Optimal and Dynamically Feasible Path Planning for an Anguilliform Fish-Inspired Robot in Presence of Obstacles

2018 ◽  
Author(s):  
Santhosh Kumar Thati ◽  
Aditi Raj ◽  
Atul Thakur
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Computation Time ◽  
Dynamics Simulation ◽  
A Algorithm ◽  
Underwater Robots ◽  
Motion Primitives ◽  
Simulation Based ◽  
Feasible Path ◽  
Marine Applications

Exploration of obstacle-ridden underwater regions for various marine applications like automated inspection, maintenance and repair of sub-sea structures and search and rescue during disaster relief is often not possible to be carried out by the human divers. Owing to their slender and hyper-redundant structure, Anguilliform-inspired robots are capable of negotiating narrow regions. However, the challenges involved in the motion planning of Anguilliform-inspired robots include the dynamic constraints imposed by the hyper-redundant joints, the interaction between fluid environment and the robot, and the presence of obstacles. This paper reports a model-predictive motion planning approach for an Anguilliform-inspired robot, wherein dynamically feasible motion primitives are generated using a dynamics simulator. The motion primitives are then used for generating a roadmap over which A* algorithm is used for searching an optimal, obstacle-free, and dynamically feasible path to the goal. Use of Euclidean heuristic in the A* based path planning for hyper-redundant underwater robots often results in the expansion of a large number of nodes and thereby slow-down the computations. Hence, we present a simulation-based admissible heuristic function that led to a speed-up of path search computation time by a factor varying from 3.1 to 5.5 over the Euclidean heuristic for our simulation-based experiments. The factor is dependent on the complexity of the scene. We also use dynamics simulation for estimating action-specific convex collision envelops for precise and efficient collision detection during the expansion of nodes in A*.

scholarly journals Survey of Robot 3D Path Planning Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Liang Yang ◽  
Juntong Qi ◽  
Dalei Song ◽  
Jizhong Xiao ◽  
Jianda Han ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Free Path ◽  
Optimal Path ◽  
Temporal Constraints ◽  
Three Dimension ◽  
Underwater Robots ◽  
Kinematic Constraints ◽  
Aerial Robots ◽  
Planning Algorithms

Robot 3D (three-dimension) path planning targets for finding an optimal and collision-free path in a 3D workspace while taking into account kinematic constraints (including geometric, physical, and temporal constraints). The purpose of path planning, unlike motion planning which must be taken into consideration of dynamics, is to find a kinematically optimal path with the least time as well as model the environment completely. We discuss the fundamentals of these most successful robot 3D path planning algorithms which have been developed in recent years and concentrate on universally applicable algorithms which can be implemented in aerial robots, ground robots, and underwater robots. This paper classifies all the methods into five categories based on their exploring mechanisms and proposes a category, called multifusion based algorithms. For all these algorithms, they are analyzed from a time efficiency and implementable area perspective. Furthermore a comprehensive applicable analysis for each kind of method is presented after considering their merits and weaknesses.

scholarly journals Path Planning of Mobile Robot in Unknown Environment

2010 ◽  
pp. 122-127
Author(s):  
Pradipta kumar Das ◽  
Romesh Laishram ◽  
Amit Konar
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Shortest Path ◽  
Target Position ◽  
Initial Position ◽  
A Algorithm ◽  
Unknown Environment ◽  
Feasible Path ◽  
The Cost

In this paper, we study the online path planning for khepera II mobile robot in an unknown environment. The well known heuristic A* algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance and time metric as the cost function.

scholarly journals Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

2021 ◽  
Vol 17 (4) ◽  
pp. 491-505
Author(s):  
G. Kulathunga ◽  
◽  
D. Devitt ◽  
R. Fedorenko ◽  
A. Klimchik ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Free Path ◽  
Optimal Path ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Straight Lines ◽  
Path Planning Algorithm

Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.

scholarly journals Robust path planning for avoiding obstacles using time-environment dynamic map

2019 ◽  
Vol 53 (1-2) ◽  
pp. 214-221 ◽  
Author(s):  
Xiaolin Zhao ◽  
Yu Zhang ◽  
Boxin Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Urban Space ◽  
Field Of View ◽  
A Algorithm ◽  
Aerial Vehicles ◽  
Trace Back ◽  
Planning Approach ◽  
Dynamic Map ◽  
Feasible Path

Small unmanned aerial vehicles are widely used in urban space because of its flexibility and maneuverability. However, there are full of dynamic obstacles and immobile obstacles which will affect safe flying in urban space. In this paper, a novel integrated path planning approach for unmanned aerial vehicles is presented, which is consisted of three steps. First, a time-environment dynamic map is constructed to represent obstacles by introducing time axis. Second, unmanned aerial vehicles’ flyable paths are explored based on breadth-first algorithm. Third, a path planning method using A* algorithm and local trace-back model is designed in order to discover sub-optimal feasible path rapidly in unmanned aerial vehicles’ field of view. Finally, the simulation results have illustrated that the proposed method can ensure unmanned aerial vehicles’ autonomous path planning safely and effectively in urban space crowded with obstacles.

Improved Sampling Strategies for Protein Model Refinement based on Molecular Dynamics Simulation

2020 ◽  
Author(s):  
Lim Heo ◽  
Collin Arbour ◽  
Michael Feig
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Structure Prediction ◽  
Protein Structures ◽  
Conformational Space ◽  
Dynamics Simulation ◽  
Model Refinement ◽  
Protein Model ◽  
Lower Accuracy ◽  
Simulation Based

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. Those methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on an optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore conformational space more broadly. Based on the insight of this analysis we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here. <br>

scholarly journals Global Dynamic Path Planning Fusion Algorithm Combining Jump-A* Algorithm and Dynamic Window Approach

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 19632-19638
Author(s):  
Lisang Liu ◽  
Jinxin Yao ◽  
Dongwei He ◽  
Jian Chen ◽  
Jing Huang ◽  
...  
Keyword(s):  
Path Planning ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Window Approach

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

scholarly journals Comparison of Downdraught and Up Draft Passive Air Conduction Systems (PACS) in a Winery Building

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 259
Author(s):  
Ádám László Katona ◽  
István Ervin Háber ◽  
István Kistelegdi
Keyword(s):  
Air Conditioning ◽  
Natural Ventilation ◽  
Dynamics Simulation ◽  
Computational Fluid Dynamics Simulation ◽  
General Applicability ◽  
Change Rate ◽  
Air Change Rate ◽  
Simulation Based ◽  
Ventilation Performance ◽  
Air Conduction

A huge portion of energy consumption in buildings comes from heating, ventilation, and air conditioning. Numerous previous works assessed the potential of natural ventilation compared to mechanical ventilation and proved their justification on the field. Nevertheless, it is a major difficulty to collect enough information from the literature to make decisions between different natural ventilation solutions with a given situation and boundary conditions. The current study tests the passive air conduction system (PACS) variations in the design phase of a medium-sized new winery’s cellar and production hall in Villány, Hungary. A computational fluid dynamics simulation based comparative analysis enabled to determine the differences in updraft (UD) and downdraught (DD) PACS, whereby the latter was found to be more efficient. While the DD PACS performed an air change range of 1.02 h−1 to 5.98 h−1, the UD PACS delivered −0.25 h−1 to 12.82 h−1 air change rate. The ventilation performance of the DD version possessed lower amplitudes, but the distribution was more balanced under different wind incident angles, thus this version was chosen for construction. It could be concluded that the DD PACS provides a more general applicability for natural ventilation in moderate climates and in small to medium scale industry hall domains with one in- and one outlet.

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