Path Planning In Dynamic Environment Research Articles

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology.

In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV's heading direction, considering its impact on the AUV's speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV's navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

Path Planning in Dynamic Environments Based on Q-Learning

With the rapid progress of science and technology, the scope of applications for mobile robots is growing. Path planning in dynamic environment is always a challenging task for mobile robot, which shows significant impacts in the medical field and the military field. Q-learning, a model-free reinforcement learning algorithm, can recognize its surroundings and demonstrate a system making decisions for itself about how this algorithm learns to make accurate decisions about achieving optimal target. Therefore, a path planning algorithm was proposed for robots in dynamic environments based on Q-learning. This algorithm can successfully generate several viable paths in environments with both static obstacles, dynamic obstacles and target point. First, three environments with different levels of complexity were created. Afterwards, to generate several optimal paths, the path planning algorithm was conducted in multiple times in different environments. Finally, the experimental results were collected and visualized to illustrate information. The effectiveness of the proposed algorithm is validated by experiment results in solving problems of path planning in dynamic environments with required point.

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.

Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments

Dyna-Q is a reinforcement learning method widely used in AGV path planning. However, in large complex dynamic environments, due to the sparse reward function of Dyna-Q and the large searching space, this method has the problems of low search efficiency, slow convergence speed, and even inability to converge, which seriously reduces the performance and practicability of it. To solve these problems, this paper proposes an Improved Dyna-Q algorithm for AGV path planning in large complex dynamic environments. First, to solve the problem of the large search space, this paper proposes a global path guidance mechanism based on heuristic graph, which can effectively reduce the path search space and, thus, improve the efficiency of obtaining the optimal path. Second, to solve the problem of the sparse reward function in Dyna-Q, this paper proposes a novel dynamic reward function and an action selection method based on the heuristic graph, which can provide more intensive feedback and more efficient action decision for AGV path planning, effectively improving the convergence of the algorithm. We evaluated our approach in scenarios with static obstacles and dynamic obstacles. The experimental results show that the proposed algorithm can obtain better paths more efficiently than other reinforcement-learning-based methods including the classical Q-Learning and the Dyna-Q algorithms.

A Novel Method for Path Planning of Mobile Robots via Fuzzy Logic and ant Colony Algorithem in Complex Daynamic Environments

Researches on mobile robot path planning with meta-heuristic methods to improve classical approaches have grown dramatically in the recent 35 years. Because routing is one of the NP-hard problems, an ant colony algorithm that is a meta-heuristic method has had no table success in this area. In this paper, a new approach for solving mobile robot navigation in dynamic environments, based on the heuristic feature of an optimized ant colony algorithm is proposed. Decision-making influenced by the distances between the origin and destination points and the angle variance to the nearest obstacles. Ideal paths are selected by the fuzzy logic. The proposed ant colony algorithm will optimize the fuzzy rules’ parameters that have been using to On-line (instant) path planning in dynamic environments. This paper presents a new method that can plan local routs all over the area and to guide the moving robot toward the final track. Using this algorithm, mobile robots can move along the ideal path to the target based on the optimal fuzzy control systems in different environments, especially in dynamic and unknown environments.

Dynamic Waypoint Navigation: Model-Based Adaptive Trajectory Planner for Human-Symbiotic Mobile Robots

Path planning in dynamic environments is still a challenging issue with autonomous mobile robots. Current methods lack adaptability to various passing scenarios, a variety of passing trajectories including an acceleration path, or immediacy in planning time, which require human-aware navigation. In this study, we propose Dynamic Waypoint Navigation (DWN), which is a model-based adaptive real-time trajectory planning method. DWN first predicts human-robot path interference and the time and position of the interference on the basis of the measured velocity of humans. It then dynamically designates several waypoints considering the time delay of both calculation time and robot travel time. Then, DWN generates several trajectories by combining different speeds (default, acceleration, and deceleration) and paths (default, right, and left) and selects the best trajectory in terms of an interference-avoidance energy cost based on the degree of velocity-vector change. DWN can also output a trajectory within 0.5 s to immediately adapt to changes in human behavior and adopt a simple mathematical model and algorithm to enable easy expansion. Simulation and experimental results reveal that the DWN can adequately select a time-efficient trajectory in real-time and adaptively change a trajectory depending on human movement.

Path Planning in Dynamic Environments with Adaptive Dimensionality

Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic.

Hybrid metaheuristic approach for robot path planning in dynamic environment

Recently robots have gained great attention due to their ability to operate in dynamic and complex environments with moving obstacles. The path planning of a moving robot in a dynamic environment is to find the shortest and safe possible path from the starting point towards the desired target point. A dynamic environment is a robot's environment that consists of some static and moving obstacles. Therefore, this problem can be considered as an optimization problem and thus it is solved via optimization algorithms. In this paper, three approaches for determining the optimal pathway of a robot in a dynamic environment were proposed. These approaches are; the particle swarming optimization (PSO), ant colony optimization (ACO), and hybrid PSO and ACO. These used to carry out the path planning tasks effectively. A set of certain constraints must be met simultaneously to achieve the goals; the shortest path, the least time, and free from collisions. The results are calculated for the two algorithms separately and then that of the hybrid algorithm is calculated. The effectiveness and superiority of the hybrid algorithm were verified on both PSO and ACO algorithms.

Real-time path planning in dynamic environments for unmanned aerial vehicles using the curve-shortening flow method

This article proposes a new algorithm for real-time path planning in dynamic environments based on space-discretized curve-shortening flows. The so-called curve-shortening flow method shares working principles with the well-established elastic bands method and overcomes some of its drawbacks concerning numerical robustness and parameterability. This is achieved by efficiently applying semi-implicit time integration for evolving the path and secondly by developing a methodology for setting the algorithm’s parameters based on physical quantities. Different short- and long-term validation scenarios are performed with three interlinked instances of the curve-shortening flow method each running on an individual industrial control and driving a real or a simulated unmanned aerial vehicle.

Multi-robot path planning in wireless sensor networks based on jump mechanism PSO and safety gap obstacle avoidance

In order to meet the real-time and accurate requirements of multi-robot path planning in dynamic environment, this paper adopted wireless sensor network to locate robots and obstacles and used an improved artificial intelligent algorithm to plan path. In this paper, a jumping mechanism particle swarm optimization (JPSO) algorithm and a safety gap obstacle avoidance algorithm (SGOA) algorithm were proposed. Compared with canonical PSO algorithm, JPSO algorithm has three improvement strategies: Fitness value evaluation function, new learning sample and jumping strategy. The JPSO algorithm updates the particles with poor comprehensive quality by jumping and adjusts the inertia weight adaptively according to fitness value evaluation function. With the cooperation of new learning samples, the global searching ability and precision of the algorithm can be improved. SGOA algorithm is mainly aimed at the problem that robots with low priority are stuck in a long wait and cannot continue to walk when avoiding obstacles. By implementing the SGOA algorithm, a new collision-free safety path can be optimized for the robot with low priority. In order to verify JPSO and SGOA algorithm, a lot of experiments were done. JPSO algorithm was compared with two other improved PSO algorithms with 6 standard test functions. The path planning and obstacle avoidance experiments of six robots were realized using the JPSO and SGOA algorithm. The experimental results show that JPSO algorithm has higher accuracy and faster convergence speed than the other two improved PSO algorithms, and SGOA algorithm can solve the dynamic obstacle avoidance problem in the path planning of multiple robots well.

Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information. Different from existing learning-based methods, we introduce a globally guided reinforcement learning approach ( G2RL ), which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state-of-the-art benchmarks.

Path planning of mobile robot in dynamic environment: fuzzy artificial potential field and extensible neural network

Path planning in dynamic environment is a great challenge for mobile robot. A large number of approaches have been used to deal with it. Since the neural network algorithm has the ability to find the optimal solution at high speed and self-learning function, it has achieved extensive applications in the path planning tasks. Considering that the optimization performance of the neural network heavily depends on the quality of the training sample, this paper proposes a novel way to provide the training samples for the neural network. Work space of the robot is divided into two parts: global safe area and local dangerous area. In the global safe area, the robot only receives the attraction force from the target and it moves towards the target directly. In the dangerous area, except the attraction force, the robot also receives the repulsion force from the obstacle(s). The repulsion force and the angle between the obstacle and the target (origin of the coordinate is in the position of the robot) are used to be the inputs of the fuzzy inferencing system, and the deflection angle of the robot is the output. The final moving direction of the robot is determined by summing this deflection angle and the direction of the attraction force. The coordinates of the target and obstacle, and the moving direction of the robot corresponding to this position relationship, constitute the training samples for the neural network. Benefited from the precise moving direction obtained by the fuzzy artificial potential field algorithm, the neural network gets excellent path optimization ability. Simulation and physical experiment results demonstrate the potential of the proposed algorithm.

Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics

In this paper, a novel variant of bio-inspired planning algorithms is presented for robot collision-free path planning in dynamic environments without prior information. The first contribution of this paper is that, with mild technical analysis, the traditional neural dynamic model almost always returns a sub-optimal choice in some challenging scenarios, such as the boundary map and the narrow pathway map. Second, the proposed planning algorithm, namely the padding mean neural dynamic model, is a topologically organized network with connections among neighbouring neurons and is good for spreading nerve impulses such as a waves without coupling effects. The signal transduction method within a network is based on a dynamic neural activity field, which propagates high neural activity from the target state to the whole field, excluding obstacle regions. Third, simulation studies are conducted to compare the performance of the proposed planning algorithm and other popular planning algorithms in terms of effectiveness and efficiency. As a result, the proposed method can drive a robot to find more reasonable paths in both static maps and unknown dynamic scenarios with moving obstacles and a moving target. Finally, the novel excitatory input design of the proposed algorithm is discussed and analysed to explore the neural stimulus propagation mechanism within the network.

Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm

Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human’s interference in a dynamic environment are thriving. A dynamic environment is the robot’s environment which is comprised of some static obstacles as well as several movable obstacles that their quantity and location change randomly through the time. Efficient path planning is one the significant necessities of these kind of robots to do their tasks effectively. Mobile robot path planning in a dynamic environment is finding a shortest possible path from an arbitrary starting point toward a desired goal point which needs to be safe (obstacle avoidance) and smooth as well as possible. To achieve this target, simultaneously satisfying a collection of certain constraints including the shortest, smooth, and collision free path is required. Therefore, this issue can be considered as an optimization problem, consequently solved via optimization algorithms. In this article, a new method based on cuckoo optimization algorithm is proposed for solving the mobile robot path planning problem in a dynamic environment. Furthermore, to diminish the computational complexity, the feature vector is also optimized (i.e. reduced in dimension) via a new proposed technique. The simulation results show the performance of proposed algorithm in finding a short, safe, smooth, and collision free path in different environment conditions.

Sensor‐based complete coverage path planning in dynamic environment for cleaning robot

Using Complete Coverage Path Planning (CCPP), a cleaning robot could visit every accessible area in the workspace. The dynamic environment requires the higher computation of the CCPP algorithm because the path needs to be replanned when the path might become invalid. In previous CCPP methods, when the neighbours of the current position are obstacles or have been visited, it is challenging for the robot to escape from the deadlocks with the least extra time cost. In this study, a novel CCPP algorithm is proposed to deal with deadlock problems in a dynamic environment. A priority template inspired by the short memory model could reduce the number of deadlocks by giving the priority of directions. Simultaneously, a global backtracking mechanism guides the robot to move to the next unvisited area quickly, taking the use of the explored global environmental information. What's more, the authors extend their CCPP algorithm to a multi-robot system with a market-based bidding process which could deploy the coverage time. Experiments of apartment-like scenes show that the authors’ proposed algorithm can guarantee an efficient collision-free coverage in dynamic environments. The proposed method performs better than related approaches on coverage rate and overlap length.

Human Brain Function in Path Planning: a Task Study

Despite plenty of research being performed in the human movement science, less attention has been paid to the probable method used by the human brain in the higher-level motor planning. The previous studies suggest that the human brain may use a predictive approach to anticipate physical dynamics of the body and the environment to plan a short and collision-free movement trajectory. We propose that the human brain may use a model-based prediction procedure in path planning in which a finite prediction horizon is used to estimate the future state of the body and the environment. A goal-oriented driving task (GDT) in a virtual street was designed to consider the human path planning method in dynamic environments. Two groups of experiments were presented to consider the ability of the human brain in estimation of a dynamic object location and planning a collision-free path. The first group of study includes four GDTs, with different conditions to evaluate how the human planning strategy would change by varying the configuration of the environment. In the second group, the changes of human planning in a visually obscured and blurred situation were considered. The results are in compliance with the theory of using a model-based prediction approach by human brains and indicate that the subjects benefit from a prediction horizon to plan their paths. Our studies provide evidence to introduce possible factors which may be used by the human brain during path planning in dynamic environments.

Online path planning in dynamic environments using the curve shortening flow method

Handling tasks with robots which are interacting in a shared workspace provide a high risk of collision. A new approach based on curve shortening flows enables collision-free path planning for robots within a varying environment on basis of a workspace model. Thereby a global path planning method based on geometrical curvature flow is combined with the locally and reactively acting potential field method. In the authors’ previous work this path was formulated as a non-linear partial differential equation and solved using explicit time-integration. This article presents a linearized model of this method which overcomes the numerical stability problems of the former formulation and allows a more efficient computation which is strongly required for the algorithm to be run on a real-time robot control. After a detailed explanation of the algorithm simulative and experimental results using the example of a handling task performed by two 5-axis manipulators is presented and effects of different parameter settings on the resulting path are discussed.

뉴럴 포텐셜 필드 알고리즘을 이용한 이동 로봇의 지역 경로계획

Robot"s technology was very simple and repetitive in the past. Nowadays, robots are required to perform intelligent operation. So, path planning has been studied extensively to create a path from start position to the goal position. In this paper, potential field algorithm was used for path planning in dynamic environments. It is used for a path plan of mobile robot because it is elegant mathematical analysis and simplicity. However, there are some problems. The problems are collision risk, avoidance path, time attrition. In order to resolve path problems, we amalgamated potential field algorithm with the artificial neural network system. The input of the neural network system is set using relative velocity and location between the robot and the obstacle. The output of the neural network system is used for the weighting factor of the repulsive potential function. The potential field algorithm problem of mobile robot"s path planning can be improved by using artificial neural network system. The suggested algorithm was verified by simulations in various dynamic environments.

Socially Adaptive Path Planning in Human Environments Using Inverse Reinforcement Learning

A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially adaptive manner such as respecting comfort zones of the pedestrians. We propose a framework for socially adaptive path planning in dynamic environments, by generating human-like path trajectory. Our framework consists of three modules: a feature extraction module, inverse reinforcement learning (IRL) module, and a path planning module. The feature extraction module extracts features necessary to characterize the state information, such as density and velocity of surrounding obstacles, from a RGB-depth sensor. The inverse reinforcement learning module uses a set of demonstration trajectories generated by an expert to learn the expert’s behaviour when faced with different state features, and represent it as a cost function that respects social variables. Finally, the planning module integrates a three-layer architecture, where a global path is optimized according to a classical shortest-path objective using a global map known a priori, a local path is planned over a shorter distance using the features extracted from a RGB-D sensor and the cost function inferred from IRL module, and a low-level system handles avoidance of immediate obstacles. We evaluate our approach by deploying it on a real robotic wheelchair platform in various scenarios, and comparing the robot trajectories to human trajectories.

Study of Three-Dimensional on-line path Planning for UAV based on Pythagorean Hodograph Curve

Abstract For the demand of the UAV autonomous path planning in dynamic thread environment, the three-dimensional path planning algorithm on-line of UAV based on Pythagorean Hodograph (PH) curve is put forward. According to the UAV’s current flight state such as location and speed, and the interrupt point or the target’s state, a flyable path with continuous curvature is planed out online. The roles of the key parameters are analyzed and the range of the values are given. On this basis, the distribution estimation algorithm is used to optimize the selection of path parameters, which avoid the blindness iterative process and consider the kinematics constraints such as the curvature, torsion and climbing angle. The multiple UAVs’ three dimensional path planning in dynamic environment are tested. Simulation results prove the validity and practicability of the algorithm.