Download Advances in Autonomous Robotics: Joint Proceedings of the by Goren Gordon, Ehud Ahissar (auth.), Guido Herrmann, Matthew PDF

By Goren Gordon, Ehud Ahissar (auth.), Guido Herrmann, Matthew Studley, Martin Pearson, Andrew Conn, Chris Melhuish, Mark Witkowski, Jong-Hwan Kim, Prahlad Vadakkepat (eds.)

This booklet constitutes the refereed court cases of the thirteenth convention on in the direction of self sufficient robot structures, TAROS 2012 and the fifteenth robotic international Congress, FIRA 2012, held as joint convention in Bristol, united kingdom, in August 2012. The 36 revised complete papers awarded including 25 prolonged abstracts have been conscientiously reviewed and chosen from 89 submissions. The papers disguise a number of issues within the box of independent robotics.

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Extra resources for Advances in Autonomous Robotics: Joint Proceedings of the 13th Annual TAROS Conference and the 15th Annual FIRA RoboWorld Congress, Bristol, UK, August 20-23, 2012

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N. Mahyuddin, G. G. e characteristic of a filtered signal can be found in [21]. This also implies that, W (t) > I where > 0 for t ≥ T˜a . We can further show that for t ≥ T˜a : V˙ (t) ≤ −rT kr r − σ(Ω1 ) ˜ − σ(Ω2 ) Θ 2 ˜ Θ 2 (48) where σ(·), σ ¯ (·) denote the minimum and maximum singular values for a matrix. ˜ → 0. Hence, the paramThis implies at least exponential convergence of (r, Θ) ˜ eter estimation error Θ enters a compact set around zero in finite-time. At this ˜ T Γ −1 Θ ˜ as follows, expense, we can now analyse, VΘ = 12 Θ ˜ T Γ −1 Θ ˜˙ V˙ Θ˜ = Θ ˜ T φ(q, q, ˜ T Ω1 W (t) = −Θ ˙ u, u)r+ ˙ Θ ˆ W (t)Θ−N (t) ˆ W (t)Θ−N (t) ˜ T Ω2 W (t)(W (t)Θ ˆ −N (t)) +Θ (49) Taking the upper bounds, ˜ − σ(Ω1 ) W (t)Θ ˜ − σ(Ω2 ) W (t)Θ ˜ V˙ Θ˜ ≤ φ m r Θ 2 ˜ 2 ˜ ≤ [ φ m r − σ(Ω1 ) ] Θ − σ(Ω2 ) Θ 2 (50) where φ m denotes the upperbound of the regressor φ(q, q, ˙ u, u).

1 NaoYARP can be used freely under the GNU General Public Licence. com/cbm/NaoYARP. S. Dahl and A. Paraschos (a) (b) Fig. 4. Figure 4a presents the distance the stimulation point on the robot surface is moved away from the point in space where the stimulation took place. Each bar represents the mean distance for 81 different starting poses. Figure 4b presents the average number of motions that produce potential self-collisions, considering only motions that start from non-collision points, (a), and motions that start from both collision and non-collision starting points, (b).

G. e characteristic of a filtered signal can be found in [21]. This also implies that, W (t) > I where > 0 for t ≥ T˜a . We can further show that for t ≥ T˜a : V˙ (t) ≤ −rT kr r − σ(Ω1 ) ˜ − σ(Ω2 ) Θ 2 ˜ Θ 2 (48) where σ(·), σ ¯ (·) denote the minimum and maximum singular values for a matrix. ˜ → 0. Hence, the paramThis implies at least exponential convergence of (r, Θ) ˜ eter estimation error Θ enters a compact set around zero in finite-time. At this ˜ T Γ −1 Θ ˜ as follows, expense, we can now analyse, VΘ = 12 Θ ˜ T Γ −1 Θ ˜˙ V˙ Θ˜ = Θ ˜ T φ(q, q, ˜ T Ω1 W (t) = −Θ ˙ u, u)r+ ˙ Θ ˆ W (t)Θ−N (t) ˆ W (t)Θ−N (t) ˜ T Ω2 W (t)(W (t)Θ ˆ −N (t)) +Θ (49) Taking the upper bounds, ˜ − σ(Ω1 ) W (t)Θ ˜ − σ(Ω2 ) W (t)Θ ˜ V˙ Θ˜ ≤ φ m r Θ 2 ˜ 2 ˜ ≤ [ φ m r − σ(Ω1 ) ] Θ − σ(Ω2 ) Θ 2 (50) where φ m denotes the upperbound of the regressor φ(q, q, ˙ u, u).

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