Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Multi-Robot Systems 1
Fully Distributed Scalable Smoothing and Mapping with Robust Multi-Robot Data AssociationIn this paper we focus on the multi-robot perception problem, and present an experimentally validated end-to-end multi-robot mapping framework, enabling individual robots in a team to see beyond their individual sensor horizons. The inference part of our system is the DDF-SAM algorithm, which provides a decentralized communication and inference scheme, but did not address the crucial issue of data association. One key contribution is a novel, RANSAC-based, approach for performing the between-robot data associations and initialization of relative frames of reference. We demonstrate this system with both data collected from real robot experiments, as well as in a large scale simulated experiment demonstrating the scalability of the proposed approach.
Collaborative 3D Localization of Robots from Relative Pose Measurements Using Gradient Descent on ManifoldsWe propose a distributed algorithm for estimating the full 3-D pose (position and orientation) of multiple autonomous vehicles with respect to a common reference frame when GPS is not available. This algorithm does not rely on the use of any maps, or the ability to recognize landmarks in the environment. Instead we assume that noisy measurements of the relative pose between pairs of robots are intermittently available. We utilize the additional information about each robot's pose provided by these measurements to improve over self-localization estimates. The proposed method is based on solving an optimization problem in an underlying product manifold (SO(3)x R<sup>3</sup>)<sup>n(k)</sup>. A provably correct explicit gradient descent law is provided. Unlike many previous approaches, the proposed algorithm is applicable to the 3-D case. The method is also capable of handling a fully dynamic scenario where the neighbor relationships are time-varying. Simulations show that the errors in the localization estimates obtained using this algorithm are significantly lower then what is achieved when robots estimate their pose without cooperation. Results from experiments with a pair of ground robots with vision-based sensors reinforce these findings.
Distributed Source Seeking by Cooperative Robots: All-To-All and Limited CommunicationsWe consider the problem of source seeking using a group of mobile robots equipped with sensors for concentration measurement (instead of the gradient). In our formulation, each robot maintains a gradient estimation, moves to the source by tracing the gradient, and all together keep a predefined formation in movement. We present two control algorithms with all-to-all and limited communications, respectively. The estimation error is taken into account to derive robust control algorithms. Comparing to existing methods, the proposed algorithm with limited communications is fully distributed, that is, each robot needs only to communicate with its one-hop neighbors and no across-hop message passing is required. Both theoretical analysis and numerical simulations are given to validate the effectiveness of our methods.
A Coordination Strategy for Multi-Robot Sampling of Dynamic ﬁ EldsA coordination mechanism to achieve the sampling task of static or dynamic fields by means of a system composed by multiple mobile robots is addressed in this paper. The problem is the estimation of a scalar field. To this aim in a probabilistic framework a solution is proposed that takes into account several constraints. The attention is focused on the vehicles motion generation and the developed strategy is designed for multiple, autonomous and distributed robots. It makes use of the Voronoi tessellation's properties to automatically distribute the vehicles' motion and of the Null-Space-Behavioral control to handle eventually conflicting motion tasks (as reaching a given point while avoiding obstacles). The algorithm can be tailored based on the communication and computational capabilities of the robots. A discussion and possible counterexamples of the applications of existing approaches are provided in the paper. Numerical simulations illustrate the results.
On Localization Uncertainty in an Autonomous InspectionThis paper presents a multi-goal path planning framework based on a self-organizing map algorithm and a model of the navigation describing evolution of the localization error. The framework combines finding a sequence of goals' visits with a goal-to-goal path planning considering localization uncertainty. The approach is able to deal with local properties of the environment such as expected visible landmarks usable for the navigation. The local properties affect the performance of the navigation, and therefore, the framework can take the full advantage of the local information together with the global sequence of the goals' visits to find a path improving the autonomous navigation. Experimental results in real outdoor and indoor environments indicate that the framework provides paths that effectively decreases the localization uncertainty; thus, increases the reliability of the autonomous goals' visits.
Probabilistic Spatial Mapping and Curve Tracking in Distributed Multi-Agent SystemsIn this paper we consider a probabilistic method for mapping a spatial process over a distributed multi-agent system and a coordinated level curve tracking algorithm for adaptive sampling. As opposed to assuming the independence of spatial features (e.g. an occupancy grid model), we adopt a novel model of spatial dependence based on the grid-structured Markov random field that exploits spatial structure to enhance mapping. The multi-agent Markov random field framework is utilized to distribute the model over the system and to decompose the problem of global inference into local belief propagation problems coupled with neighbor-wise inter-agent message passing. A Lyapunov stable control law for tracking level curves in the plane is derived and a method of gradient and Hessian estimation is presented for applying the control in a probabilistic map of the process. Simulation results over a real-world dataset with the goal of mapping a plume-like oceanographic process demonstrate the efficacy of the proposed algorithms. Scalability and complexity results suggest the feasibility of the approach in realistic multi-agent deployments.