An A*-MPPO-DWA path planning algorithm for autonomous underwater vehicle

In the context of coal mine operations, the assurance of work safety relies heavily on efficient autonomous navigation for rescue robots, yet traditional path planning algorithms such as A and RRT exhibit significant deficiencies in a coal mine environment. Traditional path planning algorithms (such as Dijkstra and PRM) have certain deficiencies in dynamic Spaces and narrow environments. For example, the Dijkstra algorithm has A relatively high computational complexity, the PRM algorithm has poor adaptability in real-time obstacle avoidance, and the A* algorithm is prone to generating redundant nodes in complex terrains. In recent years, research on underground mine scenarios has also pointed out that there are many difficulties in the integration of global planning and local planning. This paper proposes an enhanced A* algorithm in conjunction with the Dynamic Window Approach (DWA) to enhance the efficiency, search accuracy, and obstacle avoidance capability of path planning by optimizing the target function and eliminating redundant nodes. This approach enables path smoothing to be performed. In order to ensure that the requirement of multiple target point detection is realized, an RRT algorithm is proposed to reduce the element of randomness and uncertainty in the path planning process, leading to an increase in the convergence rate and overall performance of the algorithm. The solution to the problem of determining the global optimal path is proposed to be simplified by means of the optimal path planning algorithm based on the gradient coordinate rotation method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. The findings of simulation experiments conducted within the MATLAB environment demonstrate that the proposed algorithm exhibits a substantial enhancement in terms of three key metrics: path planning time, path length, and obstacle avoidance efficiency, when compared with conventional methodologies. This study provides a theoretical foundation for the autonomous navigation of mobile robots in coal mines.

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot’s navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.

As an important tool for exploring the ocean, autonomous underwater vehicle (AUV) plays an irreplaceable role in various marine activities. Due to the complexity and uncertainty of the marine environment, AUV is required to develop in a more intelligent direction. How to ensure that AUV avoids obstacles and reaches the target point smoothly is a key research issue of the AUV. The dynamic window approach (DWA) is adopted to AUV in this paper to achieve AUV's autonomous obstacle avoidance for static obstacles. The DWA is used to search for the optimal velocity command in its admissible velocity space by maximizing the objective function, however, the weights of its objective function are constant, which makes AUV lack flexibility in complex environments, and even unable to avoid obstacles. To address the above problem, reinforcement learning is introduced to optimize DWA. Q-learning, a reinforcement learning algorithm, is used to learn the weights of the DWA's objective function, which enables appropriate weights can be selected in different environments and improves the applicability of DWA in the complex environment. Compared with the original DWA, the DWA combined with Q-learning is effective and suitable for complex obstacle environments.

Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive path lengths, and inadequate dynamic obstacle avoidance. Thus, the development of innovative path planning algorithms is essential. This article addresses the challenges of mobile robot path planning in complex environments, where traditional methods often converge to local optima, leading to suboptimal path lengths, and struggle with dynamic obstacle avoidance. To overcome these limitations, we propose an integrated algorithm, the enhanced sparrow search algorithm combined with the dynamic window approach (ESSA-DWA). The algorithm first utilizes ESSA for global path planning, followed by local path planning facilitated by the DWA. Specifically, ESSA incorporates Tent chaotic initialization to enhance population diversity, effectively mitigating the risk of premature convergence to local optima. Moreover, dynamic adjustments to the inertia weight during the search process enable an adaptive balance between exploration and exploitation. The integration of a local search strategy further refines individual updates, thereby improving local search performance. To enhance path smoothness, the Floyd algorithm is employed for path optimization, ensuring a more continuous trajectory. Finally, the combination of ESSA and DWA uses key nodes from the global path generated by ESSA as reference points for the local planning process of DWA. This approach ensures that the local path closely follows the global path while also enabling real-time dynamic obstacle detection and avoidance. The effectiveness of the algorithm has been validated through both simulations and practical experiments, offering an efficient and viable solution to the path planning problem.

Path planning is a key technology for autonomous robot navigation; in order to allow the robot to achieve the optimal navigation path and real-time obstacle avoidance under the condition of complex and bumpy roads, an optimization algorithm based on the fusion of optimized A* algorithm and Dynamic Window Approach is proposed. The traditional A* algorithm generates the optimal path by minimizing the path cost. But when the road in the area where the robot is located is uneven, the path planned by the traditional A* algorithm may be the shortest but not the optimal. Because the robot can pass the ravines and bumps on the road, the cost of passing is higher at this time. At this point, for the robot, path planning must consider the path length and the number of undulations in the path. For the heuristic function of the traditional A* algorithm, the weight information of the road surface is added to it. The optimization algorithm can obtain an optimized path avoiding a lot of bumpy roads. Since the path has a large number of redundant turning points, the turning point extraction strategy is adopted to delete the redundant points of the path, and finally an optimal path with a little bumpy road, short length, and few turning points is obtained. Secondly, in order for the robot to obtain local obstacle avoidance capabilities based on the global optimal path, the optimized A* algorithm is combined with the Dynamic Window Approach to obtain a fusion algorithm that combines global path planning and local path planning. Experimental simulation results show that this algorithm can effectively avoid unnecessary bumpy roads, remove redundant turning points, improve path flatness, increase path smoothness, and achieve a compromise between path length and road surface undulations. At the same time, the local real-time obstacle avoidance capability based on the optimal path is also increased.

Path planning for autonomous mobile robots in dynamic environments is a critical challenge, particularly when robots must navigate both known and unknown obstacles in real-time. Traditional methods like ant colony optimization (ACO) have shown promise in global path planning but often suffer from slow convergence and the risk of getting trapped in local optima. Similarly, while the dynamic window approach (DWA) excels in real-time obstacle avoidance, it exhibits delayed response and higher failure rates in scenarios with fast-moving obstacles. To address these challenges, this study proposes a hybrid path planning framework, IACO-QDWA, which integrates an improved ACO (IACO) for global path optimization with a Q-learning-enhanced DWA (QDWA) for dynamic obstacle avoidance. IACO improves global path search through goal-oriented initialization and adaptive pheromone updates, while QDWA enhances local decision-making by optimizing the state-action space. The experiments demonstrate that IACO-QDWA achieves a reasonable balance between path quality, obstacle avoidance success rate, and real-time performance. It exhibits stable obstacle avoidance behavior and reliable navigation capabilities in highly dynamic environments.

The traditional Dynamic Window Approach (DWA) with constant weight values of the evaluation function leads to the inability of obstacle avoidance for the Automated Guided Vehicles (AGV) to perform obstacle avoidance and path planning in the complex environment. Effective avoidance of complex obstacles requires adaptive weight adjustment to address the evaluation function’s challenges. This paper proposes an adaptive DWA (ADWA), which introduces neural network training on the basis of the Mamdani DWA (MDWA). Firstly, the Mamdani type fuzzy controller is designed, and then the adaptive neuro-fuzzy controller is obtained by neural network training. Then, experiments are carried out through the MATLAB simulation environment. The simulation experiment results show that the improved DWA compared to traditional DWA can make the AGV pass the obstacle environment with a better trajectory and reduce the time. The improved DWA improves the autonomous obstacle avoidance capability of AGVs, which not only perfectly fits our task requirements, but also has apparent scientific and practical significance in developing AGV autonomous obstacle avoidance technology.

Aiming at the problems of low search efficiency of A* algorithm in traditional path planning, many redundant points, and inability to avoid unknown obstacles in real-time in complex environments, this paper proposes a path planning algorithm based on A* combined with Dynamic Window Approach (DWA) algorithm. First, the evaluation function of the traditional A* algorithm and the expansion strategy of sub-nodes are improved to improve the safety and search efficiency of the global path. Then the redundant nodes in the global path are processed to reduce the number of turning points and improve the smoothness of the global path. to improve the instability and energy consumption of the robot during travel; finally, based on the global path planning, the DWA algorithm is introduced to perform path planning in the local unknown environment. The local path planning is completed by retaining the key path turning points as intermediate path guidance. Real-time obstacle avoidance. Through simulation experiments, the effectiveness and feasibility of the fusion algorithm are verified.

There are problems such as poor real-time performance, low efficiency of path planning, and easy to fall into local optimum when using traditional path planning algorithms for unmanned surface vehicle path planning. Aiming at the above problems, this paper proposes a path planning algorithm for unmanned surface vehicles that integrates the optimized A* algorithm and the dynamic window approach. The algorithm firstly optimizes and adjusts the cost function and path search method of the traditional A* algorithm, and secondly adopts the double broken line optimization strategy to greatly reduce the number of path inflection points and improve the smoothness of the global path. Finally, by introducing the path evaluation sub-function into the evaluation function of the dynamic window approach, the optimized A* algorithm is integrated with the dynamic window approach. The simulation results show that the path search efficiency of the algorithm in the static environment is significantly improved compared with the traditional A* algorithm, the smoothness of the path is better than that of the traditional A* algorithm, and it has a good dynamic obstacle avoidance effect in the dynamic environment.

To address the problem of real-time obstacle avoidance and trajectory planning for autonomous underwater vehicle (AUV) operating in complex and uncertain underwater environments, this paper proposes a physics-informed deep reinforcement learning method (PIDRL). First, a dynamic model of the AUV is established based on the physical characteristics of the underwater environment. Then, a feasibility-checking function is incorporated into the action selection stage, while physics-based constraint terms—related to motion smoothness, attitude stability, and environmental adaptability—are embedded into the reward and loss functions. In this way, environmental physics and dynamic constraints are integrated into the learning framework of the Deep Q-Network. Finally, through simulation training and validation, a policy model is obtained and tested for its obstacle-avoidance and path-planning performance in a designed underwater environment. Simulation results show that, compared with traditional DRL and NMPC algorithms, the proposed method achieves superior performance in obstacle avoidance success rate and path smoothness, with path smoothness improved by 0.06–0.12 and trajectory length reduced by approximately 3%. The results demonstrate that the proposed method exhibits stronger adaptability and robustness, significantly enhancing the AUV’s autonomous obstacle-avoidance and trajectory-planning capabilities in complex dynamic environments, and providing an effective solution for physically reliable underwater intelligent control.

Multi-robot systems are experiencing increasing popularity in joint rescue, intelligent transportation, and other fields. However, path planning and navigation obstacle avoidance among multiple robots, as well as dynamic environments, raise significant challenges. We propose a distributed multi-mobile robot navigation and obstacle avoidance method in unknown environments. First, we propose a bidirectional alternating jump point search A* algorithm (BAJPSA*) to obtain the robot's global path in the prior environment and further improve the heuristic function to enhance efficiency. We construct a robot kinematic model based on the dynamic window approach (DWA), present an adaptive navigation strategy, and introduce a new path tracking evaluation function that improves path tracking accuracy and optimality. To strengthen the security of obstacle avoidance, we modify the decision rules and obstacle avoidance rules of the single robot and further improve the decision avoidance capability of multi-robot systems. Moreover, the mainstream prioritization method is used to coordinate the local dynamic path planning of our multi-robot systems to resolve collision conflicts, reducing the difficulty of obstacle avoidance and simplifying the algorithm. Experimental results show that this distributed multi-mobile robot motion planning method can provide better navigation and obstacle avoidance strategies in complex dynamic environments, which provides a technical reference in practical situations.

The A* algorithm is widely used in path planning for Automated Guided Vehicles (AGVs), but the path it generates is prone to collision with random obstacles. To address this issue, this paper proposes a hybrid path planning algorithm integrating the improved A* algorithm with Dynamic Window Approach (DWA). Firstly, a global key point extraction strategy is adopted, and Bresenham’s line algorithm is used to eliminate redundant path points and turning inflection points, optimizing the conciseness and continuity of the path while redefining the child nodes of the current position. Secondly, in complex environments, the inflection points of the global path are taken as the target points of DWA to segment the path, and local dynamic planning is combined to achieve real-time obstacle avoidance. Simulation results show that compared with the traditional A* algorithm, the improved algorithm reduces the planning time by 24.19%, decreases the number of inflection points by 40.00%, and shortens the path length by 1.49%. In environments with random obstacles, the path generated by the hybrid algorithm is smoother, which can effectively enhance the local obstacle avoidance capability and improve the safety of path planning. Furthermore, physical experiments on an AGV platform with a distributed master-slave control architecture (STM32 microcontroller and Jetson embedded processor) verify the algorithm’s hardware compatibility and real-time computing performance, validating its engineering applicability in practical industrial scenarios.

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations.

After a quarter century of mobile robot research, applications of this fascinating technology appear in real-world settings. Some require operation in environments that are densely cluttered with moving obstacles. Public mass exhibitions or conventions are examples of such challenging environments. This dissertation addresses the navigational challenges that arise in settings where mobile robots move among people and possibly need to directly interact with humans who are not used to dealing with technical details. Two important aspects are solved: Reliable reactive obstacle avoidance to guarantee safe operation, and smooth path planning that allows to dynamically adapt environment information to the motion of surrounding persons and objects. Given the existing body of research results in the field of obstacle avoidance and path planning, which is reviewed in this context, particular attention is paid to integration aspects for leveraging advantages while compensating drawbacks of various methods. In particular, grid-based wavefront propagation (NF1 and fast marching level set methods), dynamic path representation (bubble band concept), and high-fidelity execution (dynamic window approach) are combined in novel ways. Experiments demonstrate the robustness of the obstacle avoidance and path planning systems.

In the navigation of unmanned surface vehicles (USVs), various types of obstacles may be encountered, which can be categorized into real-time collision avoidance among multiple USVs and obstacle avoidance between USVs and other obstacles. Most existing autonomous obstacle avoidance algorithms do not account for the nonlinear motion characteristics of USVs, often resulting in non-compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) and a tendency to fall into local optima. To address these issues, this paper proposes a path planning algorithm that integrates the dynamic window approach (DWA) considering nonlinear characteristics with COLREGs, making the USV's motion trajectory more applicable to practical engineering scenarios. A kinematic mathematical model is established based on the motion characteristics of USVs, and an evaluation function for the optimal path is constructed using DWA. The fully informed search algorithm (FISA) is employed to select the optimal set of velocities and steering angles from the velocity sampling set, based on different cost calculation methods. The USVs use a laser radar for local obstacle detection, enabling real-time dynamic obstacle avoidance. To address the real-time collision avoidance problem among multiple USVs in open waters, the algorithm filters out COLREGs-compliant avoidance maneuvers during path planning. The correctness and feasibility of the fusion algorithm were verified through comparative simulations. In the simulated environment model, the influence of ocean currents on the USV was introduced, and multiple sets of experiments under different conditions were conducted to compare the motion trajectories, average travel distances, and average travel times of the USV. The simulation results indicate that the USV can perform accurate obstacle avoidance when encountering various types of obstacles. Compared to the traditional DWA algorithm, the proposed approach demonstrates advantages in terms of travel distance and travel time, while still achieving effective obstacle avoidance.