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The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. Increasing the controller complexity may be a desirable choice in order to obtain an improved performance. However, these two aspects may pose a considerable challenge on the optimization of robotic controllers. In this paper, we study the trade-offs between the complexity of reactive controllers and the complexity of the environment in the optimization of multi-robot obstacle avoidance for resource-constrained platforms. The optimization is carried out in simulation using a distributed, noise-resistant implementation of Particle Swarm Optimization, and the resulting controllers are evaluated both in simulation and with real robots. We show that in a simple environment, linear controllers with only two parameters perform similarly to more complex non-linear controllers with up to twenty parameters, even though the latter ones require more evaluation time to be learned. In a more complicated environment, we show that there is an increase in performance when the controllers can differentiate between front and backwards sensors, but increasing further the number of sensors and adding non-linear activation functions provide no further benefit. In both environments, augmenting reactive control laws with simple memory capabilities causes the highest increase in performance. We also show that in the complex environment the performance measurements are noisier, the optimal parameter region is smaller, and more iterations are required for the optimization process to converge.
Posted on: March 4, 2014
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A method for fabricating an imaging system, the method comprising providing a flexible substrate (200), a first layer (220) comprising a plurality of microlenses (232) and a second layer (240) comprising a plurality of image sensors (242). The method further comprises stacking the first and the second layer (220; 240) onto the flexible substrate (200) by attaching the plurality of image sensors (242) to the flexible substrate, such that each of the plurality of microlenses (232) and image sensors (242) are aligned to form a plurality of optical channels (300) , each optical channel comprising at least one microlens and at least one associated image sensor, and mechanically separating the optical channels (300) such that the separated optical channels remain attached to the flexible substrate (200) to form a mechanically flexible imaging system.
Posted on: February 28, 2014
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Objective: A fundamental issue in EEG event-related potentials (ERPs) studies is the amount of data required to have an accurate ERP model. This also impacts the time required to train a classifier for a brain-computer interface (BCI). This issue is mainly due to the poor signal-to-noise ratio, and to the large fluctuations of the EEG caused by several sources of variability. One of these sources is directly related to the experimental protocol or application designed, and may affect to amplitude or latency variations. This usually prevents BCI classifiers to generalize among different experimental protocols. In this work, we analyze the effect of the amplitude and the latency variations among different experimental protocols based on the same type of ERP. Approach: We present a method to analyze and compensate for the latency variations in BCI applications. The algorithm has been tested on two widely used ERPs (P300 and observation error potentials), in three experimental protocols in each case. We report the ERP analysis and single-trial classification. Results and significance: The results obtained show (i) how the experimental protocols significantly affect the latency of the recorded potentials but not the amplitudes, and (ii) how the use of latency-corrected data can be used to generalize the BCIs, reducing this way the calibration time when facing a new experimental protocol.
Posted on: February 23, 2014
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Humans have an incredible capacity to learn properties of objects by pure tactile exploration with their two hands. With robots moving into human-centred environment, tactile exploration becomes more and more important as vision may be occluded easily by obstacles or fail because of different illumination conditions. In this paper, we present our first results on bimanual compliant tactile exploration, with the goal to identify objects and grasp them. An exploration strategy is proposed to guide the motion of the two arms and fingers along the object. From this tactile exploration, a point cloud is obtained for each object. As the point cloud is intrinsically noisy and un-uniformly distributed, a filter based on Gaussian Processes is proposed to smooth the data. This data is used at runtime for object identification. Experiments on an iCub humanoid robot have been conducted to validate our approach.
Posted on: February 17, 2014
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Modular or multi-cellular robots hold the promise to adapt their morphology to task and environment. However, research in modular robotics has traditionally been limited to mechanically non-adaptive systems due to hard building blocks and rigid connection mechanisms. To improve adaptation and global flexibility, we suggest the use of modules made of soft materials. Thanks to recent advances in fabrication techniques the development of soft robots without spatial or material constraints is now possible. In order to exploit this vast design space, computer simulations are a time and cost-efficient tool. However, there is currently no framework available that allows studying the dynamics of soft multi-cellular systems. In this work, we present our simulation framework named Soft Cell Simulator (SCS) that enables to study both mechanical design parameters as well as control problems of soft multi-cellular systems in an time-efficient yet globally accurate manner. Its main features are: (i) high simulation speed to test systems with a large number of cells (real-time up to 100 cells), (ii) large non-linear deformations without module self-penetration, (iii) tunability of module softness (0-500 N/m), (iv) physics-based module connectivity, (v) variability of module shape using internal actuators. We present results that validate the plausibility of the simulated soft cells, the scalability as well as the usability of the simulator. We suggest that this simulator helps to master and leverage the potential of the vast design space to generate novel soft multi-cellular robots.
Posted on: January 27, 2014
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Epidural electrical stimulation (EES) of lumbosacral segments can restore a range of movements after spinal cord injury. However, the mechanisms and neural structures through which EES facilitates movement execution remain unclear. Here, we designed a computational model and performed in vivo experiments to investigate the type of fibers, neurons, and circuits recruited in response to EES. We first developed a realistic finite element computer model of rat lumbosacral segments to identify the currents generated by EES. To evaluate the impact of these currents on sensorimotor circuits, we coupled this model with an anatomically realistic axon-cable model of motoneurons, interneurons, and myelinated afferent fibers for antagonistic ankle muscles. Comparisons between computer simulations and experiments revealed the ability of the model to predict EES-evoked motor responses over multiple intensities and locations. Analysis of the recruited neural structures revealed the lack of direct influence of EES on motoneurons and interneurons. Simulations and pharmacological experiments demonstrated that EES engages spinal circuits trans-synaptically through the recruitment of myelinated afferent fibers. The model also predicted the capacity of spatially distinct EES to modulate side-specific limb movements and, to a lesser extent, extension versus flexion. These predictions were confirmed during standing and walking enabled by EES in spinal rats. These combined results provide a mechanistic framework for the design of spinal neuroprosthetic systems to improve standing and walking after neurological disorders.
Posted on: January 9, 2014
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We present the design approach and evaluation of our proto- type called “Ranger”. Ranger is a robotic toy box that aims to motivate young children to tidy up their room. We evalu- ated Ranger in 14 families with 31 children (2-10 years) using the Wizard-of-Oz technique. This case study explores two different robot behaviors (proactive vs. reactive) and their impact on children’s interaction with the robot and the tidy- ing behavior. The analysis of the video recorded scenarios shows that the proactive robot tended to encourage more playful and explorative behavior in children, whereas the reactive robot triggered more tidying behavior. Our find- ings hold implications for the design of interactive robots for children, and may also serve as an example of evaluating an early version of a prototype in a real-world setting.
Posted on: December 12, 2013
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We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.
Posted on: December 3, 2013
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Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots’ battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation.
Posted on: November 29, 2013
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Flying robots that can locomote efficiently in GPS-denied cluttered environments have many applications, such as in search and rescue scenarios. However, dealing with the high amount of obstacles inherent to such environments is a major challenge for flying vehicles. Conventional flying platforms cannot afford to collide with obstacles, as the disturbance from the impact may provoke a crash to the ground, especially when friction forces generate torques affecting the attitude of the platform. We propose a concept of resilient flying robots capable of colliding into obstacles without compromising their flight stability. Such platforms present great advantages over existing robots as they are capable of robust flight in cluttered environments without the need for complex sense and avoid strategies or 3D mapping of the environment. We propose a design comprising an inner frame equipped with conventional propulsion and stabilization systems enclosed in a protective cage that can rotate passively thanks to a 3-axis gimbal system, which reduces the impact of friction forces on the attitude of the inner frame. After addressing important design considerations thanks to a collision model and validation experiments, we present a proof-of-concept platform, named GimBall, capable of flying in various cluttered environments. Field experiments demonstrate the robot’s ability to fly fully autonomously through a forest while experiencing multiple collisions.
Posted on: November 29, 2013
