In this paper, the development of an unmanned autonomous forklift is discussed. A system configuration using vision, laser ranger finder, sonar, etc. for autonomous navigation is presented. The kinematics of a spin-turn mechanism is analyzed first, and then the obtained kinematics equations are transformed to the equations represented by path variables. These equations are nonlinear state equations to be used for control purposes. A time varying feedback control law via the chained form of Murray and Sastry [12] is derived. The effectiveness of the proposed control law is examined through simulations and experiments.

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Summary This paper examines a codesign problem in industrial networked control systems (NCS) whereby physical systems are controlled over wireless fading channels. The considered wireless channels are assumed to be stochastically dependent on the physical states of moving machineries in the industrial working space. In this paper, the moving machineries are modeled as Markov decision processes whereas the characteristics of the correlated fading channels are modeled as a binary random process whose probability measure depends on both the physical states of moving machineries and the transmission power of communication channels. Under such a state‐dependent fading channel model, sufficient conditions to ensure the stochastic safety of the NCS are first derived. Using the derived safety conditions, a codesign problem is then formulated as a constrained joint optimization problem that seeks for optimal control and transmission power policies which simultaneously minimize an infinite time cost on both communication resource and control effort. This paper shows that such optimal policies can be obtained in a computationally efficient manner using convex programming methods. Simulation results of an autonomous forklift truck and a networked DC motor system are presented to illustrate the advantage and efficacy of the proposed codesign framework for industrial NCS.