Robotics 2010: Current and future challenges

Trajectory design of an air vehicle in dense and complex environments, while pushing the limits of the vehicle to full performance is a challenging problem in two facets. The first facet is the control system design over the full flight envelope and the second is the trajectory planning utilizing the full performance of the aircraft. In this work, we try to address the mostly second facet via the generating dynamically feasible trajectory planning. Hence, a real-time implementable two step planner strategy is implemented for obtaining 3D flight-path generation for an Unmanned Aerial Vehicles in 3D Complex environments. Thus simplifications on the problem improved the real time implement ability. In our approach, initially, simplified version of the RRT planner is used for rapidly exploring the environment with an approximate line segments. The resulting connecting path is converted into flight way points through a line-of-sight segmentation. In second step, we explained two different methods to generate dynamically feasible trajectory. First one that we called Modal-Maneuver Based PRM Planner is developed for agile unmanned aerial vehicles that their maneuvers can be define with distinct modes. This allows significant decreases in control input space and thus search dimensions. In this approach the resulting connectivity path and the corresponding milestones are refined with a single query Probabilistic Road Map (PRM) implementation that creates dynamically feasible flight paths with distinct flight mode selections and their modal control inputs. In our second approach, remaining way points are connected with cubic (C2 continuous) B-Spline curve and this curve is repaired probabilistically to obtain a geometrically (prevents collisions) and dynamically feasible (considers velocity and acceleration constraints) path. At the end, the time scaling approach allow dynamic achievability considering the velocity and acceleration limits of the aircrafts. Resulting strategy is tested on real-time physical hardware system using ITU CAL mobile robot testbed for 2D environments and simulations for 3D complex environments. Computational times showed satisfactory results to used for real time implementation for UAVs operations in challenging urban environments Preface......Page 5 Gregory Dubus, Olivier David and Yvan Measson......Page 13 Satoko Abiko and Gerd Hirzinger......Page 41 Farhad Aghili......Page 57 Houssem Abdellatif, Jens Kotlarski, Tobias Ortmaier and Bodo Heimann......Page 75 Yuichi Kobayashi & Shigeyuki Hosoe......Page 97 Taku Senoo, Akio Namiki & Masatoshi Ishikawa......Page 121 Hiroyuki Nakamoto, Futoshi Kobayashi & Fumio Kojima......Page 135 Woosung Yang, Nak Young Chong & Bum Jae You......Page 145 Yuji Yamakawa , Akio Namiki , Masatoshi Ishikawa & Makoto Shimojo......Page 161 Philippe Garrec......Page 179 Stauffer Yves, Mohamed Bouri, Reymond Clavel ,Yves Allemand, Roland Brodard......Page 205 Malek Baklouti, Jamil AbouSaleh, Eric Monacelli and Serge Couvet......Page 223 Misato Nihei, Takeshi Ando, Yuzo Kaneshige, Takenobu Inoue, & Masakatsu G. Fujie......Page 235 Tetsuya Kinugasa, Yuta Otani, Takafumi Haji, Koji Yoshida, Koichi Osuka and Hisanori Amano......Page 253 and Raman Paranjape......Page 273 Hayato Omori, Taro Nakamura, Tomohide Iwanaga and Takeshi Hayakawa......Page 311 Emre Koyuncu and Gokhan Inalhan......Page 333 Kangsoo Kim & Tamaki Ura......Page 359 Burger Brice, Ferrané Isabelle, Lerasle Frederic......Page 381 Shuichi Nishio, JaeYeong Lee and Wonpil Yu, Yeonho Kim, Takeshi Sakamoto, Itsuki Noda, Takashi Tsubouchi, Miwako Doi......Page 393 Dražen Brščić and Hideki Hashimoto......Page 413 Ilkka Leppänen......Page 431 Victor Ng-Thow-Hing , Jongwoo Lim, Joel Wormer,Ravi Kiran Sarvadevabhatla, Carlos Rocha, Kikuo Fujimura and Yoshiaki Sakagami......Page 445 Takenobu Chikaraishi, Takashi Minato & Hiroshi Ishiguro......Page 467 Minoru Hashimoto and Misaki Yamano......Page 487 This chapter presents an example of human-like behavior of a planar robot arm whose joints were coupled to neural oscillators. In contrast to existing works that were only capable of rhythmic pattern generation, the proposed approach allowed the robot arm to trace a trajectory correctly through entrainment. For successfully achieving this, we proposed an optimization approach for obtaining the parameters of the neural oscillator modifying the simulated annealing method. Simulation and experimental results showed the effectiveness of the proposed approach. Moreover, it was demonstrated that the robot arm could adaptively behave responding to external disturbances keeping the shape of the trajectory unchanged. This approach will be extended to a more complex behavior toward the realization of biologically inspired robot control architectures