In this paper, we present a quantitative, trajectory-based method for calibrating stochastic motion models of water-floating robots. Our calibration method is based on the Correlated Random Walk (CRW) model, and consists in minimizing the Kolmogorov-Smirnov (KS) distance between the step length and step angle distributions of real and simulated trajectories generated by the robots. First, we validate this method by calibrating a physics-based motion model of a single 3-cm-sized robot floating at a water/air interface under fluidic agitation. Second, we extend the focus of our work to multi-robot systems by performing a sensitivity analysis of our stochastic motion model in the context of Self-Assembly ( SA). In particular, we compare in simulation the effect of perturbing the calibrated parameters on the predicted distributions of self-assembled structures. More generally, we show that the SA of water-floating robots is very sensitive to even small variations of the underlying physical parameters, thus requiring real-time tracking of its dynamics.
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In the emerging field of soft robotics, there is an interest in developing new kinds of sensors whose characteristics do not affect the intrinsic compliance of soft robot components. Additionally, non-invasive shape and deflection sensors may provoke improved solutions to assist in the control of mechanical parts in these robots. Herein, we introduce a novel method for deflection sensing where an LED element and a photodiode are placed on to two substrates connected physically or virtually at a deflection point. The deflection angle between the two planes can be extracted from the LED light intensity detected at the photodiode due to the bell-shaped angular intensity profile of the emitted light. The main advantage of this system is that the components are not in physical contact with the deflection region as in the case of strain gauges and similar sensing methods. The sensor is characterized in a range of deflections of 105-180 degrees, showing a near 1 degree resolution. The experimental data are compared to simulations, modeled by ray tracing. The light intensity vs. deflection angle measurements in our setup display a maximum difference of 9% and an average difference of approximately 5% with respect to the model. Finally, a shape monitoring system has been developed using the proposed concept for a flexible PCB. The system is composed of 12 deflection sensors that operate at frame rate of 33 Hz. This device could be applied to monitor the body shape of a soft robot.
We present a communication based navigation algorithm for robotic swarms. It lets robots guide each other’s navigation by exchanging messages containing navigation information through the wireless network formed among the swarm. We study the use of this algorithm in two different scenarios. In the first scenario, the swarm guides a single robot to a target, while in the second, all robots of the swarm navigate back and forth between two targets. In both cases, the algorithm provides efficient navigation, while being robust to failures of robots in the swarm. Moreover, we show that in the latter case, the system lets the swarm self-organize into a robust dynamic structure. This self-organization further improves navigation efficiency, and is able to find shortest paths in cluttered environments. We test our system both in simulation and on real robots.
In this article, the RObject concept is first introduced. This is followed by a survey of applicable energy scavenging technologies. Energy is a key issue for the large scale deployment of robotics in daily life, as recharging the batteries places a considerable burden on the end-user and is a waste of energy which has an overall negative impact on the limited resources of our planet. We show how the energy obtained from light, water flow, and human work, could be promising sources of energy for powering low-duty devices. To assess the feasibility of powering future RObjects with technologies, tests were conducted on commonly available robotic vacuum cleaners. These tests established an upper-bound on the power requirements for RObjects. Finally, based on these results, the feasibility of powering RObjects using scavenged energy is discussed.
In this thesis we tackle the problem of goal-oriented adaptation of a robot hitting motion. We propose the parameters that must be learned in order to use and adapt a basic hitting motion to play minigolf. Then, two different statistical methods are used to learn these parameters. The two methods are evaluated and compared. To validate the proposed approach, a minigolf control module is developed for a robotic arm. Using the different learning techniques, we show that a robot can learn the non-trivial task of deciding how the ball should be hit for a given position on a minigolf field. The result is a robust minigolf-playing system that outperforms most human players using only a small set of training examples.
We derive the theoretical performance of three bio-inspired odor source localization algorithms (casting, surge-spiral and surge-cast) in laminar wind flow. Based on the geometry of the trajectories and the wind direction sensor error, we calculate the distribution of the distance overhead and the mean success rate using Bayes inference. Our approach is related to particle filtering and produces smooth output distributions. The results are compared to existing real-robot and simulation results, and a good match is observed.
We report on real-robot odor source localization experiments carried out in an environment with obstacles in the odor plume. The robot was equipped with an ethanol sensor and a wind direction sensor, and the experiments were carried out in a wind tunnel, i.e. in a controlled environment. An enhanced version of the surge-spiral algorithm was used, which was augmented with a dedicate behavior to manage obstacles (avoid them, or follow their contour). We compare the results in terms of distance overhead and success rate, and discuss the impact of obstacles on plume traversal.
We introduce a novel bio-inspired odor source localization algorithm (surge- cast) for environments with a main wind ﬂow and compare it to two well-known algorithms. With all three algorithms, systematic experiments with real robots are carried out in a wind tunnel under laminar ﬂow conditions. The algorithms are compared in terms of distance overhead when tracking the plume up to the source, but a variety of other experimental results and some theoretical considerations are provided as well. We conclude that the surge-cast algorithm yields signiﬁcantly better performance than the casting algorithm, and slightly better performance than the surge-spiral algorithm.
We compare two well-known algorithms for locating odor sources in environments with a main wind flow. Their plume tracking performance is tested through systematic experiments with real robots in a wind tunnel under laminar flow condition. We present the system setup and show the wind and odor profiles. The results are then compared in terms of time and distance to reach the source, as well as speed in upwind direction. We conclude that the spiral- surge algorithm yields significantly better results than the casting algorithm, and discuss possible rationales behind this performance difference.