In this paper we address the stabilization of the attitude and position of a birotor miniUAV to perform autonomous flight. For this purpose, we have implemented a Kalman-based sensorfusion between inertial sensors (g...
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In this paper we address the stabilization of the attitude and position of a birotor miniUAV to perform autonomous flight. For this purpose, we have implemented a Kalman-based sensorfusion between inertial sensors (gyros-accelerometers) and the optical flow (OF) provided by the vehicle. This fusion algorithm extracts the translational-OF (TOF) component and discriminates the rotational OF (ROF). The aircraft's position is obtained through an object detection algorithm (centroid tracking). Newton-Euler motion equations were used to deduce the mathematical model of the vehicle. In terms of control we have employed a saturated-based control to stabilize the state of the aircraft around the origin. Experimental autonomous flight was successfully achieved, which validates the sensing strategy as well as the embedded control law.
In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking robotics in Urban Sites), a project whose objective is to develop an...
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In this article we explain the architecture for the environment and sensors that has been built for the European project URUS (Ubiquitous Networking robotics in Urban Sites), a project whose objective is to develop an adaptable network robot architecture for cooperation between network robots and human beings and/or the environment in urban areas. The project goal is to deploy a team of robots in an urban area to give a set of services to a user community. This paper addresses the sensor architecture devised for URUS and the type of robots and sensors used, including environment sensors and sensors onboard the robots. Furthermore, we also explain how sensorfusion takes place to achieve urban outdoor execution of robotic services. Finally some results of the project related to the sensor network are highlighted.
The challenge for unmanned aerial vehicles to sense and avoid obstacles becomes even harder if narrow passages have to be crossed. An approach to solve a mission scenario that tackles the problem of such narrow passag...
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The challenge for unmanned aerial vehicles to sense and avoid obstacles becomes even harder if narrow passages have to be crossed. An approach to solve a mission scenario that tackles the problem of such narrow passages is presented here. The task is to fly an unmanned helicopter autonomously through a course with gates that are only slightly larger than the vehicle itself. A camera is installed on the vehicle to detect the gates. Using vehicle localization data from a navigation solution, camera alignment and global gate positions are estimated simultaneously. The presented algorithm calculates the desired target waypoints to fly through the gates. Furthermore, the paper presents a mission execution plan that instructs the vehicle to search for a gate, to fly through it after successful detection, and to search for a proceeding one. All algorithms are designed to run onboard the vehicle so that no interaction with the ground control station is necessary, making the vehicle completely autonomous. To develop and optimize algorithms, and to prove the correctness and accuracy of vision-based gate detection under real operational conditions, gate positions are searched in images taken from manual helicopter flights. Afterwards, the integration of visual sensing and mission control is proven. The paper presents results from full autonomous flight where the helicopter searches and flies through a gate without operator actions.
This paper presents a cooperative control strategy for a team of aerial robotic vehicles to establish wireless airborne communication networks between distributed heterogeneous vehicles. Each aerial robot serves as a ...
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This paper presents a cooperative control strategy for a team of aerial robotic vehicles to establish wireless airborne communication networks between distributed heterogeneous vehicles. Each aerial robot serves as a flying mobile sensor performing a reconfigurable communication relay node which enabls communication networks with static or slow-moving nodes on gorund or ocean. For distributed optimal deployment of the aerial vehicles for communication networks, an adaptive hill-climbing type decentralizedcontrol algorithm is developed to seek out local extremum for optimal localization of the vehicles. The sensor networks estabilished by the decentralized cooperative control approach can adopt its configuraiton in response to signal strength as the function of the relative distance between the autonomous aerial robots and distributed sensor nodes in the sensed environment. Simulation studies are conducted to evaluate the effectiveness of the proposed decentralized cooperative control technique for robust communication networks.
Due to their advantages, omni-directional mobile robots have found many applications especially in robotic soccer competitions. However, omni directional navigation system, omni-vision system and kicking mechanism in ...
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ISBN:
(纸本)9789814291262
Due to their advantages, omni-directional mobile robots have found many applications especially in robotic soccer competitions. However, omni directional navigation system, omni-vision system and kicking mechanism in such mobile robots have not ever been combined. This situation brings the idea of a robot with no head direction into existence, a comprehensive omni directional mobile robot. Such a robot can respond more quickly and it would be capable for more sophisticated behaviors with multi-sensor data fusion algorithm for global localization. Despite recent advances, effective control and self-localization methods of omni-directional mobile robots remain as important and challenging issues. For this purpose, we utilize the sensor data fusion method in the control system parameters, self localization and world modeling. A vision-based self-localization and the conventional odometry systems are fused for robust self-localization. The methods have been tested in the many Robocup competition field middle size robots.. The localization algorithm includes filtering, sharing and integration of the data for different types of objects recognized in the environment. This paper has tried to focus on description of areas such as omni directional mechanisms, mechanical structure, omni-vision sensor for object detection, robot path planning, and other subjects related to mobile robot's software.
The objectives of autonomous mobile robotic navigation are the development of a physical system that is functionally operative without human intervention in an unstructured environment. In this work, an effective cont...
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The objectives of autonomous mobile robotic navigation are the development of a physical system that is functionally operative without human intervention in an unstructured environment. In this work, an effective control algorithm was developed for the Khepera II robot that enables the robot to navigate through any complex environment. In the present work, the knowledge of the complex environment is obtained through the multiple sensor data fusion. A GUI was created using LabVIEW to read the sensors data during navigation and used for data analysis. In this report, the development of systematic approach for mobile robot navigation, theoretical and experimental investigations are discussed and provided in the following sections.
Networks of environmental sensors are playing an increasingly important role in scientific studies of the ocean, rivers, and the atmosphere. roboticsensors can improve the efficiency of data collection, adapt to chan...
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ISBN:
(纸本)9781424474264
Networks of environmental sensors are playing an increasingly important role in scientific studies of the ocean, rivers, and the atmosphere. roboticsensors can improve the efficiency of data collection, adapt to changes in the environment, and provide a robust response to individual failures. Their operation must be driven by statistically-aware algorithms that make the most of the network capabilities for data collection and fusion. At the same time, such algorithms need to be distributed and scalable to make robotic networks capable of operating in an autonomous and robust fashion. The combination of these two objectives, complex statistical modeling and distributed coordination, presents grand technical challenges: traditional statistical modeling and inference assume full availability of all measurements and central computation. While the availability of data at a central location is certainly a desirable property, the paradigm for distributed motion coordination builds on partial, fragmented information. This work surveys recent progress at bridging the gap between sophisticated statistical modeling and distributed motion coordination.
We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which r...
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We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.
This paper presents a novel method for active-vision-based sensing-system reconfiguration for the autonomous surveillance of an object-of-interest as it travels through a multi-object dynamic workspace with an a prior...
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This paper presents a novel method for active-vision-based sensing-system reconfiguration for the autonomous surveillance of an object-of-interest as it travels through a multi-object dynamic workspace with an a priori unknown trajectory. Several approaches have been previously proposed to address the problem of sensor selection and control. However, these have primarily relied on off-line planning methods and rarely utilized on-line planning to compensate for unexpected variations in a target's trajectory. The method proposed in this paper, on the other hand, uses a multi-agent system for on-line sensing-system reconfiguration, eliminating the need for any a priori knowledge of the target's trajectory. Thus, it is robust to unexpected variations in the environment. Simulations and experiments have shown that the use of dynamic sensors with the proposed on-line reconfiguration algorithm can tangibly improve the performance of an active-surveillance system.
This paper develops active Simultaneous Localisation And Mapping (SLAM) trajectory control strategies for multiple cooperating Unmanned Aerial Vehicles (UAVs) for tasks such as surveillance and picture compilation in ...
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This paper develops active Simultaneous Localisation And Mapping (SLAM) trajectory control strategies for multiple cooperating Unmanned Aerial Vehicles (UAVs) for tasks such as surveillance and picture compilation in Global Positioning System (GPS)-denied environments. Each UAV in the team uses inertial sensor and terrain sensor information to simultaneously localise the UAV while building a point feature map of the surrounding terrain, where map information is shared between vehicles over a data fusion network. Multi-vehicle active SLAM control architectures are proposed that actively plan the trajectories and motions of each of the vehicles in the team based on maximising information in the localisation and mapping estimates. We demonstrate and compare an ideal, centralised architecture, where a central planning node chooses optimal actions for each UAV, and a coordinated, decentralised architecture, where UAVs make their own control decisions based on common shared map information. The different architectures involve varying degrees of complexity and optimality through differing communications and computational requirements. Results are presented using a three-UAV team in a six-degree of freedom multi-UAV simulator.
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