A Real World Coordination Framework for Connected Heterogeneous Robotic Systems
In this paper we consider the problem of coordinating robotic systems with different kinematics, sensing and vision capabilities, to achieve certain mission goals. An approach that makes use of a heterogeneous team of agents has several advantages when cost, integration of capabilities, or possible large search areas need to be considered. A heterogeneous team allows for the robots to become “specialized”, accomplish sub-goals more effectively, thus increasing the overall mission efficiency. We consider connectivity constraints and realistic communication, exploiting mobility to implement a power control algorithm that increases the Signal to Interference plus Noise Ratio (SINR) among certain members of the network. We also create realistic sensing fields and manipulation by using the geometric properties of the sensor field-of-view and the manipulability metric, respectively. The control strategy for each agent of the heterogeneous system is governed by an artificial physics law that considers the different kinematics of the agents and the environment, in a decentralized fashion. We show that the network is able to stay connected at all times and covers the environment well. We demonstrate the applicability of the proposed strategy through simulation results implementing a pursuit-evasion game in a cluttered environment.
KeywordsCognitive Radio Mobile Agent Obstacle Avoidance Mobile Sensor Search Mode
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