COACHES Cooperative Autonomous Robots in Complex and Human Populated Environments

Public spaces in large cities are increasingly becoming complex and unwelcoming environments. Public spaces progressively become more hostile and unpleasant to use because of the overcrowding and complex information in signboards. It is in the interest of cities to make their public spaces easier to use, friendlier to visitors and safer to increasing elderly population and to citizens with disabilities. Meanwhile, we observe, in the last decade a tremendous progress in the development of robots in dynamic, complex and uncertain environments. The new challenge for the near future is to deploy a network of robots in public spaces to accomplish services that can help humans. Inspired by the aforementioned challenges, COACHES project addresses fundamental issues related to the design of a robust system of self-directed autonomous robots with high-level skills of environment modelling and scene understanding, distributed autonomous decision-making, short-term interacting with humans and robust and safe navigation in overcrowding spaces. To this end, COACHES will provide an integrated solution to new challenges on: (1) a knowledge-based representation of the environment, (2) human activities and needs estimation using Markov and Bayesian techniques, (3) distributed decision-making under uncertainty to collectively plan activities of assistance, guidance and delivery tasks using Decentralized Partially Observable Markov Decision Processes with efficient algorithms to improve their scalability and (4) a multi-modal and short-term human-robot interaction to exchange information and requests. COACHES project will provide a modular architecture to be integrated in real robots. We deploy COACHES at Caen city in a mall called “Rive de l’orne”. COACHES is a cooperative system consisting of ?xed cameras and the mobile robots. The ?xed cameras can do object detection, tracking and abnormal events detection (objects or behaviour). The robots combine these information with the ones perceived via their own sensor, to provide information through its multi-modal interface, guide people to their destinations, show tramway stations and transport goods for elderly people, etc.... The COACHES robots will use different modalities (speech and displayed information) to interact with the mall visitors, shopkeepers and mall managers. The project has enlisted an important an end-user (Caen la mer) providing the scenarios where the COACHES robots and systems will be deployed, and gather together universities with complementary competences from cognitive systems (SU), robust image/video processing (VUB, UNICAEN), and semantic scene analysis and understanding (VUB), Collective decision-making using decentralized partially observable Markov Decision Processes and multi-agent planning (UNICAEN, Sapienza), multi-modal and short-term human-robot interaction (Sapienza, UNICAEN

Multi-Robot Perception and Action: World Modeling and Task Allocation

Multi-Robot Systems (MRS) is one research area where Artificial Intelligence and robotic techniques can be efficiently integrated. In this paper, we attempt to highlight some common aspects of the robotic and AI literature on multi-robot systems by surveying the most recent works in such an area. In particular, we focus on works that deal with the problem of coordinating a team of autonomous robots perceiving the world and acting in it to carry out a common task. By surveying these works, we attempt to give a new perspective on the problem of multi-robot world modeling and distributed multi-robot coordinatio

Generation of probabilistic graphs for path planning from stochastic maps

This paper presents a method to automatically generate probabilistic graphs for path planning out of stochastic maps. A sample based technique is used to generate a set of paths from which a graph structure of the map is built. The nodes of the graph represent places in the map and the edges the paths between those places, which are labelled with the cost of traversing the edge and the probability of being navigable. We provide results of the proposed method using different types of stochastic maps such as feature-based maps, occupancy grid maps and pose graph maps

Position tracking and path planning in uncertain maps for robot formations

This paper presents a complete working system for robot formations, where path planning and localization tasks are integrated in such a way that environment uncertainty is considered in each of the tasks. Feature-based and grid-based mapping strategies are combined in a probabilistic way to compute an obstacle-free and of minimum-risk plan towards the goal. The formation benefits from the cooperative perception to obtain a joint vision of the environment, represented in a leadercentric way to minimize the effects of the uncertainty. The system has been tested and validated by means of a set of simulations as well as in real experiments. © 2012 IFAC

Distributed localization and submapping for robot formations using a prior map

In this paper, we present a distributed algorithm which allows a robot formation to jointly improve a prior stochastic map of the environment where it has to accomplish a commanded task. To reduce the computational cost of the global map updates we exploit the fact that a robot formation work in the same map area allowing us to use conditional independence properties over the state distribution. Each robot maintains its own local and global maps which can be improved with the information received when communications among robots take place. Besides, the robots also exchange their positions in order to maintain the structure of the formation. Simulation experiments were conducted showing that, after the synchronization steps, each robot will have exactly the same information about the map and about the location of the robots at its disposal. Our results also demonstrate the achieved precision and efficiency of the proposed distributed algorithm. © 2013 IFAC

Multi-robot SLAM using condensed measurements

In this paper we describe a Simultaneous Localization and Mapping (SLAM) approach specifically designed to address the communication and computational issues that affect multi-robot systems. Our method utilizes condensed measurements to exchange map information between the robots. These measurements can effectively compress relevant portions of a map in a few data. This results in a substantial reduction of both the data to be transmitted and processed, that renders the system more robust and efficient. As documented by our simulated and real world experiments, these advantages come with a very little decrease in accuracy compared to ideal (but not realistic) methods that share the full data among all the robots. © 2013 IEEE