Have you ever dreamed of flying? The Symbiotic Drone Activity is a project that aims to give you the sensation of flying while controlling a real drone. The goal of… Read more
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Bio-inspired underwater robots have several benefits compared to traditional underwater vehicles such as agility, efficiency, and an environmentally friendly body. However, the bio-inspired underwater robots developed so far have a single swimming mode, which may limit their capability to perform different tasks. This paper presents a re-configurable bio-inspired underwater robot that changes morphology to enable multiple swimming modes: octopus-mode and fish-mode. The robot is 60 cm long and 50 cm wide, weighing 2.1 kg, and consists of a re-configurable body and 8 compliant arms that are actuated independently by waterproof servomotors. In the robot, the octopus-mode is expected to perform unique tasks such as object manipulation and ground locomotion as demonstrated in literature, while the fish-mode is promising to swim faster and efficiently to travel long distance. With this platform, we investigate effectiveness of adaptive morphology in bio-inspired underwater robots. For this purpose, we evaluate the robot in terms of the cost of transport and the swimming efficiency of both the morphologies. The fish-mode exhibited a lower cost of transport of 2.2 and higher efficiency of 1.2 % compared to the octopus-mode, illustrating the effect of the multiple swimming modes by adaptive morphology.
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 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.