Path Finding Strategy Research Articles

Simulation of obstacle avoidance planning based on improved RRTstar robotic arm

Abstract An obstacle avoidance pathfinding strategy with an augmented RRT* algorithm was presented for the robotic arm’s operation. A simulation model of the robotic arm was created. The Monte Carlo method was employed to solve its workspace. To mitigate the weaknesses of the RRT* algorithm, such as slow convergence and poor orientation, the algorithm was improved by combining gravitational and repulsive force fields from the artificial potential field method with a dynamic step size strategy. Simulation experiments were carried out using MATLAB. The results showed that the revised algorithm lessened the path search time by 52.1% and the path search length by 9.1% as opposed to the original RRT* algorithm. The experiments were verified in the ROS backdrop. The proposed algorithm was found to be more advantageous for robotic arm obstacle avoidance.

Informed simulation for dynamic path finding in human-operated container terminals

Path finding for human-operated vehicles in a maritime container terminal can be very demanding, especially when pursued under dynamic traffic conditions and based on decisions that are self-governing. Preventing collision and interference among internal transfer vehicles is a major task for operations managers in organizing container transfers from the quay to the yard at minimal congestion. To this matter, we propose a novel simulation framework as an effective tool for managing conflicting goals among straddle carrier (SC) drivers. The framework embeds both local and global search strategies that are used to determine the best path between any two vertices of the grid-like transfer network. Furthermore, it also allows to make path finding decisions based on either dynamic or static data. Numerical experiments prove the effectiveness of this informed simulation-based approach. Specifically, when critical extraordinary events arise and force, for instance, SCs to cover greater distances during container transfer, the difference in performance achieved by using the local/global path finding strategy in conjunction with static/dynamic data becomes more significant.

Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm

The Jump Point Search (JPS) algorithm ignores the possibility of any-angle walking, so the paths found by the JPS algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the JPS algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-JPS algorithm based on an any-angle pathfinding strategy. First, based on the JPS algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-JPS algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-JPS algorithm reduces the path by 1.61–4.68% and the total turning angle of the path by 58.71–84.67% compared with the JPS algorithm. The AP-JPS algorithm reduces the computing time by 98.59–99.22% compared with the AP-Theta* algorithm.

Airborne Computer System Based Collision-Free Flight Path Finding Strategy Design for Drone Model

Airborne Computer System Based Collision-Free Flight Path Finding Strategy Design for Drone Model

An Energy-Efficient Mobile-Sink Path-Finding Strategy for UAV WSNs

Data collection using a mobile sink in a Wireless Sensor Network (WSN) has received much attention in recent years owing to its potential to reduce the energy consumption of sensor nodes and thus enhancing the lifetime of the WSN. However, a critical issue of this approach is the latency of data to reach the base station. Although many data collection algorithms have been introduced in the literature to reduce delays in data delivery, their performances are affected by the flight trajectory taken by the mobile sink, which might not be optimized yet. This paper proposes a new path-finding strategy, called Energy-efficiency Path-finding Strategy (EPS) in the Air-Ground Collaborative Wireless Sensor Network (AGCWSN). The proposed approach is able to greatly enhance the efficiency of data collection. The performance of the proposed strategy is simulated and compared with the existing strategies over several parameters. The simulation results show that the mobile sink with EPS can collects data with lower data delivery delay as compared to other existing strategies. The number of data retransmissions between sensor nodes and mobile sink in EPS is also the lowest in EPS among several existing strategies. The data delivery delay is 66% and 120% lower than Rest Center Tractor Scanning (RCTS) and Non-stop Center Tractor Scanning (NCTS) in irregular and grid topology respectively. The data delivery delay is 62% lower than Two Row Scanning (TRS) in grid topology and 120% lower than RkM in irregular topology. The packet loss of EPS-2 is 1.3% lower than RkM.

A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.

An optimal path finding strategy in networks based on random walk

In this paper, we study a path finding strategy based on random walk in which we allow multiple particles to set out from different but neighboring sources to their common destination. Three path finding models, the single-particle-form-one-source, the multiple-particles-from-one-source, and the last multiple-particles-form-multiple-sources (MSMP) are described. Then we apply the three models to different simulation networks. The experiment results show that the MSMP schema can decrease the path finding cost. Furthermore, we propose an absorption strategy to deal with the additional Brownian particles in networks. The experiment results on BA networks show that the absorption strategy can increase the probability of a successful path finding. In the end, we find he path found out by above methods may be the shortest theoretically, but may not be optimal in the practical application. To overcome this, we put forward a method to calculate the optimal path based on arrival reliability and verify its correctness by enumeration.

D* lite algorithm based path planning of mobile robot in static Environment

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite 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. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. 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 metric as the cost function.

Path Planning of Mobile Robot in Unknown Environment

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.

A self-organizing shortest path finding strategy on complex networks

The shortcomings of traditional methods to find the shortest path are revealed, and a strategy of finding the self-organizing shortest path based on thermal flux diffusion on complex networks is presented. In our method, the shortest paths between the source node and the other nodes are found to be self-organized by comparing node temperatures. The computation complexity of the method scales linearly with the number of edges on underlying networks. The effects of the method on several networks, including a regular network proposed by Ravasz and Barabási which is called the RB network, a real network, a random network proposed by Ravasz and Barabási which is called the ER network and a scale-free network, are also demonstrated. Analytic and simulation results show that the method has a higher accuracy and lower computational complexity than the conventional methods.

Study on Near-Optimal Path Finding Strategies in a Road Network

In this paper we analyze the existing defects of hierarchical and nonhierarchical path finding algorithm respectively in detail and present a hierarchical path finding algorithm based on the heuristic directing search approach which is used for promoting the nodes of lower level to higher level in the road network. The improved heuristic-hierarchical path finding algorithm was tested on road network of Xi'an city with a two-level hierarchy. The experimental results show that the heuristic based hierarchical path finding algorithm has been found to be over 25 times faster than the Dijkstra's algorithm and the ratio of the highest level road obtained by heuristic based hierarchical path finding algorithm is twice as much as the Dijkstra's algorithm. This means that the hierarchical path finding algorithm is suitable for the status of long-distance between origin and destination ( O- D). We also propose a novel yet simple path finding strategy based on a threshold, that is, if the distance of O- D is less than the threshold which was obtained by means of statistical analysis the nonhierarchical path finding algorithm will be used, otherwise we use hierarchical path finding algorithm to find a near-optimal or satisfactory path in city road network. The testing results of Xi'an city road network show that the roads which were found out using this new path finding strategy are easy to be adopted by drivers.

Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity

In this paper, we propose a susceptible-infected model with identical infectivity, in which, at every time step, each node can only contact a constant number of neighbors. We implemented this model on scale-free networks, and found that the infected population grows in an exponential form with the time scale proportional to the spreading rate. Furthermore, by numerical simulation, we demonstrated that the targeted immunization of the present model is much less efficient than that of the standard susceptible-infected model. Finally, we investigate a fast spreading strategy when only local information is available. Different from the extensively studied path-finding strategy, the strategy preferring small-degree nodes is more efficient than that preferring large-degree nodes. Our results indicate the existence of an essential relationship between network traffic and network epidemic on scale-free networks.

Arriving-on-Time Problem: Discrete Algorithm That Ensures Convergence

Finding optimal paths in stochastic networks is an important topic in many scientific and engineering fields. To cope with uncertainty, various performance measures, including expected travel time, reliability, value at risk, and expectation with chance constraint, have been introduced. The literature has shown that adaptive strategies that incorporate both anticipation and real-time information are more efficient than a preplanned strategy in a stochastic environment. Most adaptive optimal-path algorithms focus on least-expected travel time. Recently, an adaptive path-finding strategy, named the stochastic on-time arrival (SOTA) algorithm, was proposed to maximize the travel time reliability for any given time threshold. However, the originally proposed SOTA algorithm is based on the classic successive approximation. Whether this algorithm converges within a finite number of successive approximation steps is an open question. In this study, the proposed discrete SOTA algorithm runs in an optimal polynomial time and thus improves the computational efficiency significantly. Numerical examples are provided, and future extensions are proposed.

Arriving-on-Time Problem

Finding optimal paths in stochastic networks is an important topic in many scientific and engineering fields. To cope with uncertainty, various performance measures, including expected travel time, reliability, value at risk, and expectation with chance constraint, have been introduced. The literature has shown that adaptive strategies that incorporate both anticipation and real-time information are more efficient than a preplanned strategy in a stochastic environment. Most adaptive optimal-path algorithms focus on least-expected travel time. Recently, an adaptive path-finding strategy, named the stochastic on-time arrival (SOTA) algorithm, was proposed to maximize the travel time reliability for any given time threshold. However, the originally proposed SOTA algorithm is based on the classic successive approximation. Whether this algorithm converges within a finite number of successive approximation steps is an open question. In this study, the proposed discrete SOTA algorithm runs in an optimal polynomial time and thus improves the computational efficiency significantly. Numerical examples are provided, and future extensions are proposed.

Path finding strategies in scale-free networks.

We numerically investigate the scale-free network model of Barabási and Albert [A. L. Barabási and R. Albert, Science 286, 509 (1999)] through the use of various path finding strategies. In real networks, global network information is not accessible to each vertex, and the actual path connecting two vertices can sometimes be much longer than the shortest one. A generalized diameter depending on the actual path finding strategy is introduced, and a simple strategy, which utilizes only local information on the connectivity, is suggested and shown to yield small-world behavior: the diameter D of the network increases logarithmically with the network size N, the same as is found with global strategy. If paths are sought at random, D is equivalent to N(0.5) is found.

Defasciculation as a neuronal path finding strategy: Involvement of a specific glycoprotein

Leech sensory afferents change their growth behavior as they enter the CNS. Arriving from the periphery in fasciculated tracts, they abruptly defasciculate and expand into diffuse trees before reassembling into four distinct central tracts. In the organ-cultured germinal plate, growing sensory afferents were incubated with monovalent Fab fragments of the Lan3-2 antibody, which recognizes a 130 kd sensory neuron protein by its mannose epitope. Very low concentrations of Lan3-2 (6 and 12 nM) specifically inhibited the central defasciculation of sensory afferents, which then continued growing as a single tract. In contrast, monoclonal antibody Lan3–6, which binds to an internal sensory antigen, failed to yield the same effect. These observations suggest that this sensory neuron 130 kd surface glycoprotein participates in a developmentally significant heterophilic interaction specific for the CNS.