Mobile robot navigation has remained an open problem over the last two decades. Mobile robots are required to navigate in unknown and dynamic environments, and in recent years the use of mobile robots in material handling has considerably increased. Usually workers push carts around warehouses and manually handle orders which is not very cost-effective. To this end, a potential method to control a swarm of mobile robots in a warehouse with static and dynamic obstacles is to use the wireless control approach. Further, to be able to control different types of mobile robots in the warehouse, the fuzzy logic control approach has been chosen. Therefore, in this paper, an on-line navigation technique for a wheeled mobile robot (WMR) in an unknown dynamic environment using fuzzy logic techniques is investigated. In this paper, we aim to use the robot in application in a warehouse. Experimental results show the effectiveness of the proposed algorithm.

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Three nonlinear continuous-time predictive control schemes are proposed to address the trajectory tracking control problem of rigid link robot manipulators. The control laws using state variable feedback minimize a quadratic performance index of the state predicted tracking error. Without online optimization, an asymptotic tracking of smooth reference trajectories is guaranteed. The proposed controllers achieve the positions and speed tracking objectives via link position measurements. The Lyapunov theory is used to prove the boundedness and stability convergence of the state tracking. Robustness with respect to payload uncertainties and viscous friction is shown. Simulations for a two-link rigid robot are performed to validate the proposed controller.

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D* based navigation algorithms provide robust and realtime means of achieving path planning in dynamic environments. Author of this paper introduces a notion of predictable time-based obstacles. The algorithm proposed in the paper defines a centralized obstacle-map that is shared among multiple agents (robots) performing path planning. Each robot plans its path individually on an obstacle-map using a slightly modified version of D* Lite and then shares an updated version of the map, which includes its planned path as a new obstacle, with its peers. The planned paths appear as temporary time-based obstacles to peer robots. Planned paths are divided into discrete temporal sections so as to help peer robots optimize paths temporally. The proposed algorithm also presents a priority measure which helps us decide the optimized sequence of individual pathplanning order followed by cooperating robots. Since the implemented algorithm is tested in simulation using Mobile robot Programming Toolkit, the Real-time performance analysis is done to confirm the real-time execution time of the proposed algorithm.

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In this paper, we propose a computational trajectory generation algorithm for swarm mobile robots using local information in a dynamic environment. The algorithm plans a reference path based on constrained convex nonlinear optimization which avoids both static and dynamic obstacles. This algorithm is combined with one-step-ahead predictive control for a swarm of mobile robots to track the generated paths and reach the goals without collision. The numerical simulations and experimental results demonstrate the effectiveness of the proposed free-collision path planning algorithm.

The approximate nonlinear receding-horizon control law is used to treat the trajectory tracking control problem of rigid link robot manipulators. The derived nonlinear predictive law uses a quadratic performance index of the predicted tracking error and the predicted control effort. A key feature of this control law is that, for their implementation, there is no need to perform an online optimization, and asymptotic tracking of smooth reference trajectories is guaranteed. It is shown that this controller achieves the positions tracking objectives via link position measurements. The stability convergence of the output tracking error to the origin is proved. To enhance the robustness of the closed loop system with respect to payload uncertainties and viscous friction, an integral action is introduced in the loop. A nonlinear observer is used to estimate velocity. Simulation results for a two-link rigid robot are performed to validate the performance of the proposed controller.