Abstract
Cloud robotics can largely enhance the robot intelligence by offloading tasks to the cloud dynamically. However, the robots differ in their own hardware configuration such as battery and processing capacity, while the transmission frames are also a mixture of different quality of service (QoS) requirements. As the competition for limited channel resource is inevitable, how to optimize the system performance by effective resource allocation is a key problem. The paper proposes a two-tier hierarchical-based MAC (Two-Tier MAC) which means the classification exists not only in frames but also in robots. The Lyapunov optimization technique is used to maximize the time-averaged quality satisfaction. The experiments show the superior performance of the Two-Tier MAC compared with other MAC protocols especially in overloaded networks. Meanwhile, the system also presents a longer lifetime because the Two-Tier MAC takes energy balance into consideration.
Highlights
Robotics is evolving from a single platform to cluster collaboration for the applications with high complexity, uncertainty, and real time
Where B = Ef∑Nn=11⁄2R2nðtÞ + μ2nðtÞ/2g is defined as a finite constant to simplify the inequation above, and V is a nonnegative constant parameter that controls the trade-off between drift ΔLðtÞ and satisfaction function
According to Equation (12), P1 can be decoupled into two subproblems: the first one is the admission control (AC) related to RðtÞ, which means the new frames will be rejected if the queue is full
Summary
Robotics is evolving from a single platform to cluster collaboration for the applications with high complexity, uncertainty, and real time. Robots share information and cooperate with each other by a decentralized wireless network, the intelligence is still limited by their own hardware configuration. For enhancing the survival ability in complex environment, robots adopt a decentralized ad hoc network and contend for communication channel in the carrier sense multiple access/collision avoidance (CSMA/CA) mode. The Lyapunov optimization technique as well as isolated time slots and admission control is adopted to maximize the system utility even in poor network condition. All frames are divided into different priorities waiting in corresponding queues with isolated time slots to reduce collision consumption. Frames in the same queue but from different robots are allocated diverse accessing probability to ensure the energy balance (ii) The system optimization can be decoupled into two independent issues by the Lyapunov method.
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