References of "Autonomous Robots"
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See detailModel predictive cooperative localization control of multiple UAVs using potential function sensor constraints
Dentler, Jan Eric UL; Kannan, Somasundar UL; Bezzaoucha, Souad UL et al

in Autonomous Robots (2018)

The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this ... [more ▼]

The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver. [less ▲]

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See detailAnalyzing and improving multi-robot missions by using process mining
Roldán, Juan Jesús; Olivares Mendez, Miguel Angel UL; del Cerro, Jaime et al

in Autonomous Robots (2017)

Multi-robot missions can be compared to industrial processes or public services in terms of complexity, agents and interactions. Process mining is an emerging discipline that involves process modeling ... [more ▼]

Multi-robot missions can be compared to industrial processes or public services in terms of complexity, agents and interactions. Process mining is an emerging discipline that involves process modeling, analysis and improvement through the information collected by event logs. Currently, this discipline is successfully used to analyze several types of processes, but is hardly applied in the context of robotics. This work proposes a systematic protocol for the application of process mining to analyze and improve multi-robot missions. As an example, this protocol is applied to a scenario of fire surveillance and extinguishing with a fleet of UAVs. The results show the potential of process mining in the analysis of multi-robot missions and the detection of problems such as bottlenecks and inefficiencies. This work opens the way to an extensive use of these techniques in multi-robot missions, allowing the development of future systems for optimizing missions, allocating tasks to robots, detecting anomalies or supporting operator decisions. [less ▲]

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See detailA mixed integer linear programming approach to pursuit evasion problems with optional connectivity constraints
Thunberg, Johan UL; Ögren, P.

in Autonomous Robots (2011), 31(4), 333-343

In this paper, we address the multi pursuer version of the pursuit evasion problem in polygonal environments. By discretizing the problem, and applying a Mixed Integer Linear Programming (MILP) framework ... [more ▼]

In this paper, we address the multi pursuer version of the pursuit evasion problem in polygonal environments. By discretizing the problem, and applying a Mixed Integer Linear Programming (MILP) framework, we are able to address problems requiring so-called recontamination and also impose additional constraints, such as connectivity between the pursuers. The proposed MILP formulation is less conservative than solutions based on graph discretizations of the environment, but still somewhat more conservative than the original underlying problem. It is well known that MILPs, as well as multi pursuer pursuit evasion problems, are NP-hard. Therefore we apply an iterative Receding Horizon Control (RHC) scheme where a number of smaller MILPs are solved over shorter planning horizons. The proposed approach is implemented in Matlab/Cplex and illustrated by a number of solved examples. [less ▲]

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See detailUnmanned aerial vehicles UAVs attitude, height, motion estimation and control using visual systems
Mondragon, Ivan F.; Olivares Mendez, Miguel Angel UL; Campoy, Pascual et al

in AUTONOMOUS ROBOTS (2010), 29(1), 17-34

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an ... [more ▼]

This paper presents an implementation of an aircraft pose and motion estimator using visual systems as the principal sensor for controlling an Unmanned Aerial Vehicle (UAV) or as a redundant system for an Inertial Measure Unit (IMU) and gyros sensors. First, we explore the applications of the unified theory for central catadioptric cameras for attitude and heading estimation, explaining how the skyline is projected on the catadioptric image and how it is segmented and used to calculate the UAV's attitude. Then we use appearance images to obtain a visual compass, and we calculate the relative rotation and heading of the aerial vehicle. Additionally, we show the use of a stereo system to calculate the aircraft height and to measure the UAV's motion. Finally, we present a visual tracking system based on Fuzzy controllers working in both a UAV and a camera pan and tilt platform. Every part is tested using the UAV COLIBRI platform to validate the different approaches, which include comparison of the estimated data with the inertial values measured onboard the helicopter platform and the validation of the tracking schemes on real flights. [less ▲]

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See detailLearning model-free robot control by a Monte Carlo EM algorithm
Vlassis, Nikos UL; Toussaint, Marc; Kontes, Georgios et al

in Autonomous Robots (2009), 27(2), 123-130

We address the problem of learning robot control by model-free reinforcement learning (RL). We adopt the probabilistic model for model-free RL of Vlassis and Toussaint (Proceedings of the international ... [more ▼]

We address the problem of learning robot control by model-free reinforcement learning (RL). We adopt the probabilistic model for model-free RL of Vlassis and Toussaint (Proceedings of the international conference on machine learning, Montreal, Canada, 2009), and we propose a Monte Carlo EM algorithm (MCEM) for control learning that searches directly in the space of controller parameters using information obtained from randomly generated robot trajectories. MCEM is related to, and generalizes, the PoWER algorithm of Kober and Peters (Proceedings of the neural information processing systems, 2009). In the finite-horizon case MCEM reduces precisely to PoWER, but MCEM can also handle the discounted infinite-horizon case. An interesting result is that the infinite-horizon case can be viewed as a 'randomized' version of the finite-horizon case, in the sense that the length of each sampled trajectory is a random draw from an appropriately constructed geometric distribution. We provide some preliminary experiments demonstrating the effects of fixed (PoWER) vs randomized (MCEM) horizon length in two simulated and one real robot control tasks. [less ▲]

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