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Addressing the challenges of low convergence accuracy and stagnation at local optima in the application of the golden jackal optimizer (GJO) to mobile robot path planning, this paper proposes a hybrid strategy-based golden jackal optimizer (HGJO) algorithm. The improved algorithm employs a pre-decreasing slow nonlinear energy decay strategy to balance the global and local search capabilities. The roulette wheel selection algorithm and Lévy flight strategy are introduced into the position update of the GJO algorithm, so the proposed algorithm avoids stagnation at the local optimum. The HGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions and the CEC2021 benchmark function. It is also applied to ablation experiments for mobile robot path planning. The experimental results show that the HGJO algorithm improves the average path length in path planning by 0.21%, 82.4%, and 7.9% over the original algorithm in three different environments under 30 independent experiments. • A new energy decreasing method is proposed. • A roulette wheel selection strategy is introduced. • A new hybrid position updating strategy is proposed. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) is a prominent algorithm that offers great exploration capabilities among various meta-heuristic algorithms. However, its monotonous and one-dimensional search strategy limits its searching performance in the solving process. Thus, to address this issue, a Q-learning based multi-strategy integrated ABC algorithm (QMABC) is proposed. In the QMABC, multiple search strategies are proposed to utilize different individual experiences and search approaches for solution updates. Then, Q-learning is employed for strategy selection. In comparison to previous studies, this paper introduces more effective state and action configurations within the framework of Q-learning. To evaluate the performance of the QMABC, CEC 2017 benchmark functions are adopted to compare it to different meta-heuristic algorithms including ABC based and non-ABC based algorithms. Moreover, applications in path planning are implemented to further verify the effectiveness of the QMABC. Overall, it should be highlighted that the proposed QMABC demonstrates superiority in both numerical and practical experiments. • A Q-learning based multi-strategy integrated ABC algorithm is proposed. • Multiple strategies are introduced to the onlooker bee phase. • Q-learning is utilized to adaptively determine the most suitable strategy. • Novel state and action settings are designed in Q-learning framework. • Proposed algorithm shows superiority in numerical and practical experiments. [ABSTRACT FROM AUTHOR]

Path planning is an important and classical problem in theoretical research and practical applications. In the complex and hierarchical space scenarios, path planning faces more difficulties and challenges due to the structural particularity. Considering the directivity of paths in hierarchical spaces, it is more important to guarantee the fluency and efficiency of the paths in hierarchical spaces. In this paper, we introduce a path network planning problem from multi-source to multi-destination in hierarchical spaces, namely Balanced-Flow Path Network Planning (BF-PNP) problem, and prove its NP-completeness. To balance the flow rate among the layers in the space, we propose a batch scheduling algorithm with the objective of minimizing the scheduling time consumption. To evaluate the performance on efficiency and time complexity, we perform a series of simulations and the results indicate the advantages of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star (RRT*) to address the issues of slow convergence, high path cost, and how to plan suitable paths in the presence of congested regions in the environment. First, the CCPF-RRT* algorithm adds the artificial potential field to RRT*, which can drastically cut down on the number of iterations and speed up convergence. Second, a movement cost function that takes congestion intensity and path length into account when evaluating a connection between two path nodes is created. This function is then used to direct the growth of new nodes and the updating of parent nodes so that the algorithm takes congestion intensity and path length into account when generating paths. Third, in order to reduce the path cost, the algorithm establishes parent nodes for random nodes by combining the benefits of the F-RRT* algorithm. The movement cost function is added to the process of establishing parent nodes to optimize the path cost in order to prevent the creation of parent nodes that force the path to traverse through crowded areas. In experimental simulation results, the algorithm outperforms RRT*, Q-RRT*, PQ-RRT*, and F-RRT* not only in terms of initial solution and quick convergence speed, but is also able to design an ideal path with the lowest movement cost in a crowded environment using the movement cost function. [ABSTRACT FROM AUTHOR]

Mobile robot path planning refers to the design of the safely collision-free path with shortest distance and least time-consuming from the starting point to the end point by a mobile robot autonomously. In this paper, a systematic review of mobile robot path planning techniques is presented. Firstly, path planning is classified into global path planning and local path planning according to the mastery of environmental information. In the global path planning, environment modeling methods and path evaluation method are introduced. The methods of environment modeling include grid method, topology method, geometric feature method and mixed representation method. In the local path planning, we introduce the sensors commonly used in the detection environment, including laser radar and visual sensor. Next, according to the characteristics of algorithms, mobile robot path planning algorithms are divided into three categories: classical algorithms, bionic algorithms and artificial intelligence algorithms. Among the classical algorithms, we introduce the cell decomposition method, sampling based method, graph search algorithm, artificial potential field method and dynamic window method. In the algorithm based on bionics, we introduce genetic algorithm, ant colony algorithm, gray wolf algorithm, etc. in detail. In artificial intelligence algorithm, we introduce neural network algorithm and fuzzy logic. Finally, we compare the key technologies of mobile robot path planning in the form of graphs and charts based on the classification statistics of the collected literature to provide references for future research. [ABSTRACT FROM AUTHOR]

Automatic path planning is an essential aspect of unmanned aerial vehicle (UAV) autonomy. This paper presents a three dimensional path planning algorithm based on adaptive sensitivity decision operator combined with particle swarm optimization (PSO) technique. In the proposed method, an adaptive sensitivity decision area is constructed to overcome the defects of local optimal and slow convergence. By using this specified area, the potential particle locations with high probabilities are determined and other candidates are deleted to improve computational capacity. Then the searching space of particles is constrained in a limited boundary to avoid premature state. In addition, the searching accuracy is enhanced by the relative particle directivity from current location. The objective function is redesigned by taking into account the distance to destination and UAV self-constraints. To evaluate the path length, the paired-sample T-Test is performed and the straight line rate ( SLR ) index is introduced. In the two scenarios applied in this paper, our proposed method is 35.4 % , 21.6 % and 49.5 % better compared with other three tested optimization algorithms in the path cost on average. Correspondingly it is 9.6 % , 12.8 % , and 25.3 % better in SLR , which is capable of generating higher quality paths efficiently for UAVs. [ABSTRACT FROM AUTHOR]

This paper addresses problems of large planning time and cost uncertainty for informative path planning of a mobile sensor where the location of sensor deployment is different of that of an operational area. The first problem is that the cost has no term dependent on sensor state before arriving at the operational area and it causes large planning time. The information of the state of interest dissipates over time during the planning time and it degrades performance of sensing operation. The other problem is that the cost is dependent on the parameters to be estimated. To assess the cost, the target state in the future should be predicted by integrating the system model based on noisy initial estimate. The limitation of the informative path planning has a greater impact on performance in this specific problem. A strategy to cope with these problems is to devise a real-time path planning algorithm by using online optimization. The proposed algorithm is divided into two phases; determining the path to the boundary of the operational area and guiding the sensor by an informative potential field in the area. Detailed analysis on performance of the proposed algorithm compared to an optimal solution by nonlinear programming is given. The simulation results have demonstrated that the proposed algorithm can cope with performance degradation observed in the optimal solution. [ABSTRACT FROM AUTHOR]

Natural or man-made disasters can cause different kinds of moving obstacles (e.g., fires, plumes, floods), which make some parts of the road network temporarily unavailable. After such incidents occur, responders have to go to different destinations to perform their tasks in the environment affected by the disaster. Therefore they need a path planner that is capable of dealing with such moving obstacles, as well as generating and coordinating their routes quickly and efficiently. In this paper, we present a novel approach for using a multi-agent system for navigating one or multiple responders to one or multiple destinations in the presence of moving obstacles. Our navigation system supports information collection from hazard simulations, spatio-temporal data processing and analysis, connection with a geo-database, and route generation in dynamic environments affected by disasters. We design and develop a set of software geospatial agents that assist emergency actors in dealing with the spatio-temporal data required for emergency navigation, based on their roles in the disaster response. One of the key components of the system is the path planning module, which combines the modified A* algorithm, insertion heuristics, and auction algorithm to calculate obstacle-avoiding routes for multiple responders with multiple destinations. A spatial data model is designed to support the storage of information about the tasks and routes produced during the disaster response. Our system has been validated using four navigation cases. Some preliminary results are presented in this paper and show the potential of the system for solving more navigation cases. [ABSTRACT FROM AUTHOR]

In this paper, the navigation system of the autonomous vehicle prototype Verdino is introduced. Two navigation levels are considered. In the first level, a trajectory is generated from the current position toward a goal that considers two different approaches. In the first, the minimum cost path is obtained using a classical approach (used for regular navigation). The second approach is a little more complex, relying on a set of precomputed primitives representing the motion model of the vehicle, which are used as part of an ARA⁎ algorithm in order to find the best trajectory. This trajectory consists of both forward and backward motion segments for complex maneuvers. In the second level, a local planner is in charge of computing the commands sent to the vehicle in order to follow the trajectory. A set of tentative local trajectories is computed in the Frenét space and scored using several factors, described in this paper. Some results for the two navigation levels are shown at the end of this document. For the global planner, several examples of the maneuvers obtained are shown and certain related factors are quantified and compared. As for the local planner, a study on the influence of the defined weights on the vehicle׳s final behavior is presented. Also, from these tests several configurations have been chosen and ranked according to two different proposed behaviors. The navigation system shown has been tested both in simulated and in real conditions, and the attached video shows the vehicle׳s real-world performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic-Window Approach) to study the obstacle avoidance problem of robots in complex continuous state space. First, the obstacle avoidance problem is converted into an optimal learning incentive problem, and the self-learning of the obstacle avoidance policy is realized based on DDPG; second, the DWA obstacle avoidance trajectory evaluation function is optimized using the DDPG reward incentive mechanism, and the Experience Replay mechanism; finally, the algorithm model is simulated. The experiments show that the model can significantly circumvent the deficiency of the DWA algorithm in limiting to the optimal local solution in a complex environment and solve the action output problem in the continuous velocity and turning angle value interval of the robot; through the trial and error interaction with the environment and the trajectory evaluation incentive feedback, the obstacle avoidance passing ability of the robot in a complex environment is improved. [Display omitted] • We propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic Window Approach), named the DDPG-DWA. • The DDPG-DWA algorithm can solve the problem of action output in the continuous velocity and turning angle value range of the robot. • The DDPG-DWA algorithm can avoid the problem of DWA algorithm easily being limited to local obstacle avoidance optimal solutions in complex environments. • The model is suitable for path obstacle avoidance of intelligent agents in complex, narrow and irregular environments. [ABSTRACT FROM AUTHOR]

Urban air mobility (UAM) has attracted the attention of aircraft manufacturers, air navigation service providers and governments in recent years. Preventing the conflict among urban aircraft is crucial to UAM traffic safety, which is a key in enabling large scale UAM operation. Pre-flight conflict-free path planning can provide a strategic layer in the maintenance of safety performance, thus becomes an important element in UAM. This paper aims at tackling conflict-free path planning problem for UAM operation with a consideration of four-dimensional airspace management. In the paper, we first introduce AirMatrix, previously developed by the team, and extend it as a four-dimensional airspace management concept. On the basis of AirMatrix, we formulate the shortest flight time path planning problem considering resolution of conflicts with both static and dynamic obstacles. A Conflict-Free A* (CFA*) algorithm is developed for planning four-dimensional paths based on first-come-first-served scheme. The algorithm contains a novel design of heuristic function as well as a conflict detection and resolution strategy. Numerical simulation was carried out using the building information in a typical urban area in Singapore. The results show that the algorithm can generate paths resolving a significant number of potential conflicts in airspace utilization, with acceptable computational time and flight delay. The findings of this study will provide references for stakeholders to support the development of UAM. [ABSTRACT FROM AUTHOR]

Integrating multiple features for autonomous flight of aircraft requires extensive research over wide categories, high-level of know-how, and experiences. To contribute to these issues, this paper treats the realizable integration techniques of the path planning, trajectory generation, and trajectory-tracking control for the mission autonomy of the aircraft. A collision-free path over the three-dimensional digital terrain is efficiently generated using the P-RRT* algorithm and optimized using the line-of-sight path optimizer proposed in this paper. After additional waypoints are inserted into the planned straight-line path segments, a flyable trajectory for the position and heading angle can be built using the spline trajectory generator with reference to the prescribed velocity distribution. Finally, the incremental backstepping-control design has been adopted to provide accurate trajectory-tracking control. The paper addresses in detail the underlying rationales of the present selection of each technique from the realizability point of view. The integrated designs are validated through simulation studies for a series of autonomous mission scenarios of the rotorcraft. The results show that the P-RRT* algorithm combined with the line-of-sight path optimizer can provide a fast and efficient solution for path planning. Also, the extremely accurate trajectory-tracking performance has been achieved by applying the incremental backstepping control to the flyable trajectory obtained using the spline trajectory generator. [ABSTRACT FROM AUTHOR]

Unmanned combat aerial vehicle (UCAV) technology has to address many challenges in complex battlefield environments. To produce a safe and low-energy flying path, a UCAV requires many points to build a path that avoids threats, which increases the problem dimension, consumes more computational resources and makes the results unstable. To address this issue, this paper employs a new model known as double-layer coding (DLC) for path planning with unevenly distributed points to decrease the number of superfluous points on the path. Meanwhile, the RPSO algorithm, which introduces a novel strategy of rotating particles in high-dimensional space to search for targets, is proposed as an enhanced particle swarm optimization (PSO) algorithm. The proposed method effectively improves PSO ′ s exploration capacity. Furthermore, RPSO is employed to implement the double-layer coding model for path planning (DLCRPSO). The experimental results show that the proposed DLCRPSO method for path planning always produces feasible flight paths in complex environments. [Display omitted] • A double-layer coding model for path planning is established. • An improved PSO with a novel exploration strategy is proposed. • The superiority of the double-layer coding model used improved PSO is verified. [ABSTRACT FROM AUTHOR]

• The MDA + RRT algorithms guarantee that planned path satisfies the constraint of angle. • Compared with other algorithms, this algorithm does not increase the computation amount. • The feasibility and optimization of MDA + RRT are preferable to the corresponding RRT. • This method is a universal method that can be associated with any kind of RRT algorithm. In this paper, a cable-driven hyper-redundant manipulator with 17 degrees of freedom is presented for narrow and complex environments firstly. Based on the mechanical structure, the MDA + RRT algorithms is proposed for path planning considering the maximum deflection angle of joint. First, the transformation relationship between the path parameters and manipulator parameters is obtained through the theorem of sine of triangles and two extreme points, which can convert limitation of the deflection angle of the joint to the limitation of the deflection angle of the path. Then, three corresponding improved algorithms are proposed: MDA-RRT, MDA-RRT*, MDA-QRRT* on the basis of the traditional RRT, RRT* and Q-RRT* algorithms. Finally, six algorithms are applied to plan the path of avoiding three different obstacles respectively by simulation. The results demonstrate that, three improved algorithms can guarantee that the planned path satisfies the constraint of deflection angle of joint without increasing the computational amount compared with the three traditional algorithms, only by limiting the selection range of the next random vertex. Among the six algorithms, the feasibility and optimization of the MDA-QRRT* algorithm are the best and the feasibility and optimization of the three improved algorithms are also better than the corresponding traditional algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the unmanned underwater vehicle two-dimensional autonomous path planning problem in the environment affected by ocean current and obstacles, this paper applies the improved Fireworks-Ant Colony Hybrid Algorithm to solve it. Firstly, a two-dimensional Lamb vortex ocean current environment model with randomly distributed obstacles is established, and the circular obstacle is equivalent to a square grid. Then, the mathematical model of path planning is established considering the energy consumption cost, navigation time cost and navigation distance cost. Finally, the improved fireworks-ant colony hybrid algorithm is applied to solve the nonlinear optimization problem, and this algorithm is compared with the basic ant colony algorithm for simulation experiments in the four different marine environments. The experimental results show that this algorithm can quickly find the global optimal solution, and the more complex the environment, the more obvious its advantages. The algorithm proposed in this paper provides a new way for autonomous path planning of underwater vehicles. [ABSTRACT FROM AUTHOR]

• Propose a sampling-based asymptotically optimal path planning algorithm. • The proposed algorithm guarantees a fast convergence rate. • Theoretical proof of asymptotic optimality and fast convergence rate is given. During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the unmanned aerial vehicle (UAV) global path planning as an optimization problem with multiple constraints and proposes an improved fireworks algorithm (FWA) and particle swarm optimization (PSO) cooperation algorithm to generate an optimal path. The objective function of the UAV flight path is modeled to have the shortest length satisfying strict multiple threat area constraints. The α constrained method using the level comparison strategy is integrated into both FWA and PSO to enhance their superior constraint-handling ability. To increase the population diversity, the whole population is divided to fireworks and particles, which perform search operation in parallel. A new mutation strategy in the fireworks is adopted to avoid falling into the local optimum. Information sharing mechanism between fireworks and particles is established to make the population achieve the excellent global optimization performance. Several numerical simulations are carried out and the results show that our proposed algorithm performs well in obtaining high quality solutions and handling constraints. [ABSTRACT FROM AUTHOR]

• Combines exploration, mapping and coverage. • Best utilization of onboard sensors such as long range distance sensors. • Combines the advantages of offline and online coverage path planning. In this paper, we present a novel problem of simultaneous exploration and area coverage by multiple cooperating mobile robots. As the robots cover an initially unknown region, they perform intermittent exploration of the region and build a map, which in turn is used to plan the coverage path. We use a Voronoi partition based multi-robot coverage strategy using the Manhattan distance metric to solve the coverage problem and a frontier based exploration strategy for exploration mapping. We provide results of simulation using Matlab/V-rep environments to demonstrate the proposed multi-robot simultaneous exploration and coverage (MR-SimExCoverage) problem using the spanning tree based coverage (STC) algorithm. [ABSTRACT FROM AUTHOR]