Jump Point Search Research Articles

Hybrid Mobile Robot Path Planning Using Safe JBS-A*B Algorithm and Improved DWA Based on Monocular Camera

This paper addresses the formidable challenge of enabling autonomous navigation in Mobile Robots (MRs), focusing on the development of advanced path planning strategies. Despite their pivotal role in diverse applications, they face challenges in dynamic settings due to limitation in existing Global Path Planning (GPP) and Local Path Planning (LPP) techniques. In response to this, we propose an innovative hybrid path planning approach that enhances the A* algorithm with a risk-aware heuristic function and integrates the Jump Point Search (JPS) technique for route optimization. Additionally, B-spline smoothing is employed for perceptually global trajectory refinement. Our approach also includes an innovative improvement to the Dynamic Window Approach (DWA) to align with the proposed enhanced A* algorithm for effective local navigation. Acknowledging the importance of high-quality input in path planning, we present substantial improvements to the IRDC-Net, a monocular-image semantic-segmentation model that we studied previously. Novel improvements include the integration of quantization and the Adam optimizer, along with the implementation of the Balanced Cross-Entropy loss function. These enhancements not only elevate the output quality of IRDC-Net but also reduce the model’s training parameters. The experimental results demonstrate the performance and viability of the proposed algorithm. Ultimately, the hybrid MR’s path planning algorithm exhibits proficiency across various tasks, particularly in addressing the challenge of evading moving obstacles to ensure the robot’s safety while adhering to the global path.

Automatic cable routing based on improved pathfinding algorithm and B-spline optimization for collision avoidance

Abstract With the recent growth of electrical and electronic systems such as electric vehicles, the demand for automatic cable routing for electrical wiring design is increasing. However, real industry use cases of automatic cable routing are still rare especially in three-dimensional design. In this study, we propose a new pathfinding algorithm, JPS–Theta*, which combines the existing pathfinding algorithms, Jump Point Search and Theta*, that is better suited for cable routing. In addition, we propose a B-spline optimization algorithm to create natural cable shapes while avoiding collisions. In the experiments, it was found that the proposed pathfinding algorithm complements the existing algorithms and is thought to be more suitable for the purpose of automatic cable routing. Additionally, ant colony optimization for continuous domains, a meta-heuristic algorithm, was successfully used for optimizing the B-spline to obtain cable shapes without collision. Lastly, as a case study, the proposed method was directly applied to the electrical panel design to show its effectiveness. We expect that the proposed method will be able to improve the efficiency and quality of electrical wiring design.

Research on Path Planning for Robots with Improved A* Algorithm under Bidirectional JPS Strategy

Aiming to address the A* algorithm’s issues of traversing a large number of nodes, long search times, and large turning angles in path planning, a strategy for multiple improvements to the A* algorithm is proposed. Firstly, the calculation of the heuristic function is refined by utilizing the Octile distance instead of traditional distance, which more accurately predicts the optimal path length. Additionally, environmental constraints are introduced to adaptively adjust the weight of the heuristic function, balancing the trade-off between search speed and path length. Secondly, the bidirectional jump point search method is integrated, allowing simultaneous path searches from both directions. This significantly reduces path search times and the number of nodes traversed. Finally, the path undergoes two rounds of smoothing using a path smoothing strategy until the final path is generated. To validate the effectiveness of the improved A* algorithm, simulations are conducted on ten types of grid maps. Results demonstrate that the improved A* algorithm markedly decreases path search times while maintaining path length, with greater speed improvements observed as the map size increases. Furthermore, the improved algorithm is applied in experiments with mobile robots, achieving significant reductions in average path search times of 79.04% and 37.41% compared to the traditional A* algorithm and the JPS algorithm, respectively. This enhancement effectively meets the requirements for rapid path planning in mobile robotics applications.

A fuel-efficient pathfinding algorithm for next-generation WIG crafts

ABSTRACT Wing-In-Ground (WIG) crafts have the advantage of being fuel-efficient by utilising the ground effect. Next-generation WIG crafts can choose flight paths that navigate over obstacles while flying at specific altitudes. However, current route computation algorithms for ships fail to account for paths that allow ships to navigate over obstacles. Leveraging fundamental path-finding approaches such as A* and Jump Point Search (JPS), our algorithms are designed to forecast routes that significantly improve operational efficiency. Validation was performed using real topographic data from Indonesia, focusing on fuel consumption metrics to evaluate the algorithms’ effectiveness. The results evaluate the superiority of our methods in optimising route selection for WIG crafts, marking a advancement in autonomous marine navigation technology.

A fast planning approach for 3D short trajectory with a parallel framework

For real applications of unmanned aerial vehicles, the capability of navigating with full autonomy in unknown environments is a crucial requirement. However, planning a shorter path with less computing time is contradictory. To address this problem, we present a framework with the map planner and point cloud planner running in parallel in this paper. The map planner determines the initial path using the improved jump point search method on the 2D map, and then it tries to optimize the path by considering a possible shorter 3D path. The point cloud planner is executed at a high frequency to generate the motion primitives. It makes the drone follow the solved path and avoid the suddenly appearing obstacles nearby. Thus, vehicles can achieve a short trajectory while reacting quickly to the intruding obstacles.We demonstrate fully autonomous quadrotor flight tests in unknown and complex environments with static and dynamic obstacles to validate the proposed method. In simulation and hardware experiments, the proposed framework shows satisfactorily comprehensive performance.

Bidirectional Jump Point Search Path-Planning Algorithm Based on Electricity-Guided Navigation Behavior of Electric Eels and Map Preprocessing.

The electric eel has an organ made up of hundreds of electrocytes, which is called the electric organ. This organ is used to sense and detect weak electric field signals. By sensing electric field signals, the electric eel can identify changes in their surroundings, detect potential prey or other electric eels, and use it for navigation and orientation. Path-finding algorithms are currently facing optimality challenges such as the shortest path, shortest time, and minimum memory overhead. In order to improve the search performance of a traditional A* algorithm, this paper proposes a bidirectional jump point search algorithm (BJPS+) based on the electricity-guided navigation behavior of electric eels and map preprocessing. Firstly, a heuristic strategy based on the electrically induced navigation behavior of electric eels is proposed to speed up the node search. Secondly, an improved jump point search strategy is proposed to reduce the complexity of jump point screening. Then, a new map preprocessing strategy is proposed to construct the relationship between map nodes. Finally, path planning is performed based on the processed map information. In addition, a rewiring strategy is proposed to reduce the number of path inflection points and path length. The simulation results show that the proposed BJPS+ algorithm can generate optimal paths quickly and with less search time when the map is known.

Reducing Redundant Work in Jump Point Search

JPS (Jump Point Search) is a state-of-the-art optimal algorithm for online grid-based pathfinding. Widely used in games and other navigation scenarios, JPS nevertheless can exhibit pathological behaviours which are not well studied: (i) it may repeatedly scan the same area of the map to find successors; (ii) it may generate and expand suboptimal search nodes. In this work, we examine the source of these pathological behaviours, show how they can occur in practice, and propose a purely online approach, called Constrained JPS (CJPS), to tackle them efficiently. Experimental results show that CJPS has low overheads and is often faster than JPS in dynamically changing grid environments: by up to 7x in large game maps and up to 14x in pathological scenarios.

Pipe-Routing and Pathfinding in 3D (Student Abstract)

Pipe-routing in 3D is a common problem in the design of industrial plant layouts. Here, we aim to minimise the structural cost of the plant (which can have multi-billion dollar budgets), while maintaining safety and engineering constraints. We tackle this problem by developing efficient methods for optimal 3D search. We contribute an adaption of Jump Point Search, a well-known symmetry-breaking technique for 2D grids, into 3D: in contrast to related work, our algorithm preserves path feasibility. In combination with a novel method for limiting over-scanning, we report search time speedups of up to an order of magnitude on benchmarks in the literature. We further develop three new and varied voxel benchmark data sets sourced from 3D applications in the literature in order to provide better opportunities for differentiating competing techniques. Towards pipe-routing, this work also identifies several remaining issues for translating the size of industrial domains and their associated constraints into 3D search.

Optimal Pathfinding on Weighted Grid Maps

In many computer games up to hundreds of agents navigate in real-time across a dynamically changing weighted grid map. Pathfinding in these situations is challenging because the grids are large, traversal costs are not uniform, and because each shortest path has many symmetric permutations, all of which must be considered by an optimal online search. In this work we introduce Weighted Jump Point Search (JPSW), a new type of pathfinding algorithm which breaks weighted grid symmetries by introducing a tiebreaking policy that allows us to apply effective pruning rules in symmetric regions. We show that these pruning rules preserve at least one optimal path to every grid cell and that their application can yield large performance improvements for optimal pathfinding. We give a complete theoretical description of the new algorithm, including pseudo-code. We also conduct a wide-ranging experimental evaluation, including data from real games. Results indicate JPSW is up to orders of magnitude faster than the nearest baseline, online search using A*.

Implementasi Algoritma Jump Point Search Pada Game Prince Of Walangsungsang

The development of the gaming industry has provided an opportunity to combine entertainment with education. In this context, this study aims to implement the Jump Point Search (JPS) algorithm in the game "Prince of Walangsungsang" to provide a more interesting playing experience and introduce players to the history of Prince Walangsungsang journey. This study focuses on the action role-playing game (RPG) genre that combines game elements with historical stories. In making games, the development method used is the Game Development Life Cycle (GDLC) to ensure structured and efficient development steps. The JPS algorithm is implemented as an artificial intelligence (AI) agent on non-player characters (NPC) in games. By implementing the JPS algorithm, in-game NPCs can optimize their movements in a complex game environment, speed up finding the shortest path, and avoid unnecessary routes, as well as avoid dynamic obstacles. Games "Prince of Walangsungsang" provide a more realistic and challenging gaming experience for players, as well as introduce historical aspects through interactions with NPCs. The results of the study show that the implementation of the JPS algorithm in the game "Prince of Walangsungsang" improves the efficiency and intelligence of NPCs, so that players can experience a more dynamic and interesting playing experience. In addition, game-based introduction to the history of Prince Walangsungsang also gives players the opportunity to learn about and appreciate the cultural heritage associated with this historical figure.

Coverage path planning of unmanned surface vehicle based on improved biological inspired neural network

In order to meet the requirements of coverage path planning task for unmanned surface vehicle, a coverage apth planning algorithm of unmanned surface vehicle based on improved biological inspired neural network is proposed. The template model method and jump point search algorithm are introduced on the basis of biological inspired neural network to solve the problem that the original algorithm cannot completely cover and lock when adjacent to obstacles. To meet the task requirements and enrich the functionality of the algorithm, the island type obstacle template is introduced to make the algorithm give priority to island coverage detection. The problem of obstacle disappearance is solved by enhancing the ability of algorithm to cover specific area first. In the simulation, six marine maps with different complexity are established to verify the effectiveness of the path planning algorithm. Compared with the other coverage path planning algorithms, the simulation experiment proves that the proposed improved biological inspired neural network path planning algorithm improves the efficiency of coverage path planning, shortens the path length and reduces the path repetition rate on the premise of ensuring 100% coverage. Furthermore, the proposed improved biological inspired neural network algorithm achieves the shortest path planning time.

Path planning of indoor mobile robot based on improved A* algorithm incorporating RRT and JPS

Indoor mobile robots are widely used in modern industry. Traditional motion control methods for robots suffer from discontinuous path curvature, low planning efficiency, and insufficient verification of theoretical algorithms. Therefore, a motion control system for an intelligent indoor robot was designed. By optimizing the radar map detecting and positioning, path planning, and chassis motion control, the performance of the system has been improved. First, a map of the warehouse environment is established, and the number of resampling particles interval is set for the Gmapping building process to improve the efficiency of map construction. Second, an improved A* algorithm is proposed, which converts the path solution with obstacles between two points into the path solution without obstacles between multiple points based on the Rapidly expanding Random Trees and Jump Point Search algorithms and further improves the pathfinding speed and efficiency of the A* algorithm by screening the necessary expansion nodes. The Dynamic Window Approach (DWA) algorithm based on the dynamic window is used to smooth the path, and the target velocity is reasonably assigned according to the kinematic model of the robot to ensure the smooth motion of the chassis. By establishing raster map models of different sizes, the traditional and improved A* pathfinding algorithms are compared and validated. The results illustrate that the improved pathfinding algorithm reduces the computing time by 67% and increases the pathfinding speed by 47% compared with the A* algorithm. Compared with the traditional method, the speed and effect are greatly improved, and the motion control system can meet the requirements of autonomous operation of mobile robots in indoor storage.

RJA-Star Algorithm for UAV Path Planning Based on Improved R5DOS Model

To improve the obstacle avoidance ability of agricultural unmanned aerial vehicles (UAV) in farmland settings, a three-dimensional space path planning model based on the R5DOS model is proposed in this paper. The direction layer of the R5DOS intersection model is improved, and the RJA-star algorithm is constructed with the improved jump point search A-star algorithm in our paper. The R5DOS model is simulated in MATLAB. The simulation results show that this model can reduce the computational complexity, computation time, the number of corners and the maximum angles of the A-star algorithm. Compared with the traditional algorithm, the model can avoid obstacles effectively and reduce the reaction times of the UAV. The final fitting results show that compared with A-star algorithm, the RJA-star algorithm reduced the total distance by 2.53%, the computation time by 97.65%, the number of nodes by 99.96% and the number of corners by 96.08% with the maximum corners reduced by approximately 63.30%. Compared with the geometric A-star algorithm, the running time of the RJA-star algorithm is reduced by 95.84%, the number of nodes is reduced by 99.95%, and the number of turns is reduced by 67.28%. In general, the experimental results confirm the effectiveness and feasibility of RJA star algorithm in three-dimensional space obstacle avoidance.

Trajectory Planning of USV: On-Line Computation of the Double S Trajectory Based on Multi-Scale A* Algorithm with Reeds–Shepp Curves

Trajectory planning aims to provide a time-related control target that contains the concerned states. For an underactuated surface vehicle (USV), planning challenges include limitations on curvature, speed, acceleration, and jerk. These challenges are relevant for the precise control of USVs. To solve these problems, an on-line double S method and multi-scale A* trajectory planning algorithm is proposed by integrating Reeds–Shepp curves (RSC), where a quad-tree-based graph is used for path planning and collision detection. Simulations illustrate that the proposed method has a better performance than the smooth rapid-exploration random tree (smooth-RRT) method and jump point search (JPS) method, and that path length and the state limitations are satisfied.

Application of reinforcement learning based on curriculum learning for the pipe auto-routing of ships

Abstract The pipe routing of ships has been manually performed by experts, and the design quality depends on the competence of the experts. Therefore, studies on pipe-routing automation and optimization are required. In addition, the pipe-routing task in a ship that requires frequent pipe-routing modifications requires a long time to be optimized. In this study, we developed a methodology that enables a rapid response in situations where frequent pipe-routing modifications are required by applying curriculum learning that can be stably learned by gradually solving easy-to-complex problems. In addition, this study aimed to minimize the length of the pipe and number of bends as an objective function. Finally, the proposed methodology was verified by comparing it with existing studies that used the A*, jump point search, and reinforcement-learning algorithms to determine the search speed, number of bends, and length of the path.

A new multi-objective point path planning algorithm for mobile robots

Aiming at the path planning problem of mobile robot passing through multiple target points with the shortest path under two-dimensional raster map, this paper proposes a method combining an improved A* algorithm with an annealing genetic algorithm. For the problem that A* algorithm will search adjacent nodes in turn, Jump Point Search (JPS) algorithm is introduced to avoid the search of surrounding nodes by searching the jump point of the current node and reduce the calculation time. In view of the prematurity problem of genetic algorithm, the simulated annealing algorithm is introduced to make the simulated annealing algorithm and genetic algorithm complement each other and overcome the prematurity problem of genetic algorithm effectively. By using TSPLIB dataset, the path planning of the improved genetic algorithm is obviously better than that of the original genetic algorithm. Finally, the feasibility and effectiveness of the method are verified by simulation, and the shortest safe path can be found quickly in the process of optimization.

Explanation and Speedup Comparison of Advanced Path-planning Algorithms Presented on Two-dimensional Grid

Path planning or network route planning problems are an important issue in AI, robotics, or computer games. Appropriate implementation and knowledge of advanced and classical path-planning algorithms can be important for both autonomous navigation systems and computer games. In this paper, we compare advanced path planning algorithms implemented on a two-dimensional grid. Advanced path planning algorithms, including pseudocode, are introduced. The experiments were performed in the Python environment, thus with a significant performance margin over C++ or Rust implementations. The main focus is on the speedup of the algorithms compared to a baseline method, which was chosen to be the well-known Dijkstra's algorithm. All experiments correspond to trajectories on a two-dimensional grid, with variously defined constraints. The motion from each node corresponds to a Moore neighborhood, i.e., it is possible in eight directions. In this paper, three well-known path planning algorithms are described and compared: the Dijkstra, A* and A* /w Bounding Box. And two advanced methods are included, namely Jump Point Search (JPS), incorporated with the Bounding Box variant (JPS+BB), and Simple Subgoal (SS). These advanced methods clearly show their advantage in the context of the speed up of solution time.

A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

To improve the finding path accuracy of the ant colony algorithm and reduce the number of turns, a jump point search improved ant colony optimization hybrid algorithm has been proposed in this article. Firstly, the initial pheromone concentration distribution gets from the jump points has been introduced to guide the algorithm in finding the way, thus accelerating the early iteration speed. The turning cost factor in the heuristic function has been designed to improve the smoothness of the path. Finally, the adaptive reward and punishment factor, and the Max–Min ant system have been introduced to improve the iterative speed and global search ability of the algorithm. Simulation through three maps of different scales is carried out. Furthermore, the results prove that the jump point search improved ant colony optimization hybrid algorithm performs effectively in finding path accuracy and reducing the number of turns.

A Smooth Jump Point Search Algorithm for Mobile Robots Path Planning Based on a Two-Dimensional Grid Model

To address the problems of the traditional A ∗ algorithm in solving paths with many expansion nodes, high memory overhead, low operation efficiency, and many path corners, this paper improved the traditional A ∗ algorithm by combining jump point search strategy and adaptive arc optimization strategy. Firstly, to improve the safety of our paths, the risk area of the obstacles was expanded. Then, the A ∗ algorithm was combined with the jump point search strategy to achieve the subnode jump search, reducing the calculation scale and memory overhead, and improving search efficiency. Considering the influence of the density of obstacles on search efficiency, the heuristic function was enhanced according to the special effects of the density of obstacles. Finally, the redundant jump point and adaptive arc optimization strategies were used to shorten the path length further and enhance the initial path’s smoothness. Simulation results showed that our algorithm outperforms traditional A ∗ and literature algorithms in path length, security, and smoothness, and then was further validated and applied in large-scale marine environments and realistic settings.

3D JPS Path Optimization Algorithm and Dynamic-Obstacle Avoidance Design Based on Near-Ground Search Drone

As various fields and industries have progressed, the use of drones has grown tremendously. The problem of path planning for drones flying at low altitude in urban as well as mountainous areas will be crucial for drones performing search-and-rescue missions. In this paper, we propose a convergent approach to ensure autonomous collision-free path planning for drones in the presence of both static obstacles and dynamic threats. Firstly, this paper extends the jump point search algorithm (JPS) in three dimensions for the drone to generate collision-free paths based on static environments. Next, a parent node transfer law is proposed and used to implement the JPS algorithm for any-angle path planning, which further shortens the planning path of the drones. Furthermore, the optimized paths are smoothed by seventh-order polynomial interpolation based on minimum snap to ensure the continuity at the path nodes. Finally, this paper improves the artificial potential field (APF) method by a virtual gravitational field and 3D Bresenham’s line algorithm to achieve the autonomous obstacle avoidance of drones in a dynamic-threat conflict environment. In this paper, the performance of this convergent approach is verified by simulation experiments. The simulation results show that the proposed approach can effectively solve the path planning and autonomous-obstacle-avoidance problems of drones in low-altitude flight missions.