Distributed multi-robot exploration under complex constraints

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería InformáticaClave Programa: DBICódigo Línea: 19Mobile robots have emerged as a prime alternative to explore physical processes of interest. This is particularly relevant in situations that have a high risk for humans, like e.g. in search and rescue missions, and for applications in which it is desirable to reduce the required time and manpower to gather information, like e.g. for environmental analysis. In such context, exploration tasks can clearly benefit from multi-robot coordination. In particular, distributed multi-robot coordination strategies offer enormous advantages in terms of both system¿s efficiency and robustness, compared to single-robot systems. However, most state-of-the-art strategies employ discretization of robots¿ state and action spaces. This makes them computationally intractable for robots with complex dynamics, and limits their generality. Moreover, most strategies cannot handle complex inter-robot constraints like e.g. communication constraints. The goal of this thesis is to develop a distributed multi-robot exploration algorithm that tackles the two aforementioned issues. To achieve this goal we first propose a single-robot myopic approach, in which we build to develop a non-myopic informative path planner. In a second step, we extend our non-myopic single-robot algorithm to the multi-robot case. Our proposed algorithms build on the following techniques: (i) Gaussian Processes (GPs) to model the spatial dependencies of a physical process of interest, (ii) sampling-based planners to calculate feasible paths; (iii) information metrics to guide robots towards informative locations; and (iv) distributed constraint optimization techniques for multi-robot coordination. We validated our proposed algorithms in simulations and experiments. Specifically, we carried out the following experiments: mapping of a magnetic field with a ground-based robot, mapping of a terrain profile with two quadcopters equipped with an ultrasound sensor, and exploration of a simulated wind field with three quadcopters. Results demonstrate the effectiveness of our approach to perform exploration tasks under complex constraints.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Cost Adaptation for Robust Decentralized Swarm Behaviour

Decentralized receding horizon control (D-RHC) provides a mechanism for coordination in multi-agent settings without a centralized command center. However, combining a set of different goals, costs, and constraints to form an efficient optimization objective for D-RHC can be difficult. To allay this problem, we use a meta-learning process -- cost adaptation -- which generates the optimization objective for D-RHC to solve based on a set of human-generated priors (cost and constraint functions) and an auxiliary heuristic. We use this adaptive D-RHC method for control of mesh-networked swarm agents. This formulation allows a wide range of tasks to be encoded and can account for network delays, heterogeneous capabilities, and increasingly large swarms through the adaptation mechanism. We leverage the Unity3D game engine to build a simulator capable of introducing artificial networking failures and delays in the swarm. Using the simulator we validate our method on an example coordinated exploration task. We demonstrate that cost adaptation allows for more efficient and safer task completion under varying environment conditions and increasingly large swarm sizes. We release our simulator and code to the community for future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

DECENTRALIZED MULTI-ROBOT PLANNING TO EXPLORE AND PERCEIVE

In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we propose an adaptation of this method to improve also object recognition by integrating into the DVF the interest in covering explored areas with photos. The robots will then act to maximize the explored space and the photo coverage, ensuring better perception and object recognition