Path planning for permutation-invariant multirobot formations

In many multirobot applications, the specific assignment of goal configurations to robots is less important than the overall behavior of the robot formation. In such cases, it is convenient to define a permutation-invariant multirobot formation as a set of robot configurations, without assigning specific configurations to specific robots. For the case of robots that translate in the plane, we can represent such a formation by the coefficients of a complex polynomial whose roots represent the robot configurations. Since these coefficients are invariant with respect to permutation of the roots of the polynomial, they provide an effective representation for permutation-invariant formations. In this paper, we extend this idea to build a full representation of a permutation-invariant formation space. We describe the properties of the representation, and show how it can be used to construct collision-free paths for permutation-invariant formations.     

A spatial orthogonal allocation and heterogeneous cultural hybrid algorithm for multirobot exploration mission planning

A spatial orthogonal allocation method is devised for multirobot tasks allocation.A 3D space model is adopted to describe exploration mission;meanwhile spatial orthogonal tentative technology is utilized to update the attractor position for load balance.Heterogeneous interactive cultural hybrid architecture is proposed to solve a robot route planning problem;it utilizes good-point-set to initialize population spaces,redefine novel evolution model and particle evolution ability,and introduce near-neighbor lo...     

Scalable and exact sampling method for probabilistic generative graph models

Interest in modeling complex networks has fueled the development of multiple probabilistic generative graph models (PGGMs). PGGMs are statistical methods that model the network distribution and match common characteristics of real world networks. Recently, scalable sampling algorithms for well known PGGMs, made the analysis of large-scale, sparse networks feasible for the first time. However, it has been demonstrated that these scalable sampling algorithms do not sample from the original underlying distribution, and sometimes produce very unlikely graphs. To address this, we extend the algorithm proposed in Moreno et al. (in: IEEE 14th international conference on data mining, pp 440–449, 2014) for a single model and develop a general solution for a broad class of PGGMs. Our approach exploits the fact that PGGMs are typically parameterized by a small set of unique probability values—this enables fast generation via independent sampling of groups of edges with the same probability value. By sampling within groups, we remove bias due to conditional sampling and probability reallocation. We show that our grouped sampling methods are both provably correct and efficient. Our new algorithm reduces time complexity by avoiding the expensive rejection sampling step previously necessary, and we demonstrate its generality, by outlining implementations for six different PGGMs. We conduct theoretical analysis and empirical evaluation to demonstrate the strengths of our algorithms. We conclude by sampling a network with over a billion edges in 95 s on a single processor.     

Narrow passage sampling for probabilistic roadmap planning

Probabilistic roadmap (PRM) planners have been successful in path planning of robots with many degrees of freedom, but sampling narrow passages in a robot's configuration space remains a challenge for PRM planners. This paper presents a hybrid sampling strategy in the PRM framework for finding paths through narrow passages. A key ingredient of the new strategy is the bridge test, which reduces sample density in many unimportant parts of a configuration space, resulting in increased sample density in narrow passages. The bridge test can be implemented efficiently in high-dimensional configuration spaces using only simple tests of local geometry. The strengths of the bridge test and uniform sampling complement each other naturally. The two sampling strategies are combined to construct the hybrid sampling strategy for our planner. We implemented the planner and tested it on rigid and articulated robots in 2-D and 3-D environments. Experiments show that the hybrid sampling strategy enables relatively small roadmaps to reliably capture the connectivity of configuration spaces with difficult narrow passages.     

A flooding algorithm for multirobot exploration

In this paper, we present a multirobot exploration algorithm that aims at reducing the exploration time and to minimize the overall traverse distance of the robots by coordinating the movement of the robots performing the exploration. Modeling the environment as a tree, we consider a coordination model that restricts the number of robots allowed to traverse an edge and to enter a vertex during each step. This coordination is achieved in a decentralized manner by the robots using a set of active landmarks that are dropped by them at explored vertices. We mathematically analyze the algorithm on trees, obtaining its main properties and specifying its bounds on the exploration time. We also define three metrics of performance for multirobot algorithms. We simulate and compare the performance of this new algorithm with those of our multirobot depth first search (MR-DFS) approach presented in our recent paper and classic single-robot DFS.     

Assistance networks for dynamic multirobot tasks

In this paper, we consider dynamic multirobot tasks that can be done by any of the robots, but only with the assistance of any other robot. We propose a novel approach based on the concept of ‘assistance networks’ with two complementary aspects, namely assistant finding and network topology update. Each robot, encountering a new task, seeks an assisting robot among its immediate neighbors in the assistance network in a decentralized manner. The network topology is defined based on pairwise stability via payoff functions that consider general task-related guidelines. As such, the number of potential assisting robots can be ensured a priori depending on tasks’ requirements. As robots move around, the topology is updated via pairwise games. If the games are conducted by a network coordinator, each game is shown to result in a pairwise stable network. A series of simulation and experimental results in a variety of different scenarios demonstrate that the robots are able to get assistance or give assistance flexibly.     

Quasi-plus sampling edge correction for spatial point patterns

A widely applicable edge correction method for estimating summary statistics of a spatial point pattern is proposed. We reconstruct point patterns in a larger region containing the sampling window by matching sampled and simulated kth nearest neighbour distance distributions of the given pattern and then apply plus sampling. Simulation studies show that this approach, called quasi-plus sampling, gives estimates with smaller root mean squared errors than estimates obtained by using other popular edge corrections. We apply the proposed approach to real data and yield an estimate of a summary statistic that is more plausible than that obtained by a popular edge correction.     

A spatial sampling criterion for sonar obstacle detection

A spatial sampling criterion for sonar systems that allows all obstacles within a given radius from the sensor to be detected is described. The environment considered is a two-dimensional floor plan that is extended into the third dimension, in which the scanning is performed in the horizontal plane. In this environment, edgelike reflectors, such as edges of doors or doorways, and oblique surfaces are the most difficult to detect. By considering the physics of sound propagation, the sonar scanning density required to detect these objects is determined. An experimental verification is included. The limitations of detecting objects with sonar in a more general environment are discussed. These results can be used to determine the necessary spacing in a transducer ring array and the maximum step size that a mobile robot can translate without danger of collision     

Two-handed spatial interface tools for neurosurgical planning

A computer-based neurosurgical planning system lets neurosurgeons easily manipulate 3D data using their everyday skills from handling tools with two hands. It has been tested in actual surgical procedures     

Potential functions based sampling heuristic for optimal path planning

Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.     

Probabilistic Road Map sampling strategies for multi-robot motion planning

This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.

First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.

Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.     

Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products

The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate- or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1 km² spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.     

Isoparametric line sampling for the inspection planning of sculptured surfaces

This paper presents a new approach for the sampling of sculptured surfaces using continuous scanning coordinate measuring machine probe heads. This approach is based on scanning isoparametric lines on the sculptured surface. This paper addresses the issue of isoparametric line sampling. The problem is to determine the locations of the sample lines extracted from the surface CAD model. These lines are fitted to construct a substitute geometry of the surface. The accuracy of the sampling plan is characterised by the maximum deviation between the substitute geometry and the surface CAD model. Two new algorithms for sampling isoparametric lines are proposed. These are, automatic sampling, and surface curvature-based sampling. Both algorithms are constrained by the maximum number of scan lines, and the step over distance between subsequent sample lines. Automatic sampling uses the deviations between the substitute geometry, and the surface CAD model to determine the sample locations. Curvature change-based sampling uses the change in surface curvature to determine the sampling line locations. Both algorithms are compared to uniform iso-planar sampling. The algorithms, their implementation, and a case study are presented in this paper.     

A hierarchical procedure for multi-skilled sales force spatial planning

Marketing and sales comprise an important part of a company's core processes. We study this core component in a very large corporation in the process industry. This company has thousands of employees worldwide involved in sales and marketing activities. The optimal utilization of this large workforce is rigorously analyzed in this paper. We develop a hierarchical procedure including a sequence of mathematical models to address workload determination and sales force planning within the company. In particular, our sequence of models determines: (a) the optimal number of visits to each of the existing customers with the goal of retaining them; (b) the optimal number of employees with different kinds of skills for satisfying the workload obtained using model (a) including the time to hunt for new customers; and (c) the optimal way of providing the workforce calculated in part (b) (i.e., optimal strategies for hiring, relocation or dismissal of current workforce). Moreover, we face some nonlinear constrained integer programming problems in our sequence of mathematical models which are not easily solved by commercial software. We develop our own efficient methods for solving such problems optimally.     

Path planning for grasping operations using an adaptive PCA-based sampling method

The planning of collision-free paths for a hand-arm robotic system is a difficult issue due to the large number of degrees of freedom involved and the cluttered environment usually encountered near grasping configurations. To cope with this problem, this paper presents a novel importance sampling method based on the use of principal component analysis (PCA) to enlarge the probability of finding collision-free samples in these difficult regions of the configuration space with low clearance. By using collision-free samples near the goal, PCA is periodically applied in order to obtain a sampling volume near the goal that better covers the free space, improving the efficiency of sampling-based path planning methods. The approach has been tested with success on a hand-arm robotic system composed of a four-finger anthropomorphic mechanical hand (17 joints with 13 independent degrees of freedom) and an industrial robot (6 independent degrees of freedom).     

Alternate spatial sampling approaches for ecosystem structure inventory using spaceborne lidar

The application of spaceborne lidar data to mapping of ecosystem structure is currently limited by the relatively small fraction of the earth's surface sampled by these sensors; this limitation is likely to remain over the next generation of lidar missions. Currently planned lidar missions will collect transects of data with contiguous observations along each transect; transects will be spread over swaths of multiple kilometers, a sampling pattern that results in high sampling density along track and low sampling density across track. In this work we demonstrate the advantages of a hybrid spatial sampling approach that combines a single conventional transect with a systematic grid of observations. We compare this hybrid approach to traditional lidar sampling that distributes the same number of observations into five transects. We demonstrate that a hybrid sampling approach achieves benchmarks for the spatial distribution of observations in approximately 1/3 of the time required for transect sampling and results in estimates of ecosystem height that have half the uncertainty as those from transect sampling. This type of approach is made possible by a suite of technologies, known together as Electronically Steerable Flash Lidar. A spaceborne sensor with the flexibility of this technology would produce estimates of ecosystem structure that are more reliable and spatially complete than a similar number of observations collected in transects and should be considered for future lidar remote sensing missions.     

COBOS: Cooperative backoff adaptive scheme for multirobot task allocation

In this paper, the cooperative backoff adaptive scheme (COBOS) is proposed for task allocation amongst a team of heterogeneous robots. The COBOS operates in regions with limited communication ranges, and is robust against robot malfunctions and uncertain task specifications, with each task potentially requiring multiple robots. The portability of tasks across teams (or when team demography changes) is improved by specifying tasks using basis tasks in a matrix framework. The adaptive feature of COBOS further increases the flexibility of robot teams, allowing robots to adjust their actions based on past experience. In addition, we study the properties of COBOS: operation domain; communication requirements; computational complexity; and solution quality; and compare the scheme with other task-allocation mechanisms. Realistic simulations are carried out to verify the effectiveness of the proposed scheme.     

Sampling-based motion planning of manipulator with goal-oriented sampling

Intelligent Service Robotics - A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a...