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The goal of this work is to develop techniques that allow one or more robotic observers to operate with full autonomy while accomplishing the task of model building. The planning algorithm operates using certain simple but flexible models of the observer sensor and actuator abilities. We provide techniques that allow us to implement these sensor mo...
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... So, the C-space of a robot typifies the space of all configurations, which are processed by the robot. As the robot has n degrees of freedom, the C-space for this robot is ndimensional [2]. ...
In this paper, a modified derivation has been introduced to analyze the construction of C-space. The profit from using C-space is to make the process of path planning more safety and easier. After getting the C-space construction and map for two-link planar robot arm, which includes all the possible situations of collision between robot parts and obstacle(s), the A* algorithm, which is usually used to find a heuristic path on Cartesian W-space, has been used to find a heuristic path on C-space map. Several modifications are needed to apply the methodology for a manipulator with degrees of freedom more than two. The results of C-space map, which are derived by the modified analysis, prove the accuracy of the overall C-space mapping and construction, and then a successful and guaranteed path from a start to goal configuration has been obtained without any collision probability. The results had been achieved by (Matlab R2015a) software, which run on Intel (R) Core (TM) i3-3120M CPU.
... In early times, random algorithms were used to choose the candidate (Tovar et al., 2002;Lv et al., 2011). This method has great randomness, and the researchers must consider the filtering of the random point and environmental traversal. ...
Simultaneous environment exploration and map building of a mobile robot in an unknown environment are studied. Based on the real time data acquired from a laser sensor, a suitable environment exploration strategy with obstacle avoidance ability is proposed. To handle the problems existing in producing and evaluating candidates, feasible approaches are proposed. They can maximise the expected information gain and keep the environment information integrated, and ensure the environment exploration's continuum and complete traversal. The new evaluation method overcomes the drawbacks (attend to one criterion and lose another) of the traditional weighted average method. It can comprehensively evaluate the travelling cost, expected information gain and rotating angle to guarantee the quality of the optimal candidate. Furthermore, a topological map model is proposed which uses the nodes of the growing neural gas network as the topological network nodes. Through the growing characteristic of the GNG network, new topological nodes are added into the network to abstract and express the holistic knowledge of the surrounding environment and construct the environment map. Simulation results of two different indoor environments demonstrate its effectiveness and feasibility.
... In [Tovar et al., 2002] an algorithm for robot navigating is presented. The authors of [Tovar et al., 2002] analyzed the generation of motion strategies for building a map of an indoor environment using a mobile robot with range and video sensors. ...
... In [Tovar et al., 2002] an algorithm for robot navigating is presented. The authors of [Tovar et al., 2002] analyzed the generation of motion strategies for building a map of an indoor environment using a mobile robot with range and video sensors. They solved the problems of fundamental planning with information gain by sensors. ...
Landmark-based car navigation is a widely used technique for automotive and robot navigation. Wireless landmarks have some key features such as robustness and simple detection that make them suitable for automotive navigation. In this paper, a light-weight embedded algorithm for high speed car navigation in the roads with branches is presented which can be efficiently used in real-time automotive systems. We implemented the proposed algorithm on a real-time MIPS-based embedded system and analyzed its accuracy and efficiency in some real road maps, especially for high speed movements in real roads. Experimental results show that the proposed algorithm can be used for high speeds (up to about 360 km/h) with a very small error rate. Additionally, the experimental results show that the power consumption of the proposed system is suitable for built-in car applications.
... In [5] an algorithm for robot navigating is presented. The authors of [5] analyzed the generation of motion strategies for building a map of an indoor environment using a mobile robot with range and video sensors. ...
... In [5] an algorithm for robot navigating is presented. The authors of [5] analyzed the generation of motion strategies for building a map of an indoor environment using a mobile robot with range and video sensors. They solved the problems of fundamental planning with information gain by sensors. ...
Automatic car navigation systems have been used to guide humans or even automatically route them in roads with sufficient a security and correctness. Landmark based car navigation is a widely used technique for automotive and robot navigation. Wireless landmarks have some key features such as robustness and simple detection that make them suitable for automotive navigation. In this paper, a lightweight wireless landmark-based car navigation algorithm (LWCN) is presented which can be efficiently used in real-time systems. We implemented the LWCN on a real-time MIPS-based embedded system and analyzed its accuracy and efficiency in various states, especially for high speed movements in real roads. Experimental results show that LWCN can be used for high speed (up to 250 m/s) with very small error rate.
... Thus, the critical curves delineate noncritical regions in the plane such that, for a noncritical region R, the set of contact features remains constant for all (x, y) ∈ R. The set of critical curves is defined in terms of obstacle features (edges and vertices) and the length of the rod L. The catalog of critical curves is illustrated inFigure 2. More detailed explanations concerning their construction can be found in [31, 20]. This partition of the plane into noncritical regions induces a cylindrical decomposition on Q L . ...
... If κ and κ are adjacent, any configuration in κ can be connected to any configuration in κ by a free path whose projection onto the x-y plane crosses β transversally, with constant orientation in some neighborhood of the crossing point. The test for adjacency is a bit complicated, but it is formulated entirely in terms of the qualitative description of the cells [31, 20]. The complete graph for our example is illustrated inFigure 5. ...
This paper addresses the pursuit-evasion problem of maintaining surveillance by a pursuer of an evader in a world populated by polygonal obstacles. This requires the pursuer to plan collision-free motions that honor distance constraints imposed by sensor capabilities, while avoiding occlusion of the evader by any obstacle. We extend the three-dimensional cellular decomposition of Schwartz and Sharir to represent the four-dimensional conguration space of the pursuer-evader system, and derive necessary conditions for surveillance (equivalently, sucien t conditions for escape) in terms of this new representation. We then give a game theoretic formulation of the problem, and use this formulation to characterize optimal escape trajectories for the evader. We propose a shooting algorithm that nds these trajectories using the minimum principle. Finally, noting the similarities between this surveillance problem and the problem of cooperative manipulation by two robots, we present several cooperation strategies that maximize system performance for cooperative motions.
... Pairs of line segments are matched and the translation is computed by least square minimization, using an initial estimate for the displacement provided by odometry. The technique proposed in [33] refines the alignment of scans collected by multiple robots using the partial Hausdorff distance to compute the best transformation between a new scan and a point map of the previously explored region. This method also requires an initial estimate of the pose of the scans. ...
Most map building methods employed by mobile robots are based on the assumption that an estimate of robot poses can be obtained from odometry readings or from observing landmarks or other robots. In this paper we propose methods to build a global geometric map by integrating scans collected by laser range scanners without using any knowledge about the robots' poses. We consider scans that are collections of line segments. Our approach increases the flexibility in data collection, since robots do not need to see each other during mapping, and data can be collected by multiple robots or a single robot in one or multiple sessions. Experimental results show the effectiveness of our approach in different types of indoor environments
... The result is a robot that autonomously explores its environment, optimizing the exploration at each stage and merging newly acquired sensor data into its existing map. Preliminary versions of this work appeared in [35] and [36]. ...
... For instance, a position very close to a free edge (with great chance of discovering new space) must be discarded if the robot has no information to integrate this new area to the explored space. Similar forms of this utility function have been presented in [35,36]. The main difference between our previously proposed utility functions and the one presented in this paper is that now our utility function can be used to measure the utility of a single robot location or a path associated with a sequence composed by several sensing locations. ...
In this paper, we present techniques that allow one or multiple mobile robots to efficiently explore and model their environment. While much existing research in the area of Simultaneous Localization and Mapping (SLAM) focuses on issues related to uncertainty in sensor data, our work focuses on the problem of planning optimal exploration strategies. We develop a utility function that measures the quality of proposed sensing locations, give a randomized algorithm for selecting an optimal next sensing location, and provide methods for extracting features from sensor data and merging these into an incrementally constructed map. We also provide an efficient algorithm driven by our utility function. This algorithm is able to explore several steps ahead without incurring too high a computational cost. We have compared that exploration strategy with a totally greedy algorithm that optimizes our utility function with a one-step-look ahead. The planning algorithms which have been developed operate using simple but flexible models of the robot sensors and actuator abilities. Techniques that allow implementation of these sensor models on top of the capabilities of actual sensors have been provided. All of the proposed algorithms have been implemented either on real robots (for the case of individual robots) or in simulation (for the case of multiple robots), and experimental results are given.
... Of course, such an approach requires significant memory to store the results of the preprocessing stage, but in our experiments (using a typical PC), memory limitations have not been a problem, even in maps with several thousands of vertices. We assume that a polygonal map of the environment is provided, such as described in [46]. The algorithm Preprocessing is shown below, and its steps are as follows. ...
This paper deals with the surveillance problem of computing the motions of one or more robot observers in order to maintain visibility of one or several moving targets. The targets are assumed to move unpredictably, and the distribution of obstacles in the workspace is assumed to be known in advance. Our algorithm computes a motion strategy by maximizing the shortest distance to escape—the shortest distance the target must move to escape an observer's visibility region. Since this optimization problem is intractable, we use randomized methods to generate candidate surveillance paths for the observers. We have implemented our algorithms, and we provide experimental results using real mobile robots for the single target case, and simulation results for the case of two targets-two observers.
... Of course, such an approach requires significant memory to store the results of the preprocessing stage, but in our experiments (using a typical PC), memory limitations have not been a problem, even in maps with several thousands of ver- tices. We assume that a polygonal map of the environment is provided, such as described in (Tovar et al., 2002). The algorithm PREPROCESSING is shown below, and its steps are as follows. ...
In this paper we address the problem of generating a motion strategy to find an object in a known 3-D environment as quickly as possible on average. We use a sampling scheme that generates an initial set of sensing locations for the robot and then we propose a convex cover algorithm based on this sampling. Our algorithm tries to reduce the cardinality of the resulting set and has the main advantage of scaling well with the dimensionality of the environment. We then use the resulting convex covering to generate a graph that captures the connectivity of the workspace. Finally, we search this graph to generate trajectories that try to minimize the expected value of the time to find the object.
... In this problem, it is often desirable to minimize sensing overlap so as not to cover the same region more than once. For all the previous problems, there are characteristics that can be added to make them more general, for example, kinematic constraints on movement [23], position uncertainty [24, 25, 26], limited sensors [6, 27], etc. The motion strategies that consider multiple robots [28, 29, 30, 31] are generally harder to generate but usually yield better performance (compared to a single robot). ...
This work addresses the problem of generating a motion strategy for solving a visibility-based task with a mobile robot equipped with sensors. In particular, the problem is to find a static object -- modeled with a probability density function -- in a completely known environment. Given a starting position for the robot, the problem is to generate a trajectory that minimizes the expected value of the time to see the object for the first time along that route. The problem is shown to be NP-hard and efficient algorithms that perform well in practice are presented. Several versions of this problem are addressed. The first one is the case of a point robot moving in a polygonal workspace. The robot is restricted to sense the world only at predefined locations arranged in a graph. The proposed solution uses an utility function to drive a greedy search algorithm in a reduced space, able to explore several steps ahead without incurring too high a computational cost. This approach is also extended to coordinate a team of robots searching in parallel to further reduce the search time without an increase in the computational complexity of the algorithm. The second version assumes the robot is able to sense the environment continuously. In this case, the proposed approach partitions the polygonal workspace into regions separated by critical curves. Then, the calculus of variations and numerical integration are applied to compute locally optimal trajectories within each individual region. Finally, the resulting sub-paths are concatenated to generate a complete path. In the final version, the robot is no longer a point but a mobile manipulator moving in a 3-D environment. The proposed solution builds on previous results and also introduces a sample-based convex cover to estimate the size and shape of visibility regions in 3-D. The resulting convex regions are exploited to generate trajectories that compromise between moving the base and moving the robotic arm.
