This work addresses multi-robot coordination in social human-populated environments using a market-based framework for solving the Multi-Robot Task Allocation (MRTA) problem. Humans are considered in the proposed coor...
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This work addresses multi-robot coordination in social human-populated environments using a market-based framework for solving the Multi-Robot Task Allocation (MRTA) problem. Humans are considered in the proposed coordination mechanism by means of accounting for social costs in bid evaluations and requesting collaboration in socially blocking situations. Initially, the effect of a realistic environment with varying number of static/moving humans on the behavior and performance of our method is studied through an extensive suite of experiments in a high-fidelity simulator. Results show that the total traveled distance and time are increased when humans are present in the environments. Localization noise is also increased particularly in the case of static people. In the second series of experiments, a number of problematic cases resulting in longer modified paths, blocked passages, and long waits have been investigated. A comparative study targeting human-agnostic navigation and planning, human-aware navigation and human-agnostic planning, and human-aware navigation and planning has been conducted. Both simulated and real robot experiments confirm the effectiveness of accounting for humans at both team and individual levels. This leads to respecting social constraints as well as achieving a better performance based on MRTA metrics.
Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithm...
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Collision avoidance, in particular between robots, is an important component for autonomous robots. It is a necessary component in numerous applications such as humanrobot interaction, automotive or unmanned aerial ve...
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Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on publi...
ISBN:
(纸本)9780994988614
Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on public transportation vehicles, (ii) explanatory air-quality and meteorological variables obtained from two static monitoring stations, (iii) land-use data of the city, and (iv) traffic statistics. We propose two novel approaches for estimating the targeted pollutant level at desired time-location pairs, extending also to areas of the city that are beyond the coverage of our mobile sensor network. The first is a log-linear regression model which is built over a virtual dependency graph based on land-use data. The second is a deep learning framework that automatically captures the dependencies of the data based on autoencoders. We have evaluated the two proposed approaches against three canonical modeling techniques considering metrics of coefficient of determination (R²), root mean square error (RMSE), and the fraction of predictions within a factor of two of observations (FAC2). Using more than 45 million real measurements in the models, the results show consistently superior performance in respect to the canonical techniques.
Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithm...
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Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithmic solution in which each robot locally finds the best paths and coverage actions to keep the desired coverage level over the whole environment. Using Fast Marching Methods, optimal paths are computed in terms of coverage quality, while keeping a safety distance to obstacles. Additionally, our solution enables a computationally efficient evaluation of a list of potential trajectories, allowing us to choose the one that mostly improves the coverage along the whole path. The combination of this algorithm with a Dynamic Window navigation makes our approach competitive in terms of flexibility and robustness in changing environments with existing solutions. Finally, we also propose a coverage action controller, locally computed and optimal, that makes the robots maintain the coverage level of the environment significantly close to the objective. Simulations and real experiments validate the whole approach.
This work tackles the problem of quadrotor formation control, using exclusively on-board resources. Local inter-robot localization systems are typically characterized by limited sensing capabilities, either in range o...
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ISBN:
(纸本)9781510855083
This work tackles the problem of quadrotor formation control, using exclusively on-board resources. Local inter-robot localization systems are typically characterized by limited sensing capabilities, either in range or in the field of view. Most of the existing literature on the subject uses inter-robot communication to obtain the unavailable information, but problems such as communication delays and packet loss can seriously compromise the system stability, especially when the system shows fast dynamics such as that of quadrotors. This work focuses on the sensor field of view limitation: it proposes a formation control algorithm that extends the existing methods to allow each quadrotor to control the occupied area of its sensor field of view, while moving to the right place in the formation. This decreases the situations when necessary inter-robot information is unavailable through local sensing, thus reducing the communication requirements. The system is proven to be stable when this algorithm is applied. Results, both using simulated and real quadrotors, show the correct behavior of the algorithm without the use of communications, even when each robot can only sense a subset of the robots in the group.
The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of ...
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The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization problem. Their aim is to increase the robustness of the generated robotic controllers, as compared to previous PSO algorithms. We show that we can successfully apply this set of PSO algorithms to learn 166 parameters of a robotic controller for the obstacle avoidance task. We also study the impact that an increased evaluation budget has on the robustness and average performance of the optimized controllers. Finally, we validate the control solutions learned in simulation by testing the most robust controller in three different real arenas.
This paper presents an overtaking decision algorithm for networked intelligent vehicles. The algorithm is based on a cooperative tracking and sensor fusion algorithm that we previously developed. The ego vehicle is eq...
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ISBN:
(纸本)9781509018222
This paper presents an overtaking decision algorithm for networked intelligent vehicles. The algorithm is based on a cooperative tracking and sensor fusion algorithm that we previously developed. The ego vehicle is equipped with lane keeping and lane changing capabilities, as well as a forward-looking lidar sensor. The lidar data are fed to the tracking module which detects other vehicles, such as the vehicle that is to be overtaken (leading) and the oncoming traffic. Based on the estimated distances to the leading and the oncoming vehicles and their speeds, a risk is calculated and a corresponding overtaking decision is made. We compare the performance of the overtaking algorithm between the case when the ego vehicle only relies on its lidar sensor, and the case in which it fuses object estimates received from the leading car which also has a forward-looking lidar. Systematic evaluations are performed in Webots, a calibrated high-fidelity simulator.
In recent years, a growing number of research groups have targeted the development and deployment of networks using low-cost chemical sensors for monitoring air quality. Due to economical reasoning, most of these syst...
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ISBN:
(纸本)9781509020669
In recent years, a growing number of research groups have targeted the development and deployment of networks using low-cost chemical sensors for monitoring air quality. Due to economical reasoning, most of these systems make use of some sort of mobility to increase spatial coverage. The effect of mobility on measurement quality has, however, been largely neglected. The long response time of the chemical sensors typically used for this type of application, in conjunction with platform mobility, leads to significant signal distortion. While this problem can be addressed through signal deconvolution techniques, their effectiveness is limited by the typical poor Signal-to-Noise Ratio (SNR) of the measured signal. In this paper, we study the possibility of enhancing the measurement quality of chemical sensors through the use of active sampling (or sniffing). We propose different sniffer designs, employing both fans and pumps as actuators. Using a rigorous experimental framework, inside a wind tunnel, we study the ability of active samplers to increase measurement SNR, and thus indirectly to improve sensor dynamic response. We obtain a significant and consistent improvement in SNR for one of our pump-based sniffer designs. Finally, we validate the robustness of this signal enhancement in real-world conditions through an outdoor car-based experiment.
This paper investigates the problem of controlling a heterogeneous group of vehicles with the aim of forming multilane convoys. We use a distributed, graph-based control law, implemented in a longitudinal coordinate s...
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This paper investigates the problem of controlling a heterogeneous group of vehicles with the aim of forming multilane convoys. We use a distributed, graph-based control law, implemented in a longitudinal coordinate system parallel to the road. Each vehicle maintains a local graph with information from only nearby vehicles, in which the desired distances between vehicles are calculated dynamically. This allows for fast adaptation to the changes in the number of vehicles and their positions. We have also implemented a distributed mechanism that allows vehicles to change lane in a cooperative way within the convoy. Systematic experiments have been carried out in a high-fidelity simulator in order to show the performance of the proposed control law.
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