and wave data were considered to shorten the voyage time from the departure to destination points. Furthermore, the Dijkstra algorithm was applied to the quadtree to determine the suboptimal path. We then constructed a visibility graph with path waypoints using an improved line-of-sight algorithm to perform path planning on the visibility graph. The proposed algorithm not only facilitates path planning with minimum voyage time but also maintains the USV clearance distance from the coastline. To verify the performance, the proposed algorithm was compared with other methods for South Korean case studies. Overall, the proposed algorithm was able to generate a high-quality suboptimal path for the USV within a short time span.

Design and development of a glass façade cleaning robot

This paper presents a unique design concept, dynamic modeling, and control strategies for efficient coverage path planning of a glass façade cleaning robot (GFCR). The robot design has been conceptualized using mechanisms for adhesion, motion, steering, and cleaning. The dynamic model for robot vertical glass façade cleaning is derived using Lagrangian formulation. A modified particle swarm optimization (PSO) is used to autotune the proportional, integral, and derivative (PID) parameters for the trajectory tracking simulation and it is more efficient and robust compared to the standard PSO algorithm. The path planning algorithm using hybrid PID-PSO approach is also developed for energy-efficient coverage of the robot for glass façade cleaning. The coverage algorithm illustrates the energy-performance of the GFCR for different paths viz., horizontal line sweep (HLS), vertical line sweep (VLS), spiral line sweep (SLS), and special cell diffusion (SCD) motion. Simulation reveals the robot motion for HLS path is the most energy-efficient. The GFCR model with minimum energy consumption has been validated by working trials. The GFCR has potential applications for cleaning high-rise glass façade buildings and photovoltaic (PV) solar panels.

Informed Sampling Exploration Path Planner for 3D Reconstruction of Large Scenes

As vision-based navigation of small aircraft has been demonstrated to reach relative maturity, research into effective path-planning algorithms to complete the loop of autonomous navigation has been booming. Although the literature has seen some impressive works in this area, efficient path-planning that can be used in tasks such as inspection and coverage is still an open problem. In this spirit, this letter presents an online path-planning algorithm for fast exploration and 3D reconstruction of a previously unknown area of interest. Micro Aerial Vehicles (MAVs) are an ideal candidate for this task due to their maneuverability, but their limited computational power and endurance require efficient planning strategies. Popular sampling-based methods randomly sample the MAV's configuration space and evaluate viewpoints according to their expected information gain. Most often, however, valuable resources are spent on information gain calculations of unpromising viewpoints. This letter proposes a novel informed sampling approach that leverages surface frontiers to sample viewpoints only where high information gain is expected, leading to faster exploration. We study the impact of informed sampling in a wide range of photo-realistic scenes, and we show that our approach outperforms state-of-the-art exploration path planners in terms of both speed and reconstruction quality.

Online time-optimal path and trajectory planning for robotic multipoint assembly

PurposeThe purpose of this paper is to propose an efficient path and trajectory planning method to solve online robotic multipoint assembly.Design/methodology/approachA path planning algorithm called policy memorized adaptive dynamic programming (PM-ADP) combines with a trajectory planning algorithm called adaptive elite genetic algorithm (AEGA) for online time-optimal path and trajectory planning.FindingsExperimental results and comparative study show that the PM-ADP is more efficient and accurate than traditional algorithms in a smaller assembly task. Under the shortest assembly path, AEGA is used to plan the time-optimal trajectories of the robot and be more efficient than GA.Practical implicationsThe proposed method builds a new online and efficient path planning arithmetic to cope with the uncertain and dynamic nature of the multipoint assembly path in the Cartesian space. Moreover, the optimized trajectories of the joints can make the movement of the robot continuously and efficiently.Originality/valueThe proposed method is a combination of time-optimal path planning with trajectory planning. The traveling salesman problem model of assembly path is established to transfer the assembly process into a Markov decision process (MDP). A new dynamic programming (DP) algorithm, termed PM-ADP, which combines the memorized policy and adaptivity, is developed to optimize the shortest assembly path. GA is improved, termed AEGA, which is used for online time-optimal trajectory planning in joints space.

Dynamic mobile anchor path planning for underwater wireless sensor networks

In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Consequently, this leads towards the inefficient energy consumption by mobile anchors due to unnecessary transmission of beacon messages at unnecessary areas. Therefore, an efficient dynamic mobile path planning (EDMPP) algorithm to tackle the irregular deployment and non-collinear virtual beacon point placement, targeting the underwater environment settings is presented in this paper. In addition, EDMPP controls the redundant beacon message deployment and overlapping, for beacon message distribution in mobile assistant localization. The simulation results show that the performance of the EDMPP is improved by increasing the localization accuracy and decreasing the energy consumption with optimum path length.

An improved minimal path selection approach with new strategies for pavement crack segmentation

Intelligent pavement detection technology provides a new research idea for identifying pavement cracks. To solve the problem that traditional minimal cost path selection (MPS) is computationally intensive and inefficient in crack segmentation, this study proposes an improved minimal cost path algorithm (IMPS). The contribution of IMPS is reflected in its more efficient path planning than MPS and better choice of source and destination of each path by using new strategies. First, a grayscale pavement image is divided into nonoverlapping cells with a certain size. Next, the darkest points in each cell are selected as candidate points and adjacent candidate points are connected based on IMPS. Then pseudocracks are removed based on postprocessing steps before cracks are well segmented. IMPS is compared with other crack segmentation algorithms to prove its reliability. The efficiency of IMPS is increased by approximately 56% than MPS. The accuracy of IMPS is higher than that of MPS and other methods, with Mean Intersection over Union reaching 73.99%.