Abstract

Autonomous navigation and obstacle avoidance are core capabilities that enable robots to execute tasks in the real world. We propose a new approach to collision avoidance that accounts for uncertainty in the states of the agent and the obstacles. We first demonstrate that measures of entropy—used in current approaches for uncertainty-aware obstacle avoidance—are an inappropriate design choice. We then propose an algorithm that solves an optimal control sequence with a guaranteed risk bound, using a measure of overlap between the two distributions that represent the state of the robot and the obstacle, respectively. Furthermore, we provide closed form expressions that can characterize the overlap as a function of the control input. The proposed approach enables modelpredictive control framework to generate bounded-confidence control commands. An extensive set of simulations have been conducted in various constrained environments in order to demonstrate the efficacy of the proposed approach over the prior art. We demonstrate the usefulness of the proposed scheme under tight spaces where computing risk-sensitive control maneuvers is vital. We also show how this framework generalizes to other problems, such as object-following.

Abstract

In this work, we propose adaptive driving behaviors for simulated cars using continuous control deep reinforcement learning. Deep Deterministic Policy Gradient(DDPG) is known to give smooth driving maneuvers in simulated environments. Unfortunately, simple feedforward networks, lack the capability to contain temporal information, hence we have used its Recurrent variant called Recurrent Deterministic Policy Gradients. Our trained agent adapts itself to the velocity of the traffic. It is capable of slowing down in the presence of dense traffic, to prevent collisions as well to speed up and change lanes in order to overtake when the traffic is sparse. The reasons for the above behavior, as well as, our main contributions are: 1. Application of Recurrent Deterministic Policy Gradients. 2. Novel reward function formulation. 3. Modified Replay Buffer called Near and Far Replay Buffers, wherein we maintain two replay buffers and sample equally from both of them.

Abstract

With the growing demand of large-scale heavy lift vessels in the deep-sea offshore construction works, high performance of Dynamic positioning (DP) systems is becoming ever crucial. However, current DP systems on board of heavy lift vessels do not consider model uncertainty (typically arising from mooring forces). In this paper, an observer-based robust controller is designed that can tackle model uncertainty in hydrodynamic damping and mooring forces, environmental disturbances as well as can filter out the high-frequency vessel movement. Closed-loop system stability is analytically established in terms of uniformly ultimately boundedness. In addition, several key performance indicators are provided for tuning the performance of the controller. The effectiveness of the proposed control framework is studied in simulation with a crane-vessel system.

Abstract

In the literature of any time-delay control (TDC)-based methods, the boundedness of the error due to time-delay estimation (TDE) is crucial to prove the stability. However, the TDE error has been studied by discretizing the closed-loop system while neglecting the effect of discretization error; consequently, the TDE error is considered to be upper bounded by a constant. This letter proves that such constant upper bound is restrictive in nature due to the explicit involvement of system states in the TDE error. Thereby, without discretizing the closed-loop system, a new structure of the upper bound of TDE error is directly formulated in the continuous-time domain which has an explicit dependency on the system states. Via this formulation, this letter solves the long-standing problem for TDC of having consistent stability analysis and control design in continuous time. Based on the newly proposed structure of TDE error, an enhanced robust control law is formulated. The effectiveness of the proposed method is experimentally substantiated as compared to the conventional TDC using a multiple-degrees-of-freedom robot.

Abstract

Classical Image-Based Visual Servoing (IBVS) makes use of geometric image features like point, straight line and image moments to control a robotic system. Robust extraction and real-time tracking of these features are crucial to the performance of the IBVS. Moreover, such features can be unsuitable for real world applications where it might not be easy to distinguish a target from rest of the environment. Alternatively, an approach based on complete photometric data can avoid the requirement of feature extraction, tracking and object detection. In this work, we propose one such probabilistic model based approach which uses entire photometric data for the purpose of visual servoing. A novel image modelling method has been proposed using Student Mixture Model (SMM), which is based on Multivariate Student’s t-Distribution. Consequently, a vision-based control law is formulated as a least squares minimisation problem. Efficacy of the proposed framework is demonstrated for 2D and 3D positioning tasks showing favourable error convergence and acceptable camera trajectories. Numerical experiments are also carried out to show robustness to distinct image scenes and partial occlusion.

Abstract

This letter proposes a new adaptive control method for a class of nonlinearly parametrized switched systems that includes Monod kinetics and Euler-Lagrange systems with nonlinear in parameters form as special cases. As compared to the adaptive switched frameworks proposed in literature, the proposed adaptation framework has the distinguishing feature of updating the gains of the active and inactive subsystems simultaneously: by doing this it avoids high gains for the active subsystems or vanishing gains for the inactive ones. The design is studied analytically and its performance is validated in simulation with a robotic manipulator example.

Abstract

The adaptive sliding mode control technique relaxes the assumption of known bound of the disturbance in discrete-time systems. However, the existing technique of gain adaptation in discrete-time sliding mode control has issues of gain overestimation and underestimation. Therefore, this paper proposes a technique to adapt the switching gain such that the adaptive gain can tackle the uncertainty without any knowledge of the bound of uncertainty while overcoming the over- and under-estimation problems of switching gain.

Abstract

The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF.

Abstract

Underestimation and overestimation problems are commonly observed in conventional adaptive sliding mode control (ASMC). These problems refer to the fact that the adaptive controller gain unnecessarily increases when the states are approaching the sliding surface (overestimation) or improperly decreases when the states are getting far from it (underestimation). In this paper, we propose a novel ASMC strategy that overcomes such issues. In contrast to the state of the art, the proposed strategy is effective even when an a priori constant bound on the uncertainty cannot be imposed. Comparative results using a two-link manipulator demonstrate improved performance as compared to the conventional ASMC. Experimental results on a biped robot confirm the effectiveness and robustness of the proposed method under various practical uncertainties.

Abstract

A consideration of actuator saturation is an important aspect to study the effectiveness of a designed controller in practice. However, the conventional Lyapunov theory-based design is not always suitable to analyze the quantitative behavior of closed-loop system. This article presents a time-scale redesign-based saturated tracking controller for a class of feedback linearizable multi-input-multi-output (MIMO) nonlinear systems. The proposed controller is built upon the frameworks of contraction and partial contraction theories which ensures that bounded tracking performance as well as quantify the steady-state error bounds in terms of the various control design parameters. Notably, in contrast to the existing Lyapunov-method-based designs, the proposed approach allows to tune the controller performance without arbitrary reduction of singular perturbation parameters. Therefore, the vulnerability of the controller toward actuator saturation and noise, due to the ill-effects of high-gains stemming from the conventional high-gain controllers, are reduced. The extensive experimental results using a wheeled mobile robot are provided to demonstrate the effectiveness of the proposed controller.