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We study the Acceleration Obstacle (AO) as a concept to enable a robot's navigation in human crowds. The AO's geometric properties are analyzed and a direct sampling-free algorithm is proposed to approximate its boundary by linear constraints. The resulting controller is formulated as a quadratic program and evaluated in interaction with simulated bi-directional crowd flow in a corridor. We compare it to alternative robotic controllers, considering the robot's and the crowd's performance and the robot's behavior with respect to emergent lanes. Our results indicate that the robot can achieve higher efficiency when being less integrated in lanes., IEEE Robotics and Automation Letters, 7 (4), ISSN:2377-3766

The thesis at hand is concerned with robots' navigation in human crowds. Specifically, methods are developed for planning a mobile robot's local motion between pedestrians, and they are evaluated in experiments where a robot interacts with real pedestrians as well as in simulations of a crowd and a robot. The thesis is divided in three main contributions. The first contribution describes a novel method for non-holonomic robots of convex shape to avoid imminent collisions with moving obstacles. The method assists navigation by correcting steering from the robot's path planner or driver. Its performance is evaluated using a custom simulator, which replicates real crowd movements from a campus dataset, and corresponding metrics which quantify agents' efficiency, the robot's impact on the crowd, and the number of collisions. Further, the method is implemented and evaluated on the standing wheelchair Qolo. In the experiments performed, it drives in autonomous mode using on-board sensing (LiDAR, RGB-D camera and a system to track pedestrians). It avoids collisions with up to five pedestrians and passes through a door. The second contribution studies the Acceleration Obstacle (AO) for enabling a robot's navigation in human crowds. The AO's geometric properties are analyzed and a direct sampling-free algorithm is proposed to approximate its boundary by linear constraints. The resulting controller is formulated as a quadratic program and evaluated in interaction with simulated bi-directional crowd flow in a corridor. A comparison to alternative robotic controllers is carried out, considering the robot's and the crowd's performance and the robot's behavior with respect to emergent lanes. Results indicate that the robot can achieve higher efficiency outside lanes. In the third contribution, the problem for a mobile robot to navigate seamlessly in a human crowd is treated by an inverse reinforcement learning (IRL) approach. A novel feature is proposed to model costs of anticipated collisions between agents. The feature approximates agents' pairwise interaction energy, a function which prior work has derived empirically from crowd data as an interaction potential driving pedestrians' mutual avoidance. Using a recent framework to perform IRL from locally optimal examples in continuous space, cost functions which incorporate the novel feature are learned efficiently from high-dimensional examples of real crowd motion. Examples are obtained from two public datasets containing pedestrians' and wheelchair users' trajectories. The learned models are evaluated and compared in how accurately their local optima model the training examples and test examples. Furthermore, predictions based on test examples' initial states only are generated similarly by optimization, and their distance to recorded ground truth is measured. Both models' predictions compare favorably to a recent related approach from the literature. Finally, a control system which computes and executes in real-time an optimal trajectory according to the learned cost functions is implemented on a robotic wheelchair, to steer it between pedestrians perceived by an on-board tracking system. The robot is deployed on campus, where the controller's performance is evaluated qualitatively. Results show that the approach often generates apt motion plans, which complement pedestrians' motion in an efficient manner, albeit oscillations between locally optimal solutions may occur.

Autonomous mobile robots (AMR) are rapidly being introduced into our world in transportation, delivery, medical service, agriculture, and household applications. Their ability to reduce the burden on humans has made them viable and increasingly popular sources of productivity, but with their increased presence in our society, assuring that they behave in socially acceptable and safe ways is critical to their widespread integration and success. However, in many real-world applications, these robotic systems are subject to various uncertainties from different sources, such as the presence of dynamic actors like humans and other robots, or the presence of external disturbances and sensing/actuation faults. These uncertainties bring challenges to motion planning as they can cause the robot to behave in strange and unnatural ways, deviating away from their desired behaviors, towards humans, and potentially into unsafe situations. Many of these uncertainties appear during robot operations, and robots typically use reactive motion planners, which may not be agile enough to keep the system or its environment safe. More recently, robot planning has been achieved through learning-based methods, which may have proactive components, but these approaches use black boxes, making it challenging to understand why and how robots make certain decisions, which is critical to gain trust and fully integrate robots into our shared world. This dissertation presents a set of proactive motion planning frameworks that promote social awareness and safety for autonomous mobile robots operating under uncertainties. The frameworks we develop monitor the future states of mobile robots and the nature of future interactions between robots and dynamic actors to improve and refine motion planning accordingly to a given scenario; whether it is a social navigation case study in which the robot must safely navigate in the presence of multiple humans, or if uncertainties are coming from different sources like sensing/actuation faults. First, we introduce a Hidden Markov Model (HMM)-based predictive model that adds to traditional prediction methods by accounting for uncertainties in predictions to plan proactive motion for a robot in the presence of multiple humans. Explicit predictions, however, can be restrictive in environments with multiple actors. To address this challenge, we reduce the problem to a binary classification through the design of a Decision Tree (DT)-based interpretable monitor that is used to predict and explain future interactions with dynamic actors for proactive non-interfering motion planning. We further extend this monitoring approach to adapt and improve on the failure modes of a baseline reactive motion planner, while also re-integrating HMM principles to enable fast runtime updating, so that predictions and planning can improve as operations continue. To extend our work to handle highly dynamic and dense environments, we leverage the idea of attention within human-human interactions and design a deep neural network (DNN) based approach that predicts which actors are most important to consider as constraints within the framework of a model predictive controller (MPC). Finally, we show that the approaches presented in our work can extend beyond the social navigation case study, through work in recovery from failures for a robot under decision uncertainties. The techniques presented in this dissertation are validated through extensive simulations and experiment case studies with real unmanned ground and aerial vehicles navigating in the presence of humans.

Funding Information: This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017274 (DARKO). Publisher Copyright: Copyright © 2022 Swaminathan, Kucner, Magnusson, Palmieri, Molina, Mannucci, Pecora and Lilienthal. | openaire: EC/H2020/101017274/EU//DARKO Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In particular, we exploit online streams of pedestrian data, commonly available from the robot's detection and tracking pipeline, to refine the prediction model and its performance in unseen scenarios. To avoid the forgetting of previously learned concepts, a problem known as catastrophic forgetting, our framework includes a regularization loss to penalize changes of model parameters that are important for previous scenarios and retrains on a set of previous examples to retain past knowledge. Experimental results on real and simulation data show that our approach can improve prediction performance in unseen scenarios while retaining knowledge from seen scenarios when compared to naively training the prediction model online., Comment: To be published in IEEE Robotics and Automation Letters. Accepted for presentation at 2022 IEEE International Conference on Robotics and Automation (ICRA)

Dependability of robot co-workers plays an important role in increasing the effectiveness of human-robot interaction in manufacturing. Our goal is to understand the role of motion planning parameters in human-robot collaboration and to provide guidelines for the selection of the most suitable motion planner. The human factors analysis provided in this paper highlights that repeatability of the motion and predictability of the robot timing affect the quality of human-robot collaboration. Copyright (C) 2020 The Authors.

In this work, we present a real-time approach for human-aware motion replanning using a two-level hierarchical architecture. The lower level leverages stable dynamical systems to generate motor commands and to online reshape the robot trajectories. The reshaping strategy modifies the velocity of the robot to match three requirements: i) to increase the human safety in case of close interaction with the robot, ii) to guarantee the correct task execution in case of unforeseen obstacles (including the human), and iii) to replan online the current task taking into account the human behavior. The lower level has to execute all needed computations in real-time. To this end, we also propose a novel approach that leverages depth space structure and parallel programming to rapidly and accurately estimate the robot-obstacle distances. The higher level of the architecture monitors the human and provides to the lower level information about the human status. The proposed approach has been tested in a human robot interaction scenario, showing promising results in terms of safe human-robot coexistence.

The new paradigm of human-robot collaboration has led to the creation of shared work environments in which humans and robots work in close contact with each other. Consequently, the safety regulations have been updated addressing these new scenarios. The mere application of these regulations may lead to a very inefficient behavior of the robot. In order to preserve safety for the human operators and allow the robot to reach a desired configuration in a safe and efficient way, a two layers architecture for trajectory planning and scaling is proposed. The first layer calculates the nominal trajectory and continuously adapts it based on the human behavior. The second layer, which explicitly considers the safety regulations, scales the robot velocity and requests for a new trajectory if the robot speed drops. The proposed architecture is experimentally validated on a Pilz PRBT manipulator., To be published in IEEE Robotics and Automation Letters (RA-L) and IEEE International Conference on Robotics and Automation (ICRA) 2021 in Xi'an, China

This paper describes a novel method for non-holonomic robots of convex shape to avoid imminent collisions with moving obstacles. The method's purpose is to assist navigation in crowds by correcting steering from the robot's path planner or driver. We evaluate its performance using a custom simulator which replicates real crowd movements and corresponding metrics which quantify the agent's efficiency and the robot's impact on the crowd and count collisions. We implement and evaluate the method on the standing wheelchair Qolo. In our experiments, it drives in autonomous mode using on-board sensing (LiDAR, RGB-D camera and a system to track pedestrians) and avoids collisions with up to five pedestrians and passes through a door.