Abstract

Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional control cost. To do so, we first introduce a novel data-driven perception module that observes LIDAR point clouds and estimates which features/regions an AV must navigate towards for drift minimization. Then, we introduce an interpretable model predictive controller (MPC) that moves an AV toward such feature-rich regions while avoiding visual occlusions and gracefully trading off drift and control cost. Our experiments on challenging, dynamic scenarios in the state-ofthe-art CARLA simulator indicate our method redu

Abstract

Many commodity sensors that measure the robot and dynamic obstacle’s state have non-Gaussian noise characteristics. Yet, many current approaches treat the underlying uncertainty in motion and perception as Gaussian, primarily to ensure computational tractability. On the other hand, existing planners working with non-Gaussian uncertainty do not shed light on leveraging distributional characteristics of motion and perception noise, such as bias for efficient collision avoidance.This paper fills this gap by interpreting reactive collision avoidance as a distribution matching problem between the collision constraint violations and Dirac Delta distribution. To ensure fast reactivity in the planner, we embed each distribution in Reproducing Kernel Hilbert Space and reformulate the distribution matching as minimizing the Maximum Mean Discrepancy (MMD) between the two distributions. We show that evaluating the MMD …

Abstract

We present a joint multi-robot trajectory optimizer that can compute trajectories for tens of robots in aerial swarms within a small fraction of a second. The computational efficiency of our approach is built on breaking the per-iteration computation of the joint optimization into smaller, decoupled sub-problems and solving them in parallel through a custom batch optimizer. We show that each of the sub-problems can be reformulated to have a special Quadratic Programming structure, wherein the matrices are shared across all the problems and only the associated vector varies. As a result, the batch solution update rule reduces to computing just large matrix-vector products which can be trivially accelerated using GPUs. We validate our optimizer’s performance in difficult benchmark scenarios and compare it against existing state-of-the-art approaches. We demonstrate remarkable improvements in computation time its scaling with respect to the number of robots. Moreover, we also perform better in trajectory quality as measured by smoothness and arc-length metrics.

Abstract

As compared to conventional adaptive control, robust adaptive control aims to provide robustness against unmodeled dynamics, coupling effects, and other endogenous and exogenous disturbances. Robust adaptive control has been an active research area over more than three decades, and has flourished in many application domains. Despite the advances, some bottlenecks still need to be circumvented for further progressing in the field. This special issue collects recent advances in robust adaptive control from theoretical and application perspectives. Theoretical aspects in robust adaptive control addressed in this special issue are:• Relaxing the persistence of excitation (PE) condition, standard in adaptive control and estimation: Katuyar, Roy, and Bhasin propose a novel condition, called finite persistence excitation (f-PE), milder than PE in terms of excitation requirement and online verifiability.

Abstract

A detailed design approach undertaken in the development of “Skylark” is presented in this paper. “Skylark” is a non-conventional fixed-wing biplane Micro Air Vehicle (MAV) with a wingspan and chord length within 150 mm. It is specially designed with the ability to host onboard vision-assisted autonomous navigation systems. Fixed-wing MAV with capabilities of vision assisted autonomous navigation is not reported in the open literature. To stay within the maximum dimensional constraint, flying wing configuration with a low aspect ratio is preferred for MAV design, and therefore, the stability is inadequate due to lower static margin when compared to bigger Unmanned Aerial Vehicles (UAVs). In this paper, the novel design strategy addresses the major challenges such as high payload-carrying capacity, stability, and onboard processing required for vision-assisted autonomous navigation. The higher payload …

Abstract

In this paper, we solve an optimal consensus control problem of maximizing the state-dependent communi- cation connectivity during a multi-agent consensus dynamics. A proportional-derivative type consensus controller is leveraged to drive agents into a symmetric formation. The asymptotic stability of the closed-loop system dynamics is established using Lyapunov theory, which helps us to deduce an intuitive time-varying gain profile based on a sufficient condition for convergence. Further, a Model Predictive Control approach is adopted to minimize a quadratic cost over a finite prediction horizon by adjusting the controller gains, such that the optimal connectivity is attained on the way with less control efforts, while handling constraints to agents’ states, inputs, turn-rates and disturbances injected into agent velocities. Simulation results with time-varying controller gains demonstrate the impact of our proposed technique

Abstract

This paper presents detailed mathematical modeling, controller design, and flight test results for the autonomous mission of a nonconventional fixed-wing biplane micro air vehicle (MAV) called Skylark having span and chord length within 150 mm. Although numerous fixed-wing MAV designs are reported in the open literature, an MAV’s reliable autonomous flight is still a challenge. The primary difficulties in performing the autonomous mission of fixed-wing MAV are system integration within the weight and power budget, center of gravity (CG) management, and flight controller design for fast dynamics. In this paper, the key challenges are addressed by using the higher payload capacity of Skylark, suitable selection and design of avionics components, and design of controller after detailed development of the mathematical model, including the additional effect of propeller wash and motor countertorque. An …

Abstract

In this paper, the Multi-Swarm Cooperative Information-driven search and Divide and Conquer mitigation control (MSCIDC) approach is proposed for faster detection and mitigation of forest fire by reducing the loss of biodiversity, nutrients, soil moisture, and other intangible benefits. A swarm is a cooperative group of Unmanned Aerial Vehicles (UAVs) that fly together to search and quench the fire effectively. The multiswarm cooperative information-driven search uses a multi-level search comprising cooperative information-driven exploration and exploitation for quick/accurate detection of fire location. The search level is selected based on the thermal sensor information about the potential fire area. The dynamicity of swarms, aided by global regulative repulsion and merging between swarms, reduces the detection and mitigation time compared to the existing methods. The local attraction among the members of the swarm helps the non-detector members to reach the fire location faster, and divide-and-conquer mitigation control ensures a non-overlapping fire sector allocation for all members quenching the fire. The performance of MSCIDC has been compared with different multi-UAV methods using a simulated environment of pine forest. The performance clearly shows that MSCIDC mitigates fire much faster than the multi-UAV methods. The Monte-Carlo simulation results indicate that the proposed method reduces the average forest area burnt by 65% and mission time by 60% compared to the best result case of the multi-UAV approaches, guaranteeing a faster and successful mission. Index Terms—Swarm search, forest firefighting, cooperative information-driven search, divide and conquer mitigation control

Abstract

Underwater gliders are common robotic platforms that usually have at least three degrees of freedom (DoF), namely surge, heave, and pitch. The heave motion generated using variable buoyancy can be vectored along surge direction using wings. These gliders are usually designed to be operated for specific applications and the mission dictates the diving requirements, usually quantified using factors such as dive-cycle, dive-in-depth, and range. Several parameters influence diving performance, of which, dimension and position of the wing play a significant role. The objective of this work is to study the effect of wing parameters such as the position of the wing (along the hull) and its dimensions, on the diving performance of the glider and thereby determine their optimal values for a given mission requirement. The wing can be accordingly designed and attached to the hull of the glider. The design and dynamics of RoBuoy, a novel underwater glider, is used for this study. Conveniently RoBuoy’s design allows modular wing positioning and dimensions, and hence the results of the study can be tested on this platform. A detailed study of the influence of the chosen wing parameters on the dynamics of the glider along with a Genetic Algorithm (GA) based approach to optimizing the parameters is presented in this paper.

Abstract

Among primates, the prehensile nature of the hand is vital for greater adaptability and a secure grip over the substrate/branches, particularly for arm-swinging motion or brachiation. Though various brachiation mechanisms that are mechanically equivalent to underactuated pendulum models are reported in the literature, not much attention has been given to the hand design that facilitates both locomotion and within-hand manipulation. In this paper, we propose a new robotic gripper design, equipped with shape conformable active gripping surfaces that can act as an active or passive joint and adapt to substrates with different shapes and sizes. A floating base serial chain, named GraspMaM, equipped with two such grippers, increases the versatility by performing a range of locomotion and manipulation modes without using dedicated systems. The unique gripper design allows the robot to estimate the passive joint state while arm-swinging and exhibits a dual relationship between manipulation and locomotion. We report the design details of the multimodal gripper and how it can be adapted for the brachiation motion assuming it as an articulated suspended pendulum model. Further, the system parameters of the physical prototype are estimated, and experimental results for the brachiation mode are discussed to validate and show the effectiveness of the proposed design.