1 research outputs found
Learning-based Intelligent Attack against Mobile Robots with Obstacle-avoidance
The security issue of mobile robots have attracted considerable attention in
recent years. Most existing works focus on detection and countermeasures for
some classic attacks from cyberspace. Nevertheless, those work are generally
based on some prior assumptions for the attacker (e.g., the system dynamics is
known, or internal access is compromised). A few work are delicated to physical
attacks, however, there still lacks certain intelligence and advanced control
design. In this paper, we propose a physical-based and intelligent attack
framework against the obstacle-avoidance of mobile robots. The novelty of our
work lies in the following: i) Without any prior information of the system
dynamics, the attacker can learn the detection area and goal position of a
mobile robot by trial and observation, and the obstacle-avoidance mechanism is
learned by support vector regression (SVR) method; ii) Considering different
attack requirements, different attack strategies are proposed to implement the
attack efficiently; iii) The framework is suitable for holonomic and
non-holonomic mobile robots, and the algorithm performance analysis about time
complexity and optimality is provided. Furthermore, the condition is obtained
to guarantee the success of the attack. Simulations illustrate the
effectiveness of the proposed framework