Azim Eskandarian

Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud registration to achieve real-time global localization. First, we apply a passive-perception strategy, bird-inspired drone swarm-keeping, enabling each UAV to estimate the relative motion and proximity of its neighbors using only monocular visual cues. This decentralized mechanism provides cohesive and collision-free group motion without GNSS, active ranging, or explicit communication. Second, we integrate this capability with a cooperative mapping pipeline in which one or more drones acting as global anchors generate a globally referenced monocular SLAM map. Vehicles lacking global positioning progressively align their locally generated point clouds to this shared global reference using an iterative registration strategy, allowing them to infer consistent global poses online. Other autonomous vehicles optionally contribute complementary viewpoints, but UAVs remain the core autonomous agents driving both mapping and coordination due to their privileged visual perspective. Experimental validation in simulation and indoor testbeds with drones demonstrates that the integrated system maintains swarm cohesion, improves spatial alignment by more than a factor of four over baseline monocular SLAM, and preserves reliable global localization throughout extended GNSS outages. The results highlight a scalable, lightweight, and vision-based approach to resilient UAV autonomy in tunnels, industrial environments, and other GNSS-challenged settings.

Reliable autonomy for drones operating in GNSS-intermittent or denied environments requires both stable inter-vehicle coordination and a shared global understanding of the environment. This paper presents a unified vision-based framework in which UAVs use biologically inspired swarm behaviors together with online monocular point-cloud registration to achieve real-time global localization. First, we apply a passive-perception strategy, bird-inspired drone swarm-keeping, enabling each UAV to estimate the relative motion and proximity of its neighbors using only monocular visual cues. This decentralized mechanism provides cohesive and collision-free group motion without GNSS, active ranging, or explicit communication. Second, we integrate this capability with a cooperative mapping pipeline in which one or more drones acting as global anchors generate a globally referenced monocular SLAM map. Vehicles lacking global positioning progressively align their locally generated point clouds to this shared global reference using an iterative registration strategy, allowing them to infer consistent global poses online. Other autonomous vehicles optionally contribute complementary viewpoints, but UAVs remain the core autonomous agents driving both mapping and coordination due to their privileged visual perspective. Experimental validation in simulation and indoor testbeds with drones demonstrates that the integrated system maintains swarm cohesion, improves spatial alignment by more than a factor of four over baseline monocular SLAM, and preserves reliable global localization throughout extended GNSS outages. The results highlight a scalable, lightweight, and vision-based approach to resilient UAV autonomy in tunnels, industrial environments, and other GNSS-challenged settings.

The complexity of today’s mobile robots, equipped with multiple sensors and actuators, makes linear control strategies much more challenging. This article presents and tests a deep reinforcement learning approach to control a real spherical robot based on position, velocity, and heading. The robot’s motion is achieved by driving an electric motor for rotational dynamics and two servos for longitudinal displacement. Three deep reinforcement learning controllers are obtained with different sets of control signals of increasing complexity. The best performance is obtained by using its position and heading to control the linear and angular velocities to reduce the time to reach the target. This control technique is recommended for this type of system as it learns an optimal control law that interacts with the robot’s dynamics and the environment, natively capturing the multivariable and nonlinear characteristics of the system. The article also includes previously designed controllers for the robot, comparing their performance in a series of position control tests in simulation and on the experimental platform.

Human motion is stochastic and ensuring safe robot navigation in a pedestrian-rich environment requires proactive decision-making. Past research relied on incorporating deterministic future states of surrounding pedestrians which can be overconfident leading to unsafe robot behaviour. The current paper proposes a predictive uncertainty-aware planner that integrates neural network based probabilistic trajectory prediction into planning. Our method uses a deep ensemble based network for probabilistic forecasting of surrounding humans and integrates the predictive uncertainty as constraints into the planner. We compare numerous constraint satisfaction methods on the planner and evaluated its performance on real world pedestrian datasets. Further, offline robot navigation was carried out on out-of-distribution pedestrian trajectories inside a narrow corridor

The increasing complexity of autonomous vehicles has exposed the limitations of many existing control systems. Reinforcement learning (RL) is emerging as a promising solution to these challenges, enabling agents to learn and enhance their performance through interaction with the environment. Unlike traditional control algorithms, RL facilitates autonomous learning via a recursive process that can be fully simulated, thereby preventing potential damage to the actual robot. This paper presents the design and development of an RL-based algorithm for controlling the collaborative formation of a multi-agent Khepera IV mobile robot system as it navigates toward a target while avoiding obstacles in the environment by using onboard infrared sensors. This study evaluates the proposed RL approach against traditional control laws within a simulated environment using the CoppeliaSim simulator. The results show that the performance of the RL algorithm gives a sharper control law concerning traditional approaches without the requirement to adjust the control parameters manually.

LiDAR is one of the most crucial sensors for autonomous vehicle perception. However, current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous LiDAR quality assessment methods, leading to uncertainty in detection performance. Additionally, existing point cloud quality assessment algorithms are predominantly designed for singleobject scenarios. In this paper, we introduce a novel image-guided point cloud quality assessment algorithm for outdoor autonomous driving environments, named the Image-Guided Outdoor Point Cloud Quality Assessment (IGO-PQA) algorithm. Our proposed algorithm comprises two main components. The first component is the IGO-PQA generation algorithm, which leverages point cloud data, corresponding RGB surrounding view images, and agent objects' ground truth annotations to generate an overall quality score for a single-frame LiDAR-based point cloud. The second component is a transformer-based IGO-PQA regression algorithm for no-reference outdoor point cloud quality assessment. This regression algorithm allows for the direct prediction of IGO-PQA scores in an online manner, without requiring image data and object ground truth annotations. We evaluate our proposed algorithm using the nuScenes and Waymo open datasets. The IGO-PQA generation algorithm provides consistent and reasonable perception quality indices. Furthermore, our proposed IGO-PQA regression algorithm achieves a Pearson Linear Correlation Coefficient (PLCC) of 0.86 on the nuScenes dataset and 0.97 on the Waymo dataset

Recent studies affirm the potential of autonomous vehicles (AVs) in reducing traffic accidents and fatalities. This article presents an overview of on-road AV testing and analyzes crash data involving AVs. The study emphasizes fatalities and errors, comparing AVs to conventional human-driven vehicles or nonautonomous vehicles (non-AVs). Statistical analysis indicates that in most cases, human errors predominantly cause accidents, with responsibility falling on non-AV entities, such as bicyclists and motorcyclists, as well as adverse weather and lighting conditions. Notably, rear-end collisions are prevalent. AVs display superior sensing and processing capabilities, except in adverse weather and automation failure scenarios. Surprisingly, human drivers are accountable for most accidents, overshadowing the significance of AV error rates. Common collision causes between AVs and non-AVs encompass overspeeding, inadequate following distances, and decision-making deficiencies in non-AVs. The study shows the potential of AVs to enhance road safety while shedding light on areas requiring continued improvement.

This paper introduces a novel approach for addressing a fundamental challenge in pedestrian trajectory prediction: reliable future state estimation in the presence of sensor noise and uncertain perception. It is common for traditional prediction models to produce overconfident and error-prone deterministic estimates. Previous research exploring probabilistic estimation methods often overlooked inherent noise in upstream perception data which is a crucial factor under adverse conditions like bad weather or occlusion. While Bayes filters are adept at integrating data from noisy sensors, they falter in handling non-linearities and long-term forecasting. Our proposed approach is an end-to-end estimator capable of handling noisy sensor data to deliver robust predictions of future states with uncertainty bounds while factoring in upstream perception uncertainty. This is achieved through an innovative encoder-decoder-based deep ensemble network designed to capture both perception and prediction uncertainty during trajectory prediction. We benchmark our model against other established approximate Bayesian inference methods on publicly available pedestrian datasets. The results demonstrate that our deep ensembles not only yield more robust predictions but also capable of reliable predictions on out-of-distribution pedestrian trajectories. Our method plays a key role in improving dynamic agent trajectory prediction, offering a more precise and robust framework for future state estimation.