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Robotics, Volume 3, Issue 4 (December 2014) – 4 articles , Pages 330-416

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1869 KiB  
Article
Drive the Drive: From Discrete Motion Plans to Smooth Drivable Trajectories
by Henrik Andreasson, Jari Saarinen, Marcello Cirillo, Todor Stoyanov and Achim J. Lilienthal
Robotics 2014, 3(4), 400-416; https://doi.org/10.3390/robotics3040400 - 11 Dec 2014
Cited by 102 | Viewed by 6597
Abstract
Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic [...] Read more.
Autonomous navigation in real-world industrial environments is a challenging task in many respects. One of the key open challenges is fast planning and execution of trajectories to reach arbitrary target positions and orientations with high accuracy and precision, while taking into account non-holonomic vehicle constraints. In recent years, lattice-based motion planners have been successfully used to generate kinematically and kinodynamically feasible motions for non-holonomic vehicles. However, the discretized nature of these algorithms induces discontinuities in both state and control space of the obtained trajectories, resulting in a mismatch between the achieved and the target end pose of the vehicle. As endpose accuracy is critical for the successful loading and unloading of cargo in typical industrial applications, automatically planned paths have not been widely adopted in commercial AGV systems. The main contribution of this paper is a path smoothing approach, which builds on the output of a lattice-based motion planner to generate smooth drivable trajectories for non-holonomic industrial vehicles. The proposed approach is evaluated in several industrially relevant scenarios and found to be both fast (less than 2 s per vehicle trajectory) and accurate (end-point pose errors below 0.01 m in translation and 0.005 radians in orientation). Full article
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563 KiB  
Article
The Role of Visibility in Pursuit/Evasion Games
by Athanasios Kehagias, Dieter Mitsche and Paweł Prałat
Robotics 2014, 3(4), 371-399; https://doi.org/10.3390/robotics3040371 - 08 Dec 2014
Cited by 9 | Viewed by 4961
Abstract
The cops-and-robber (CR) game has been used in mobile robotics as a discretized model (played on a graph G) of pursuit/evasion problems. The “classic” CR version is a perfect information game: the cops’ (pursuer’s) location is always known to the robber (evader) [...] Read more.
The cops-and-robber (CR) game has been used in mobile robotics as a discretized model (played on a graph G) of pursuit/evasion problems. The “classic” CR version is a perfect information game: the cops’ (pursuer’s) location is always known to the robber (evader) and vice versa. Many variants of the classic game can be defined: the robber can be invisible and also the robber can be either adversarial (tries to avoid capture) or drunk (performs a random walk). Furthermore, the cops and robber can reside in either nodes or edges of G. Several of these variants are relevant as models or robotic pursuit/evasion. In this paper, we first define carefully several of the variants mentioned above and related quantities such as the cop number and the capture time. Then we introduce and study the cost of visibility (COV), a quantitative measure of the increase in difficulty (from the cops’ point of view) when the robber is invisible. In addition to our theoretical results, we present algorithms which can be used to compute capture times and COV of graphs which are analytically intractable. Finally, we present the results of applying these algorithms to the numerical computation of COV. Full article
(This article belongs to the Special Issue Coordination of Robotic Systems)
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925 KiB  
Article
Adaptive Neuro-Fuzzy Technique for Autonomous Ground Vehicle Navigation
by Auday Al-Mayyahi, William Wang and Phil Birch
Robotics 2014, 3(4), 349-370; https://doi.org/10.3390/robotics3040349 - 19 Nov 2014
Cited by 48 | Viewed by 11108
Abstract
This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV [...] Read more.
This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles. Full article
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483 KiB  
Article
Robust Nonlinear Regulation of Limit Cycle Oscillations in UAVs Using Synthetic Jet Actuators
by Natalie Ramos Pedroza, William MacKunis and Vladimir V. Golubev
Robotics 2014, 3(4), 330-348; https://doi.org/10.3390/robotics3040330 - 26 Sep 2014
Cited by 20 | Viewed by 6001
Abstract
In this paper, a synthetic jet actuators (SJA)-based nonlinear robust controller is developed, which is capable of completely suppressing limit cycle oscillations (LCO) in UAV systems with parametric uncertainty in the SJA dynamics and unmodeled external disturbances. Specifically, the control law compensates for [...] Read more.
In this paper, a synthetic jet actuators (SJA)-based nonlinear robust controller is developed, which is capable of completely suppressing limit cycle oscillations (LCO) in UAV systems with parametric uncertainty in the SJA dynamics and unmodeled external disturbances. Specifically, the control law compensates for uncertainty in an input gain matrix, which results from the unknown airflow dynamics generated by the SJA. Challenges in the control design include compensation for input-multiplicative parametric uncertainty in the actuator dynamic model. The result was achieved via innovative algebraic manipulation in the error system development, along with a Lyapunov-based robust control law. A rigorous Lyapunov-based stability analysis is utilized to prove asymptotic LCO suppression, considering a detailed dynamic model of the pitching and plunging dynamics. Numerical simulation results are provided to demonstrate the robustness and practical performance of the proposed control law. Full article
(This article belongs to the Special Issue Advances in Biomimetic Robotics)
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