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In this paper, it is studied a proposal of control signal compensation of nonlinear systems under feedback linearization. An important problem in this context appears when the nonlinear system has differences (or uncertainties) with respect to the nonlinear nominal model used to design the feedback linearization control law. In this case, the practical system in closed-loop can become even unstable...
A planning and control methodology for manipulating passive objects using orbital servicers in zero gravity has been developed by the authors. In this work, a parametric sensitivity analysis of the proposed model-based control for the motion of the passive object, in terms of parametric uncertainties, is presented. A linearization methodology is used to provide a scheme with which the controller robust...
We present a novel statistical model-based control algorithm, called Control in the Reliable Region of a Statistical Model (CRROS). A statistical model is unreliable when its state passes into a region where training data is sparse. CRROS drives the state away from such an unreliable region while pursuing the desired output by taking advantage of the redundancy in the input-output relationships. We...
In this paper we present results from a user evaluation of a robot bartender system which handles state uncertainty derived from speech input by using belief tracking and generating appropriate clarification questions. We present a combination of state estimation and action selection components in which state uncertainty is tracked and exploited, and compare it to a baseline version that uses standard...
In this paper, a nonlinear tracking control of a robot arm in the presence of uncertainties is proposed by using robust right coprime factorization and sliding mode approaches. In general, there exist unknown modeling errors in measuring structural parameters of the robot arm and external disturbances in real situations. In the present control system design, the effect of the modeling errors and disturbance...
In this paper, a robust constant thrust collision avoidance maneuver approach is proposed based on the relative motion dynamic model. First, the design problem is cast into a convex optimization problem by introducing a Lyapunov function subject to linear matrix inequalities. Next, the robust controllers satisfying the requirements can be designed by solving this optimization problem. Then, a new...
In practice, robot systems are usually required to hold uncertain load whose mass may change around a certain value. In this paper, the stability of the conventional model-based Computed-Torque Control(CTC) scheme for delta robots with uncertain load is investigated. Moreover a compensation control scheme is proposed to enhance the robustness of the conventional CTC scheme. The overall control is...
Deception plays a significant role in many intelligent systems whether natural (insect colonies, animal colonies etc. to human beings) or artificial ones. In order to make artificial deception near the one existing in the real world, fuzzy theory seems a reasonable tool since it is the theory of mind through which the uncertainties can be modeled. In this work a hide and seek problem is considered...
In this paper, taking the advantages of the simplicity of PD control and the high tracking performance of SMC and avoiding the drawbacks of both control methods, a new hybrid PD-SMC tracking control is proposed for trajectory tracking control of a serial robot by applying the hybridization concept. The unique features of the proposed hybrid control law are the model-free nonlinear feedback control...
Performance of constrained movements in multiple directions of a workspace simultaneously and in presence of uncertainty is a great challenge for robots. Achieving such tasks by employing control policies which are fully determined a priori and do not take into account the system uncertainty can cause undesired stress on the robot end-effector or the environment and result in poor performance. Instead,...
We present a novel approach to the problem of autonomously recognizing and unfolding articles of clothing using a dual manipulator. The problem consists of grasping an article from a random point, recognizing it and then bringing it into an unfolded state. We propose a data-driven method for clothes recognition from depth images using Random Decision Forests. We also propose a method for unfolding...
Even when walking in complex environments, human beings are able to easily share space and navigate comfortably past each other without explicit communication. Our recent work has sought to build models of this human navigation for the purposes of producing high-quality, cooperative, collision-free motion in multi-agent navigation tasks. We will first describe some recent approaches to modeling pedestrian...
This paper presents an algorithm based on ecological models of foraging and that uses uncertainty in scientific observations made by the robot to value future actions. It is the hypothesis of this work that the foraging strategy will be an improvement over strategies based on principles from the design of experiments literature for small budget sizes. The experiment in this paper shows that for small...
Deploying robots for service tasks requires learning algorithms that scale to the combinatorial complexity of our daily environment. Inspired by the way humans decompose complex tasks, hierarchical methods for robot learning have attracted significant interest. In this paper, we apply the MAXQ method for hierarchical reinforcement learning to continuous state spaces. By using Gaussian Process Regression...
Accurate needle insertion into soft, inhomogeneous tissue is a critical aspect of many medical problems, such as the percutaneous diagnosis and therapies. In such procedures, the flexible tip-steerable needle with bevel tips can deflect during insertion and reach the target we set beforehand. In 2D motion planning, it's crucial to maintain the needle in a desired plane. However, the inhomogeneous...
This paper introduces a strategy planner for nondeterministic hybrid systems with complex continuous dynamics. The planner uses sampling-based techniques and game-theoretic approaches to generate a series of plans and decision choices that increase the chances of success within a fixed time budget. The planning algorithm consists of two phases: exploration and strategy improvement. During the exploration...
Existing multi-robot cooperative perception solutions can be mainly classified into two categories, measurement-based and belief-based, according to the information shared among robots. With well-controlled communication, measurement-based approaches are expected to achieve theoretically optimal estimates while belief-based approaches are not because the cross-correlations between beliefs are hard...
This paper presents a novel adaptive control for electrically driven robot manipulators based on the voltage control strategy. The control law is designed using a nominal model. Then, Fourier series is applied to estimate the uncertainty originated from the mismatch between the actual model and nominal model. The uncertainty includes parametric uncertainty, un-modeled dynamics and external disturbance...
In this paper, we introduce an approach called FSBS (Forward Search in Belief Space) for online planning in POMDPs. The approach is based on the RTBSS (Real-Time Belief Space Search) algorithm of [1]. The main departure from the algorithm is the introduction of similarity measures in the belief space. By considering statistical divergence measures, the similarity between belief points in the forward...
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