| Περίληψη: | This dissertation addresses the control of networked autonomous mobile robots and its experimental evaluation. The mobile robots are deployed either as a swarm or as standalone agents in a convex region, or a general non-convex region with obstacles. The topics that we examine are trajectory planning, motion control, obstacle avoidance, area coverage and homing. The fundamental contributions of this dissertation are as follows. Firstly, an automated piecewise optimal trajectory generation method for the mobile robots is developed. Secondly, an obstacleaware motion controller for the mobile robots is developed. Thirdly, a visibility based cell assignment method is proposed and used for the derivation of optimal distributed control schemes for the tasks of area coverage and homing in non-convex regions. Apart from that, each task is evaluated on a network of real robotic vehicles, where the robots are controlled in real time by properly exploiting computational geometry tools.
The experiments are conducted using mobile robots with differential drive, with the support of a self-developed external localization system. This camera based localization system is implemented in an augmented reality framework and is included in the network of the experimental setup as the main way of retrieving the position and orientation of the robots in the region of interest. The resulting position and orientation of each robot is combined with its encoder measurements in order to obtain its full state. Additionally, since the effective control of the robots is an absolute necessity, the kinodynamic model of the differential drive robot along with its kinematic constraints are considered throughout the dissertation.
The most straightforward method of controlling a real robot with respect to its model is through trajectory planning. After defining the trajectory function and determining the required properties, we propose an automated time-optimal trajectory generation method, considering a given set of points to be traversed. Based on the initial pose and velocity of the robot, our method connects the points of interest in sequence by cubic Bezier segments. Using non-linear constrained optimization, these segments are time-optimized under the velocity and acceleration constraints of the robot. The optimization problem is simplified through root-finding and evaluation of polynomials and results in an applicable, smooth trajectory, that exploits the dynamic capabilities of the robot as much as possible.
Even though trajectories can easily incorporate the robot dynamics, they are not an efficient way of controlling the robots of a swarm, where the motion of the robots is determined by their interactions. In most tasks of swarm robotics, the high level controller of each robot provides a desirable movement direction, thus a motion controller is needed to translate this direction into the actual control input of the robot. As a first step, we designed a region unaware motion controller that drives the robot as fast as possible towards a desired direction, considering the dynamic constraints, the sampling interval and the maximum traverse distance. This controller is further extended into a region aware controller that, apart from the dynamics and constraints, considers the robot dimensions as well, in order to avoid the obstacles in the visibility field of the robot. The performance of these controllers is evaluated in experimental studies, through the task of position control, in a convex and in a non-convex region, where the movement direction is determined by the shortest path connecting the robot to its target.
Finally, relying on the aforementioned motion controllers, we examined the distributed control of multi-robot configurations. After experimental evaluation of certain already developed area coverage schemes for convex regions, we turned our focus towards the distributed control in non-convex regions with obstacles. Initially, a method of assigning parts of the non-convex region to the robots was developed, where the parts in the vicinity of each robot are determined in terms of the visibility notion. Based on that, optimal spatially distributed coordination algorithms were successfully derived for the homing problem, where the robots should reach some known locations, as well as the area coverage problem, after properly expressing each of them by an aggregate objective function.
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