Concave Obstacles Research Articles

LTA*: Local tangent based A* for optimal path planning

Optimal path planning on non-convex maps is challenging: sampling-based algorithms (such as RRT) do not provide optimal solution in finite time; approaches based on visibility graphs are computationally expensive, while reduced visibility graphs (e.g., tangent graph) fail on such maps. We leverage a well-established, and surprisingly less utilized in path planning, geometrical property of convex decompositions i.e. a concave shape can be decomposed into multiple convex shapes. We propose a novel local tangent based approach, inspired by such convex decompositions, to path planning in non-convex maps. Although our local tangent approach is inspired by geometric convex decompositions, it does not require complex decomposition process. Our second contribution is an efficient corner detection method which reasons on binary pixel occupancy maps. Combined with our novel local tangent approach, which intelligently selects nodes from these corners, we modify the standard A* algorithm by feeding these nodes to its open list. With our local tangent approach, only small number of selected corners are fed to A* open list which keeps its size small even for larger maps, resulting in lower convergence time. We formally prove the optimality of our solution. Simulation on our own maps and public dataset (MAPF http://mapf.info/ ) as well as real-world experiments show that our proposed LTA* algorithm gives better convergence time and shorter path length in environments with both convex and concave obstacles.

Improved Q‐Learning Method for Multirobot Formation and Path Planning with Concave Obstacles

Aiming at the formation and path planning of multirobot systems in an unknown environment, a path planning method for multirobot formation based on improved Q‐learning is proposed. Based on the leader‐following approach, the leader robot uses an improved Q‐learning algorithm to plan the path and the follower robot achieves a tracking strategy of gravitational potential field (GPF) by designing a cost function to select actions. Specifically, to improve the Q‐learning, Q‐value is initialized by environmental guidance of the target’s GPF. Then, the virtual obstacle‐filling avoidance strategy is presented to fill non‐obstacles which is judged to tend to concave obstacles with virtual obstacles. Besides, the simulated annealing (SA) algorithm whose controlling temperature is adjusted in real time according to the learning situation of the Q‐learning is applied to improve the action selection strategy. The experimental results show that the improved Q‐learning algorithm reduces the convergence time by 89.9% and the number of convergence rounds by 63.4% compared with the traditional algorithm. With the help of the method, multiple robots have a clear division of labor and quickly plan a globally optimized formation path in a completely unknown environment.

One Way to Fill All the Concave Region in Grid-Based Map

SUMMARYThe search space of the path planning problem can greatly affect the running time and memory consumption, for example, the concave obstacle in grid-based map usually leads to the invalid search space. In this paper, the filling container algorithm is proposed to alleviate the concave area problem in 2D map space, which is inspired from the scenario of pouring water into a cup. With this method, concave areas can be largely excluded by scanning the map repeatedly. And the effectiveness has been proved in our experiments.

Probabilistic Approach to Robot Motion Planning in Dynamic Environments

Four major approaches to robot motion planning in dynamic environments are discussed: probabilistic robot, probabilistic collision state (PCS), partially closed-loop receding horizon control (PCLRHC) and gross hidden Markov model (GHMM). A comparison of three mapping techniques, Kalman filter, expectation and maximization algorithm and Markov model, is presented. The PCS method is the probabilistic extension of inevitable collision state, which is found to be the safest motion planning method. The concept of open-loop and partially closed-loop receding horizon control (OLRHC and PCLRHC) is compared critically, and the algorithms are benchmarked. GHMM is the best suited method for environments with limited space and dynamic environment due to human interactions. GHMM parameters and structure are evaluated using an incremental “learn-and-predict” approach. For exploring GHMM, we simulated a cafeteria with eight tables to be served by a robot, considering three different arrangements of tables along with convex and concave obstacles, and obtained the path length and time taken for a Hamiltonian path. During the simulation, it was observed that for a given static or dynamic environment, the concavity of the obstacles is what makes the scenario a complex one.

Reinforcement learning path planning algorithm based on obstacle area expansion strategy

We improve the traditional Q($$ \lambda $$)-learning algorithm by adding the obstacle area expansion strategy. The new algorithm is named OAE-Q($$ \lambda $$)-learning and applied to the path planning in the complex environment. The contributions of OAE-Q($$ \lambda $$)-learning are as follows: (1) It expands the concave obstacle area in the environment to avoid repeated invalid actions when the agent falls into the obstacle area. (2) It removes the extended obstacle area, which reduces the learning state space and accelerates the convergence speed of the algorithm. Extensive experimental results validate the effectiveness and feasibility of OAE-Q($$ \lambda $$)-learning on the path planning in complex environments.

Artificial Intelligence Based A Optimization Routing in Mobile Ad Hoc Networks

A Mobile Ad Hoc Network (MANET) is a hotchpotch of nodes with mobility feature, the established network utilization is dynamically outlined based on temporary architecture. In MANETs, the challenging and vital role is played by the routing protocols performance factors under different condition and environments. The routing protocols are liable to handle many nodes with limited resources. There exits many routing protocols in MANETs, one of the main key note that has to be considered in designing a routing protocol is to observe that the designed routing protocol is having an proportionate effect on network performance. The existence of obstacles may lead to many geographical routing problems like excess consumption of power and congestion of data. The aim of this paper is to take the assistance of A* algorithm that finds the walk-able path avoiding the concave obstacle in the path relaying on the gaming-theory model[29]. This algorithm decreases the delays in packet transmission and in turn increases the success rate of transmission. We take into consideration path length, penalty for node availability as probability of forwarding criteria and processes effective packet transmission. The simulated results analyse the performance of our protocol over other conventional algorithms based on congestion cost, path length, node availability penalty, delay, packet loss, throughput.

A novel edge gradient algorithm for multiple mobile robots cooperative mapping in unknown environment

This article presented a cooperative mapping technique using a novel edge gradient algorithm for multiple mobile robots. The proposed edge gradient algorithm can be divided into four behaviors such as adjusting the movement direction, evaluating the safety of motion behavior, following behavior, and obstacle information exchange, which can effectively prevent multiple mobile robots falling into concave obstacle areas. Meanwhile, a visual field factor is constructed based on biological principles so that the mobile robots can have a larger field of view when moving away from obstacles. Also, the visual field factor will be narrowed due to the obstruction of the obstacle when approaching an obstacle and the obtained map-building data are more accurate. Finally, three sets of simulation and experimental results demonstrate the performance superiority of the presented algorithm.

Avoidance of Convex and Concave Obstacles With Convergence Ensured Through Contraction

This letter presents a closed-form approach to obstacle avoidance for multiple moving convex and star-shaped concave obstacles. The method takes inspiration in harmonic-potential fields. It inherits the convergence properties of harmonic potentials. We prove impenetrability of the obstacles hull and asymptotic stability at a final goal location, using contraction theory. We validate the approach in a simulated co-worker industrial environment, with one KUKA arm engaged in a pick and place grocery task, avoiding in real-time humans moving in its vicinity and in simulation to drive wheel-chair robot in the presence of moving obstacles.

Same Fuzzy Logic Controller for Two-Wheeled Mobile Robot Navigation in Strange Environments

For any mobile device, the ability to navigate smoothly in its environment is of paramount importance, which justifies researchers’ continuous work on designing new techniques to reach this goal. In this work, we briefly present a description of a hard work on designing a Same Fuzzy Logic Controller (S.F.L.C.) of the two reactive behaviors of the mobile robot, namely, “go to goal obstacle avoidance” and “wall following,” in order to solve its navigation problems. This new technique allows an optimal motion planning in terms of path length and travelling time; it is meant to avoid collisions with convex and concave obstacles and to achieve the shortest path followed by the mobile robot. The efficiency of employing the proposed navigational controller is validated when compared to the results from other intelligent approaches; its qualities make of it an efficient alternative method for solving the path planning problem of the mobile robot.

Cooperative mapping of unknown environments by multiple heterogeneous mobile robots with limited sensing

While recent advancements in using sophisticated onboard sensors on robotic platforms have made it possible to consign various tasks like exploration, mapping, and flocking to teams of mobile robots, some issues like handling extensive amount of data, high dependency on sensors’ performance, and high expenses emerge. In this paper, the problem of mapping unknown environments by a team of heterogeneous mobile robots with limited and inexpensive sensing abilities is addressed. The concepts of Information Space and sensor models have been employed to plan the motions of robots with limited sensory data in order to accomplish the common goal of mapping the entire workspace as complete as possible. Also, a cooperation architecture is proposed to fuse and interrelate the dissimilar data obtained by individual heterogeneous robots and allocate various exploratory tasks to each of them in order to complete the map. The algorithm works with various limited sensing models, such as depth-limited boundary distance sensor, quadridirectional depth sensor, depth-limited gap sensor, and depth-limited radially-bounded depth senor. Based on each sensor model, the best moving strategy is introduced to maximize the workspace coverage for each robot. The proposed algorithm, which yields a geometric map of the environment, is implemented in diverse simulated problems both with and without sensing noises, and the results and comparisons with a recent related work show that it is able to reliably construct maps of simply-connected and multiply-connected environments with convex and concave obstacles. In the presence of noises, the produced maps had about 12.4% false positive and 3.3% false negative errors on average. Also, some sensitivity analyses are done on the effects of workspace size and number of robots on the mapping time.

Improving mobile robot navigation by combining fuzzy reasoning and virtual obstacle algorithm

In this paper, a new approach is developed for contributing in solving the problem of autonomous mobile robot navigation in unknown environments. This approach is built upon combining fuzzy reasoning and virtual obstacle algorithm to overcome the local minimum problem encountered in presence of concave obstacles by efficiently coordinating priorities between multiple reactive behaviors such as goal reaching, obstacles avoiding, wall following and emergency situations preventing. To achieve this objective, an array of ultrasonic sensors is mounted on the mobile robot providing the distance information between the robot and obstacles. This distance information is used by the virtual obstacle algorithm to calculate some sub-goals for determining the good motion direction to avoid robot trap in local region (emergency situation), since the fuzzy reasoning is used for behavior control of the mobile robot. All the reactive behaviors are mapped into one universe of discourse to guarantee a smooth transition between them especially when the robot moves through closely spaced obstacles. In this manner, the robot oscillations are significantly reduced. Some simulation results are presented to show the ability of the developed approach in performing successfully in complex and uncertain environments.

Fuzzy Logic Based Navigation for Multiple Mobile Robots in Indoor Environments

The work presented in this paper deals with a navigation problem for multiple mobile robot system in unknown indoor environments. The environment is completely unknown for all the robots and the surrounding information should be detected by the proximity sensors installed on the robots’ bodies. In order to guide all the robots to move along collision-free paths and reach the goal positions, a navigation method based on the combination of a set of primary strategies has been developed. The indoor environments usually contain convex and concave obstacles. In this work, a danger judgment strategy in accordance with the sensors’ data is used for avoiding small convex obstacles or moving objects which include both dynamic obstacles and other robots. For big convex obstacles or concave ones, a wall following strategy is designed for dealing with these special situations. In this paper, a state memorizing strategy is also proposed for the “infinite repetition” or “dead cycle” situations. Finally, when there is no collision risk, the robots will be guided towards the targets according to a target positioning strategy. Most of these strategies are achieved by the means of fuzzy logic controllers and uniformly applied for every robot. The simulation experiments verified that the proposed method has a positive effectiveness for the navigation problem.

Inviscid Uniform Shear Flow past a Smooth Concave Body

Uniform shear flow of an incompressible inviscid fluid past a two-dimensional smooth concave body is studied; a stream function for resulting flow is obtained. Results for the same flow past a circular cylinder or a circular arc or a kidney-shaped body are presented as special cases of the main result. Also, a stream function for resulting flow around the same body is presented for an oncoming flow which is the combination of a uniform stream and a uniform shear flow. Possible fields of applications of this study include water flows past river islands, the shapes of which deviate from circular or elliptical shape and have a concave region, or past circular arc-shaped river islands and air flows past concave or circular arc-shaped obstacles near the ground.

Arclength numerical continuation in free-boundary flow

Using Helmholtz’s wake model, we reduce the study of the free boundary flow past an obstacle consisting of an arc of circle to the investigation of a Hammerstein nonlinear integral equation depending on a real parameter λ. The papers dedicated to this problem investigated the case λ>0 which corresponds to a convex obstacle with respect to the incoming fluid. Herein, we apply for the first time in the literature the arclength continuation method for the case λ<0 corresponding to a concave arc of a circle. For λ>0 the existence and the uniqueness of the solution was demonstrated, but for λ<0, depending on its value compared to the one of a turning point, the integral equation has either no solution or two distinct solutions corresponding to two different obstacles. We numerically calculate the free lines, the velocity field and the stream lines. A diagram of the drag coefficient versus the arc measure for both convex and concave obstacles suggests us to draw some conclusions concerning the optimization of the blades of a vertical axis (Savonius) wind turbine.

Dynamic formation control for autonomous underwater vehicles

Path planning and formation structure forming are two of the most important problems for autonomous underwater vehicles (AUVs) to collaborate with each other. In this work, a dynamic formation model was proposed, in which several algorithms were developed for the complex underwater environment. Dimension changeable particle swarm algorithm was used to find an optimized path by dynamically adjusting the number and the distribution of the path nodes. Position relationship based obstacle avoidance algorithm was designed to detour along the edges of obstacles. Virtual potential point based formation-keeping algorithm was employed by incorporating dynamic strategies which were decided by the current states of the formation. The virtual potential point was used to keep the formation structure when the AUV or the formation was deviated. Simulation results show that an optimal path can be dynamically planned with fewer path nodes and smaller fitness, even with a concave obstacle. It has been also proven that different formation-keeping strategies can be adaptively selected and the formation can change its structure in a narrow area and restore back after passing the obstacle.

The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control

Compared with the space fixed feature of traditional wireless sensor network (WSN), mobile WSN has better robustness and adaptability in unknown environment, so that it is always applied in the research of target tracking. In order to reach the target, the nodes group should find a self-adaptive method to avoid the obstacles together in their moving directions. Previous methods, which were based on flocking control model, realized the strategy of obstacle avoidance by means of potential field. However, these may sometimes lead the nodes group to fall into a restricted area like a trap and never get out of it. Based on traditional flocking control model, this paper introduced a new cooperative obstacle avoidance model combined with improved SA obstacle avoidance algorithm. It defined the tangent line of the intersection of node's velocity line and the edge of obstacle as the steering direction. Furthermore, the cooperative obstacle avoidance model was also improved in avoiding complex obstacles. When nodes group encounters mobile obstacles, nodes will predict movement path based on the spatial location and velocity of obstacle. And when nodes group enters concave obstacles, nodes will temporarily ignore the gravity of the target and search path along the edge of the concave obstacles. Simulation results showed that cooperative obstacle avoidance model has significant improvement on average speed and time efficiency in avoiding obstacle compared with the traditional flocking control model. It is more suitable for obstacle avoidance in complex environment.

Real-time Collision Avoidance for Pedestrian and Bicyclist Simulation: A Smooth and Predictive Approach

This article introduces a new collision avoidance model enabling the design of efficient realistic virtual pedestrian and cyclist behaviors. It is a force-based model using collision prediction with dynamic time-windows to predict future potential collisions with obstacles and other individuals. It introduces a new type of force called sliding force to allow a smooth avoidance of potential collisions while enabling the pedestrian to continue to progress towards its goal. Unlike most existing models, our forces are not scaled according to the distance to the obstacle but depending on the estimate of the collision time with this obstacle. This inherently integrates obstacles’ velocity. This greatly reduces the compu- tational complexity of the model while ensuring a smooth avoidance. This model is oscillation-free except for concave obstacles. It enables the reproduction of inherent emergent properties of real crowds such as spontaneous organizations of pedestrians into lane lines, etc. This model is computationally efficient and designed for real time simulation of large crowds.

A particle swarm approach for flight path optimization in a constrained environment

Potentials of Particle Swarm Optimization techniques in the field of flight path generation are investigated in this paper. The objective is the development of a particle swarm-based procedure performing off-line two-dimensional flight path optimizations compliant with operational constraints. Assuming a typical surveillance mission, such constraints are defined in terms of “target” and “no-fly” zones, desired flight time on targets, fixed way-points and landing areas. Flight path is described through a discrete number of suitable flight parameters varying within defined ranges, each particle of the swarm being represented by a numerical combination of these parameters. A novel obstacle avoidance model based on the evaluation of a so-called area function has been introduced.All trajectories, made up of circular arcs and straight lines, start from a specified point with fixed velocity vector, ending within a selected landing area. Both single-objective and multi-objective optimization procedures have been formulated and implemented. The former minimize the total flight path length; the latter also try to maximize the trajectory length covered over specified target areas. Sensitivity studies with increasing problem complexity have been performed changing both number and positions of “target” and “no-fly” zones in order to assess the effectiveness as well as the robustness of the proposed approach. Algorithm capability in finding the optimum path through non-circular and concave obstacles has also been tested. Finally computational time monitoring allowed making a preliminary assessment of Particle Swarm Optimization suitability for practical applications.

Autonomously Implemented Versatile Path Planning for Mobile Robots Based on Cellular Automata and Ant Colony

Abstract A path planning method for mobile robots based on two dimensional cellular automata is proposed. The method can be applied for environments with both concave and convex obstacles. It is also appropriate for multi-robot problems as well as dynamic environments. In order to develop the planning method, environment of the robot is decomposed to a rectangular grid and the automata is defined with four states including Robot cell, Free cell, Goal cell and Obstacle cell. Evolution rules of automata are proposed in order to direct the robot toward its goal. CA based path planner method is afterwards modified by a colony technique to be applicable for concave obstacles. Then a layered architecture is proposed to autonomously implement the planning algorithm. The architecture employs an abstraction approach which makes the complexity manageable. An important feature of the architecture is internal artifacts that have some beliefs about the world. Most actions of the robot are planned and performed with re...

Single Frequency Inverse Obstacle Scattering: A Sparsity Constrained Linear Sampling Method Approach

The linear sampling method (LSM) offers a qualitative image reconstruction approach, which is known as a viable alternative for obstacle support identification to the well-studied filtered backprojection (FBP), which depends on a linearized forward scattering model. Of practical interest is the imaging of obstacles from sparse aperture far-field data under a fixed single frequency mode of operation. Under this scenario, the Tikhonov regularization typically applied to LSM produces poor images that fail to capture the obstacle boundary. In this paper, we employ an alternative regularization strategy based on constraining the sparsity of the solution's spatial gradient. Two regularization approaches based on the spatial gradient are developed. A numerical comparison to the FBP demonstrates that the new method's ability to account for aspect-dependent scattering permits more accurate reconstruction of concave obstacles, whereas a comparison to Tikhonov-regularized LSM demonstrates that the proposed approach significantly improves obstacle recovery with sparse-aperture data.