Dual-mode dynamic window approach to robot navigation with convergence guarantees

Abstract

In this paper, a novel, dual-mode model predictive control framework is introduced that combines the dynamic window approach to navigation with reference tracking controllers. This adds a deliberative component to the obstacle avoidance guarantees present in the dynamic window approach as well as allow for the inclusion of complex robot models. The proposed algorithm allows for guaranteed convergence to a goal location while navigating through an unknown environment at relatively high speeds. The framework is applied in both simulation and hardware implementation to demonstrate the computational feasibility and the ability to cope with dynamic constraints and stability concerns. *

Adaptive Weighted Guided Image Filtering for Depth Enhancement in Shape-From-Focus

“…In addition, it allows for a more compact electronic system. Therefore, it is widely used to acquire depth information of various scenarios, such as synthetic focus, autonomousc navigation [1,2], 3D parallax [3], microelectronics [4], industrial inspection [5], augmented or virtual reality (AR/VR) on mobile devices [6,7], etc.…”

Secure practical tracking of an uncertain robotic system under coarse conditions on reference trajectory and obstacles

“…Specifically, a robotic manipulator is investigated in [19] without any uncertainties since unknown system parameters and disturbance are all excluded. In [20][21][22][23][24][25][26][27], several mobile robots are considered but external disturbance is excluded in [22][23][24][25][26][27] while the system parameters are completely or partially known in [20,21,24]. Moreover, some robotic systems described by general Euler-Lagrange equations or non-linear system are considered in [28][29][30], but the system matrices (or parameters) therein must be partially known while the disturbance has a known bound or must be matched with control input.…”

“…Besides the restriction on system uncertainties, the existing results on this topic are constrained by the generality of the reference trajectories as well as the measurability of the reference trajectories and obstacles. Specifically, the reference trajectories must be at least second-order continuously differentiable in [23,25,28,30] and hence exclude a large class of ones which are just first-order continuously differentiable or even non-smooth. In practice, the reference trajectories may be given by some moving objects whose trajectories are usually undesirable, and hence may be only first-order continuously differentiable or even nonsmooth.…”

“…(i) More serious system uncertainties are allowed. Different from [19][20][21][22][23][24][25][26][27][28][29][30] where system uncertainties are constrained (as aforementioned), this article allows all the non-linear dynamic matrices and mismatched disturbance to be unknown while without known bound, and hence allows more serious uncertainties. (ii) Coarse conditions are needed on reference trajectory and obstacles.…”

“…(iii) A wider class of the reference trajectories can be followed under obstacle-avoidance. The reference trajectories to be followed in the article are not necessarily to be second-order continuously differentiable, and hence are more general than those of [23,25,28,30] for trajectory tracking joint with obstacle-avoidance, even than those of [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] for trajectory tracking regardless of obstacle-avoidance.…”