Robotics 2010 Current and future challenges Part 11 pptx

used on robots. Example tunnel problem solving tests can be seen in Fig. 15. On this experiment, the path planner evaluated its solution in 357 ms. Visual positioning system published current configurations in 7 Hz and motor controllers run at 60 Hz. Robot completed its all motion in 57 s. Fig. 15. Modal-Maneuver Based PRM experiments on Robotic Testbed In the second group experiments in the ITU CAL Robotic-Testbed, Probabilistic B-Spline Based Trajectory Planner is implemented. A capture seen in Figure 16 is taken from one of the experiments of the Probabilistic B-Spline Planner in robotic testbed. Evaluated path is tracked by a nonlinear control algorithm runs on robots computer. This experiment took 29 s and the path planner evaluated its solution in 278 ms while visual positioning system published current configurations in 15 Hz and motor controllers run at 60 Hz. Fig. 16. Probabilistic B-Spline Trajectory Planner experiments on Robotic Testbed 6.2 Simulations of 3D Environments To illustrate the applicability the algorithms on 3D complex environments in varying ratio of obstacle-space, performance of the algorithms is tested for 3D single-narrow-passage problem, city-like environment, mostly-blocked environment and MelCity model environment that has volume 2 3 times greater than the others. All the experiments were conducted on a 3.00 GHz Intel Pentium(R) 4 processor and the average results are obtained over 50 runs. In the first group simulations, Modal-Maneuver Based PRM algorithm for aerial vehicles is tested and computational times of the all phases of the algorithm are illustrated in Table 2 As can be seen in results, total times mostly based on Mode-Based Planner phase. As antic- ipated, increasing blocked space also increases the solution time as seen in Mostly-Blocked 5.3 Network Communication Two centralized computers (visual positioning and path planner) and robots’ computers runs on the system at the same time. These all computers are connected through a LAN network. Communication among the centralized PCs is performed with the physical ethernet cable while Centralized PCs and robots are connected with wireless network. Data communication between the units are demonstrated in Fig. 12. Testbed Network is based on a publish/subscribe architecture. To broadcast messages, sender publishes a message to all subscribers and receivers accepts only messages belongs to them according to head-tags of the messages. 5.4 Performing Low-Level Control Every robots have ability to run own low-level control algorithms. Outer loop control algorithm, a nonlinear trajectory controller, runs on robots’ own embedded Linux computers (Gumstix). To perform this algorithm, reference path is received from Path Planner PC while current configurations is received from Visual Positioning PC via wireless network. Accord- ing to position and orientation errors, trajectory controller evaluates the angular velocities of both two motors that leads the robot to track reference path. Evaluated angular velocities are sent to microcontroller (Robostix) as reference control variables through UART port. Robostix also counts the pulses of the optic encoders of the motors to evaluate current angular velocities. Received reference angular velocities and current angular velocities are compared and PWM signals are generated via PID controllers (as inner control loop) and then these PWM signals are sent to motor drivers. These both application is coded in C performs on Gumstix and Robostix. All these control architecture demonstrated in Fig. 14. Fig. 14. Control Architecture of the ITUCAL Robotic Testbed 6. Experiments and Simulation Results 6.1 Physical Hardware Demonstrations To demonstrate the applicability of the algorithms on physical systems, robot experiments have been implemented. In first group experiments on the ITU CAL Robotic Testbed, simplified version of the Modal-Maneuver Based PRM is used. On this application, two independently controlled primitive maneuver modes -straight forward mode and turning mode- are used on robots. Example tunnel problem solving tests can be seen in Fig. 15. On this experiment, the path planner evaluated its solution in 357 ms. Visual positioning system published current configurations in 7 Hz and motor controllers run at 60 Hz. Robot completed its all motion in 57 s. Fig. 15. Modal-Maneuver Based PRM experiments on Robotic Testbed In the second group experiments in the ITU CAL Robotic-Testbed, Probabilistic B-Spline Based Trajectory Planner is implemented. A capture seen in Figure 16 is taken from one of the experiments of the Probabilistic B-Spline Planner in robotic testbed. Evaluated path is tracked by a nonlinear control algorithm runs on robots computer. This experiment took 29 s and the path planner evaluated its solution in 278 ms while visual positioning system published current configurations in 15 Hz and motor controllers run at 60 Hz. Fig. 16. Probabilistic B-Spline Trajectory Planner experiments on Robotic Testbed 6.2 Simulations of 3D Environments To illustrate the applicability the algorithms on 3D complex environments in varying ratio of obstacle-space, performance of the algorithms is tested for 3D single-narrow-passage problem, city-like environment, mostly-blocked environment and MelCity model environment that has volume 2 3 times greater than the others. All the experiments were conducted on a 3.00 GHz Intel Pentium(R) 4 processor and the average results are obtained over 50 runs. In the first group simulations, Modal-Maneuver Based PRM algorithm for aerial vehicles is tested and computational times of the all phases of the algorithm are illustrated in Table 2 As can be seen in results, total times mostly based on Mode-Based Planner phase. As antic- ipated, increasing blocked space also increases the solution time as seen in Mostly-Blocked 5.3 Network Communication Two centralized computers (visual positioning and path planner) and robots’ computers runs on the system at the same time. These all computers are connected through a LAN network. Communication among the centralized PCs is performed with the physical ethernet cable while Centralized PCs and robots are connected with wireless network. Data communication between the units are demonstrated in Fig. 12. Testbed Network is based on a publish/subscribe architecture. To broadcast messages, sender publishes a message to all subscribers and receivers accepts only messages belongs to them according to head-tags of the messages. 5.4 Performing Low-Level Control Every robots have ability to run own low-level control algorithms. Outer loop control algorithm, a nonlinear trajectory controller, runs on robots’ own embedded Linux computers (Gumstix). To perform this algorithm, reference path is received from Path Planner PC while current configurations is received from Visual Positioning PC via wireless network. Accord- ing to position and orientation errors, trajectory controller evaluates the angular velocities of both two motors that leads the robot to track reference path. Evaluated angular velocities are sent to microcontroller (Robostix) as reference control variables through UART port. Robostix also counts the pulses of the optic encoders of the motors to evaluate current angular velocities. Received reference angular velocities and current angular velocities are compared and PWM signals are generated via PID controllers (as inner control loop) and then these PWM signals are sent to motor drivers. These both application is coded in C performs on Gumstix and Robostix. All these control architecture demonstrated in Fig. 14. Fig. 14. Control Architecture of the ITUCAL Robotic Testbed 6. Experiments and Simulation Results 6.1 Physical Hardware Demonstrations To demonstrate the applicability of the algorithms on physical systems, robot experiments have been implemented. In first group experiments on the ITU CAL Robotic Testbed, simplified version of the Modal-Maneuver Based PRM is used. On this application, two independently controlled primitive maneuver modes -straight forward mode and turning mode- are Connectivity Path B-Spline-Based Total Planner & Filtering Planner Time Single-Passage avr 0.440 s 0.206 s 0.646 s Problem std 0.287 s 0.011 s 0.293 s City-Like avr 0.977 s 0.326 s 1.303 s Environment std 0.935 s 0.254 s 0.930 s Mostly-Blocked avr 3.930 s 2.182 s 5.837 s Environment std 2.504 s 3.347 s 3.912 s MelCity Model avr 3.306 s 0.538 s 3.844 s Volume; 2 3 x std 1.528 s 0.650 s 1.212 s Table 3. Mode-Based Path Planer Construction Times (Seconds) environments, computational time of the B-Spline based planner phase is also rises. How- ever, this rising rate does not grow exponentially and computational times mostly based on Finding Connectivity Path phase. The complete solution times suggest that our method will be applicable for real-time implementations as the solution time is favorably comparable to implementation times. 7. Conclusion 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 genera- tion 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 explor- ing 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 (C 2 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. Fig. 17. Simulation example; Modal-Maneuver Based PRM Path Planer Construction Steps for City-Like Environment Connectivity Path Filtering Mode-Based Total Planner Phase Planner Time Single-Narrow time 0.350 s 0.032 s 32.706 s 33.089 s Passage std 0.231 s 0.008 s 17.699 s 17.732 s City-Like time 0.712 s 0.036 s 42.397 s 43.145 s Environment std 0.942 s 0.008 s 24.478 s 24.639 s Mostly-Blocked time 1.376 s 0.042 s 222.229 s 223.648 s Environment std 1.132 s 0.011 s 273.451 s 273.134 s Table 2. Modal-Based PRM Path Planer Construction Times (Seconds) environment test. However, this increasing rate does not grow exponentially according to percentage of obstacle space. Note that in this approach, modal inputs of the independently controlled modes directly obtained that can be used by low level control layers. Therefore, this approach may be seen slower than other path planner methods, but this method signifi- cantly decrease task-load of the low-level layers and should be compared with kinodynamic approaches. In the second group simulations, we tested the performance of Probabilistic B-Spline Tra- jectory Planning method on 3D environments. The computational times of steps of the algorithm are illustrated in Table 3 for 3D single-narrow-passage problem, city-like environment, mostly-blocked environment and MelCity model environment that has volume 2 3 times greater than the others. Fig. 18. Simulation example; Probabilistic B-Spline Path Planer Construction Steps for MelCity Model Environment On this approach, increasing complexity of the environment, as shown in Table 2, mainly increases computational time of the connectivity path that is implemented with a simplified version of RRT. Since repairing part of the algorithm is visited much more in planning complex Connectivity Path B-Spline-Based Total Planner & Filtering Planner Time Single-Passage avr 0.440 s 0.206 s 0.646 s Problem std 0.287 s 0.011 s 0.293 s City-Like avr 0.977 s 0.326 s 1.303 s Environment std 0.935 s 0.254 s 0.930 s Mostly-Blocked avr 3.930 s 2.182 s 5.837 s Environment std 2.504 s 3.347 s 3.912 s MelCity Model avr 3.306 s 0.538 s 3.844 s Volume; 2 3 x std 1.528 s 0.650 s 1.212 s Table 3. Mode-Based Path Planer Construction Times (Seconds) environments, computational time of the B-Spline based planner phase is also rises. How- ever, this rising rate does not grow exponentially and computational times mostly based on Finding Connectivity Path phase. The complete solution times suggest that our method will be applicable for real-time implementations as the solution time is favorably comparable to implementation times. 7. Conclusion 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 genera- tion 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 explor- ing 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 (C 2 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. Fig. 17. Simulation example; Modal-Maneuver Based PRM Path Planer Construction Steps for City-Like Environment Connectivity Path Filtering Mode-Based Total Planner Phase Planner Time Single-Narrow time 0.350 s 0.032 s 32.706 s 33.089 s Passage std 0.231 s 0.008 s 17.699 s 17.732 s City-Like time 0.712 s 0.036 s 42.397 s 43.145 s Environment std 0.942 s 0.008 s 24.478 s 24.639 s Mostly-Blocked time 1.376 s 0.042 s 222.229 s 223.648 s Environment std 1.132 s 0.011 s 273.451 s 273.134 s Table 2. Modal-Based PRM Path Planer Construction Times (Seconds) environment test. However, this increasing rate does not grow exponentially according to percentage of obstacle space. Note that in this approach, modal inputs of the independently controlled modes directly obtained that can be used by low level control layers. Therefore, this approach may be seen slower than other path planner methods, but this method signifi- cantly decrease task-load of the low-level layers and should be compared with kinodynamic approaches. In the second group simulations, we tested the performance of Probabilistic B-Spline Tra- jectory Planning method on 3D environments. The computational times of steps of the algorithm are illustrated in Table 3 for 3D single-narrow-passage problem, city-like environment, mostly-blocked environment and MelCity model environment that has volume 2 3 times greater than the others. Fig. 18. Simulation example; Probabilistic B-Spline Path Planer Construction Steps for MelCity Model Environment On this approach, increasing complexity of the environment, as shown in Table 2, mainly increases computational time of the connectivity path that is implemented with a simplified version of RRT. Since repairing part of the algorithm is visited much more in planning complex and Its Applications. IROS ’89. Proceedings., IEEE/RSJ International Workshop on pp. 398 – 405. Koyuncu, E. & Inalhan, G. (2008). A probabilistic b-spline motion planning algorithm for unmanned helicopters flying in dense 3d environments, Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on pp. 815 – 821. Koyuncu, E., Ure, N. K. & Inalhan, G. (2008). A probabilistic algorithm for mode based motion planning of agile unmanned air vehicles in complex environments, Int. Federation of Automatic Control(IFAC’08) World Congress . Koyuncu, E., Ure, N. K. & Inalhan, G. (2009). Integration of path/maneuver planning in complex environments for agile maneuvering ucavs, Proc. 2th Int. Symposium on Un- manned Aerial Vehicles (UAV’09) . LaValle, S. & Kuffner, J. (1999). Randomized kinodynamic planning, Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on 1: 473 – 479 vol.1. Munoz, V., Ollero, A., Prado, M. & Simon, A. (1994). Mobile robot trajectory planning with dynamic and kinematic constraints, Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on pp. 2802 – 2807 vol.4. Nikolos, I., Valavanis, K., Tsourveloudis, N. & Kostaras, A. (2003). Evolutionary algorithm based offline/online path planner for uav navigation, Systems, Man, and Cybernetics, Part B, IEEE Transactions on 33(6): 898–912. Paulos, E. (1998). On-line collision avoidance for multiple robots using b-splines, University of California Berkeley Computer Science Division (EECS) Technical Report 98-977. Piegl, L. A. & Tiller, W. (1997). The NURBS Bookâ ˘ A ˝ O, Springer-Verlag New York, Inc. Schouwenaars, T., Feron, E. & How, J. (2004). Hybrid model for receding horizon guidance of agile autonomous rotorcraft, IFAC Symposium on Automatic Control . Song, G. & Amato, N. (2001). Randomized motion planning for car-like robots with c-prm, Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on 1: 37 – 42 vol.1. Ure, N. & Inalhan, G. (2008). Design of higher order sliding mode control laws for multi modal agile maneuvering ucavs, 2nd Int. Symposium on Systems and Controls in Aerospace . Ure, N. & Inalhan, G. (2009). Design of a multi modal control framework for agile maneuvering ucavs, IEEE Aerospace Conference . Vazquez, G. B., Sossa, A. H. & de Leon, S. J. L. D. (1994). Auto guided vehicle control using expanded time b-splines, Systems, Man, and Cybernetics,Humans, Information and Technology, IEEE International Conference on 3: 2786–2791 vol. 3. One of the limitations of the algortihm is on very narrow passages, which require aircraft to tilt considerably to avoid collision. In the problems we have examined distance between obstacles are far wider compared to wing span of the aircraft so we didn’t include this case. One of the possible future works is to handle these extreme cases. Moreover, extension of the algorithms presented to UAV fleets is another natural application of this work. 8. References Bayazit, O. B., Xie, D. & Amato, N. M. (2005). Iterative relaxation of constraints: a framework for improving automated motion planning, Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on pp. 3433–3440. Bohlin, R. & Kavraki, L. E. (2001). A randomized algorithm for robot path planning based on lazy evaluation, Handbook on Randomized Computing, Kluwer Academic Publishers, p.221-249 (2001) pp. 221–249. Boor, V., Overmars, M. H. & van der Stappen, A. F. (1999). The gaussian sampling strategy for probabilistic roadmap planners, IEEE International Conference on Robotics & Automa- tion p. 6. Clark, C. M., Rock, S. & Latombe, J C. (2003). Dynamic networks for motion planning in multi-robot space systems, p. 8. Dyllong, E. & Visioli, A. (2003). Planning and real-time modifications of a trajectory using spline techniques, Robotica 21(5): 475–482. Frazzoli, E., Dahleh, M. A. & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles, AIAA Journal of Guidance and Control 25(1): 116–129. Ghosh, R. & Tomlin, C. (2000). Nonlinear inverse dynamic control for mode-based flight, AIAA Guidance, Navigation and Control Conference . Hsu, D. (2000). Randomized single-query motion planning in expansive spaces, PhD Thesis p. 134. Hsu, D., Jiang, T., Reif, J. & Sun, Z. (2003). The bridge test for sampling narrow passages with probabilistic roadmap planners, IEEE International Conference on Robotics & Automa- tion . Hsu, D., Kavraki, L. E., Latombe, J C., Motwani, R. & Sorkin, S. (1998). On finding narrow passages with probabilistic roadmap planners, International Workshop on Algorithmic Foundations of Robotics pp. 141 – 153. Hsu, D., Kindel, R., Latombe, J C. & Rock, S. (2002). Randomized kinodynamic motion planning with moving obstacles, International Journal of Robotics Research 21(2): 233 – 255. Hsu, D., Latombe, J C. & Motwani, R. (1999). Path planning in expansive configuration spaces, International Journal Computational Geometry and Applications 4: 495–512. Inalhan, G., Stipanovic, D. & Tomlin, C. (2002). Decentralized optimization, with application to multiple aircraft coordination, Decision and Control, 2002, Proceedings of the 41st IEEE Conference on 1: 1147–1155 vol.1. Kavraki, L., Svestka, P., Latombe, J. & Overmars, M. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces, Robotics and Automation, IEEE Transactions on 12(4): 566 – 580. Kindel, R., Hsu, D., claude Robert, J. & Latombe, S. (2000). Randomized kinodynamic motion planning with moving obstacles, The International Journal of Robotics Research 21(3): 233–255. Komoriya, K. & Tanie, K. (1989). Trajectory design and control of a wheel-type mobile robot using b-spline curve, Intelligent Robots and Systems ’89. The Autonomous Mobile Robots and Its Applications. IROS ’89. Proceedings., IEEE/RSJ International Workshop on pp. 398 – 405. Koyuncu, E. & Inalhan, G. (2008). A probabilistic b-spline motion planning algorithm for unmanned helicopters flying in dense 3d environments, Intelligent Robots and Systems, 2008. IROS 2008. IEEE/RSJ International Conference on pp. 815 – 821. Koyuncu, E., Ure, N. K. & Inalhan, G. (2008). A probabilistic algorithm for mode based motion planning of agile unmanned air vehicles in complex environments, Int. Federation of Automatic Control(IFAC’08) World Congress . Koyuncu, E., Ure, N. K. & Inalhan, G. (2009). Integration of path/maneuver planning in complex environments for agile maneuvering ucavs, Proc. 2th Int. Symposium on Un- manned Aerial Vehicles (UAV’09) . LaValle, S. & Kuffner, J. (1999). Randomized kinodynamic planning, Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on 1: 473 – 479 vol.1. Munoz, V., Ollero, A., Prado, M. & Simon, A. (1994). Mobile robot trajectory planning with dynamic and kinematic constraints, Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on pp. 2802 – 2807 vol.4. Nikolos, I., Valavanis, K., Tsourveloudis, N. & Kostaras, A. (2003). Evolutionary algorithm based offline/online path planner for uav navigation, Systems, Man, and Cybernetics, Part B, IEEE Transactions on 33(6): 898–912. Paulos, E. (1998). On-line collision avoidance for multiple robots using b-splines, University of California Berkeley Computer Science Division (EECS) Technical Report 98-977. Piegl, L. A. & Tiller, W. (1997). The NURBS Bookâ ˘ A ˝ O, Springer-Verlag New York, Inc. Schouwenaars, T., Feron, E. & How, J. (2004). Hybrid model for receding horizon guidance of agile autonomous rotorcraft, IFAC Symposium on Automatic Control . Song, G. & Amato, N. (2001). Randomized motion planning for car-like robots with c-prm, Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on 1: 37 – 42 vol.1. Ure, N. & Inalhan, G. (2008). Design of higher order sliding mode control laws for multi modal agile maneuvering ucavs, 2nd Int. Symposium on Systems and Controls in Aerospace . Ure, N. & Inalhan, G. (2009). Design of a multi modal control framework for agile maneuvering ucavs, IEEE Aerospace Conference . Vazquez, G. B., Sossa, A. H. & de Leon, S. J. L. D. (1994). Auto guided vehicle control using expanded time b-splines, Systems, Man, and Cybernetics,Humans, Information and Technology, IEEE International Conference on 3: 2786–2791 vol. 3. One of the limitations of the algortihm is on very narrow passages, which require aircraft to tilt considerably to avoid collision. In the problems we have examined distance between obstacles are far wider compared to wing span of the aircraft so we didn’t include this case. One of the possible future works is to handle these extreme cases. Moreover, extension of the algorithms presented to UAV fleets is another natural application of this work. 8. References Bayazit, O. B., Xie, D. & Amato, N. M. (2005). Iterative relaxation of constraints: a framework for improving automated motion planning, Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on pp. 3433–3440. Bohlin, R. & Kavraki, L. E. (2001). A randomized algorithm for robot path planning based on lazy evaluation, Handbook on Randomized Computing, Kluwer Academic Publishers, p.221-249 (2001) pp. 221–249. Boor, V., Overmars, M. H. & van der Stappen, A. F. (1999). The gaussian sampling strategy for probabilistic roadmap planners, IEEE International Conference on Robotics & Automa- tion p. 6. Clark, C. M., Rock, S. & Latombe, J C. (2003). Dynamic networks for motion planning in multi-robot space systems, p. 8. Dyllong, E. & Visioli, A. (2003). Planning and real-time modifications of a trajectory using spline techniques, Robotica 21(5): 475–482. Frazzoli, E., Dahleh, M. A. & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles, AIAA Journal of Guidance and Control 25(1): 116–129. Ghosh, R. & Tomlin, C. (2000). Nonlinear inverse dynamic control for mode-based flight, AIAA Guidance, Navigation and Control Conference . Hsu, D. (2000). Randomized single-query motion planning in expansive spaces, PhD Thesis p. 134. Hsu, D., Jiang, T., Reif, J. & Sun, Z. (2003). The bridge test for sampling narrow passages with probabilistic roadmap planners, IEEE International Conference on Robotics & Automa- tion . Hsu, D., Kavraki, L. E., Latombe, J C., Motwani, R. & Sorkin, S. (1998). On finding narrow passages with probabilistic roadmap planners, International Workshop on Algorithmic Foundations of Robotics pp. 141 – 153. Hsu, D., Kindel, R., Latombe, J C. & Rock, S. (2002). Randomized kinodynamic motion planning with moving obstacles, International Journal of Robotics Research 21(2): 233 – 255. Hsu, D., Latombe, J C. & Motwani, R. (1999). Path planning in expansive configuration spaces, International Journal Computational Geometry and Applications 4: 495–512. Inalhan, G., Stipanovic, D. & Tomlin, C. (2002). Decentralized optimization, with application to multiple aircraft coordination, Decision and Control, 2002, Proceedings of the 41st IEEE Conference on 1: 1147–1155 vol.1. Kavraki, L., Svestka, P., Latombe, J. & Overmars, M. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces, Robotics and Automation, IEEE Transactions on 12(4): 566 – 580. Kindel, R., Hsu, D., claude Robert, J. & Latombe, S. (2000). Randomized kinodynamic motion planning with moving obstacles, The International Journal of Robotics Research 21(3): 233–255. Komoriya, K. & Tanie, K. (1989). Trajectory design and control of a wheel-type mobile robot using b-spline curve, Intelligent Robots and Systems ’89. The Autonomous Mobile Robots [...]... coordinate system with linear and angular velocity components In (1), U, V, W, and P, Q, R are x, y, z components of linear and angular velocities , M, and I represent displacement, mass, and mass moments or products of inertia of a vehicle  and g are constants expressing water density and gravitational acceleration Hydrodynamic forces and moments are represented by X, Y, Z, and L, M, N, each of which... Guidance, Control, and Dynamics, Vol 24, No 5, (September 2001) 1029–1031 Kim, K & Ura, T (2009) Optimal Guidance for Autonomous Underwater Vehicle Navigation within Undersea Areas of Current Disturbances Advanced Robotics, Vol 23, No 5, (April 2009) 601–628 19 X Towards multimodal interface for interactive robots: challenges and robotic systems description 1 Burger Brice1,2,3, Ferrané Isabelle23 and Lerasle... well used in such areas and for a variety of tasks like elderly people care, or helping handicapped people as well as assistance in factories or offices Such prospects require both spatial and transactional intelligence The former is based on environment perception capabilities For a robot, this means "being able to understand and navigate in its environment; locating objects and knowing how to manipulate... law presented by Bryson and Ho (1979), shown in (6) v u 1  u v  (6)    sin 2 c   c - c  sin2 - cos 2 c x 2  x y  y   In (6),  represents the vehicle heading, and uc, vc are x, y components of the sea current velocity Though Bryson and Ho derived (6) on the assumption of the stationary flow condition, we have shown that it is also valid for time-varying currents, like tidal flows... a Shearing Flow The first numerical example in this research is an optimal navigation in a current disturbance of the linear shear flow, taken from Bryson and Ho (1979) The current velocity in this problem is described by u c (x, y)  0 vc (x, y)  -U c x/h (10a) (10b) where Uc and h are set to be 1.544 m/s and 100 m, respectively Starting from the initial position at (x0 , y0 )  (-186 m , 366 m)... most of currents the direction and the magnitude of their velocities change continuously like tidal flows As mentioned previously, the optimal guidance law (6) is also valid for time-varying current flows as well as the stationary ones Therefore, once the flow velocity distribution in a navigation region is described as a function of the position and time, our numerical scheme equally works and realizes... stability and control analysis, hydrodynamic loads are expanded and linearized on the assumption that they are functions of the instantaneous values of the perturbed velocities, control inputs, and their derivatives Thus the expressions of the hydrodynamic loads are obtained in the form of a Taylor series in these variables, which is linearized by discarding all the higher-order terms For example, X is expanded... R-One is completed It is generally known and also noticeable from (4) that according to the coupling relation, linearized equations of motion are to be split into two independent groups: the longitudinal equations including surge, heave, and pitch, and the lateral equations including sway, roll, and yaw (McRuer et al., 1990; Etkin, 1982) In Table 1, longitudinal and lateral stability derivatives appearing... process, namely gesture recognition, speech understanding and late stage multimodal fusion are described in section 2 Next, for a global evaluation, live experiments carried out on JIDO in the context of interactive manipulation tasks are described Finally, this leads us to give some prospects and future work to be done on this topic 2 Description of JIDO and its multimodal interface This section gives... tracked in 3D in stereoscopic video stream thanks to interactively distributed particle filters devoted to the human's hands and head (Qu et al., 2007) Recent investigations concern the second phase, i.e the classification of legitimate gestures These gestures are here assumed to start and end in the same natural/rest position (the hands lying along the thighs) Given an isolated gesture segment, classification . evaluate current angular velocities. Received reference angular velocities and current angular velocities are compared and PWM signals are generated via PID controllers (as inner control loop) and. evaluate current angular velocities. Received reference angular velocities and current angular velocities are compared and PWM signals are generated via PID controllers (as inner control loop) and. with linear and angular velocity components. In (1), U, V, W, and P, Q, R are x, y, z components of linear and angular velocities.  , M, and I represent displacement, mass, and mass moments

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