Modeling nonlinear dynamical systems is important for control, prediction, and decision making in numerous engineering fields. While robust methods for identifying linear systems are well established, traditional approaches often struggle with complex nonlinear behavior, particularly when data is noisy or limited. Recently, the Koopman operator framework has gained popularity as it provides global linear representations of nonlinear systems, which enables the use of linear systems theory tools. However, existing data driven Koopman operator methods, such as extended dynamic mode decomposition (EDMD), can yield degraded performance under noisy conditions and typically require large amounts of training data. In this paper, we propose a new approach for learning Koopman models using sparse Bayesian learning (SBL). Our method identifies the beliefs over Koopman model parameters directly from model evidence while inherently promoting sparsity. Additionally, our approach demonstrates improved noise robustness and effective performance even with limited data. Unlike existing methods, the SBL approach provides valuable uncertainty quantification for the learned models, which is particularly beneficial for control applications requiring probabilistic performance guarantees.

This research introduces a framework for physical Human-Robot Interaction (pHRI) scenarios that estimates human behavior and intention. By incorporating this estimation template, velocity-based adaptive controllers can enhance both the effectiveness and comfort of pHRI. Existing methods in the literature primarily rely on sensor readings from the robot, overlooking the dynamics and relationships between the human arm and the robot. In our approach, we model these dynamics and relationships as a spring-damper system and assume that human velocity is the system’s input. This formulation enables the use of a Kalman filter to estimate human velocity from the robot’s sensor measurements in real-time. With an accurate estimation of human velocity, our approach provides a more precise understanding of human intention, enabling smoother and more adaptive interactions. The real-time prediction of human velocity and intent allows for more intuitive and responsive robot behavior, leading to significant improvements in both the effectiveness and comfort of pHRI scenarios. We conducted systematic experiments using the Franka Emika Panda collaborative manipulator, demonstrating that our approach enhances pHRI performance compared to alternative methods, particularly in terms of responsiveness and user experience.

This paper introduces a force-guided robotic platform that aims to reduce musculoskeletal injuries in manufacturing and healthcare environments. The platform utilizes force feedback algorithms, system modeling with distinct force and virtual torque processing, and virtual dynamics for seamless control of heavy loads. Similar mobile robot platforms in previous studies were designed for rehabilitation purposes. This study focuses on heavy-duty applications using a cost-effective load cell configuration on a differential drive system. We propose a novel model that simplifies the human-robot interaction analysis by decomposing pushing forces into linear and rotational components represented by a combination of linear and rotational spring-damper systems. These elements are integrated into the dynamics of the mobile robot platform for a comprehensive representation of human-robot interaction. Additionally, we developed a new control method to counteract large force fluctuations and anti-integral windup with dynamic clamping to improve system robustness.

Animals' anatomies have control systems combined with multi-motors and high-bandwidth sensors. Their complicated mechanisms give them high maneuverability with sufficient inertial stabilization performance during walking, jumping, and flying. From the point of aerial locomotion, flying insects use abdomen reflexes to stabilize their head positions. Articulation of the thoracic-abdominal joint contributes to the reorientation of their bodies over the law of conservation of angular momentum. Since acceleration is a fundamental component of maneuverability, increasing the acceleration without destabilizing the body is achieved with additional appendages such as the tail and abdomen. Highly actuated abdominal muscles are an essential feature of these natural flyers, conspicuously missing from the current aerial vehicles regarding maneuverability. This study proposes a bio-inspired aerial vehicle morphology with an actively controlled abdomen-like appendage. We aim to investigate the advantages and disadvantages of the abdomen-like appendage mounted on a bi-rotor aerial vehicle by constructing the dynamical model and designing optimization-based controllers; Linear Quadratic Regulator (LQR), Model Predictive Control (MPC), and Adaptive Model Predictive Control (A-MPC). We complete our analysis with a motion planning algorithm by combining the sampling-based neighborhood graph approach with the A-MPC strategy. We demonstrate through simulation experiments that the appendage improves the stability and maneuverability of aerial vehicles and the resulting motion planning structure with A-MPC ensures that the state and input constraints are not violated.

In this paper, we propose a Model Predictive Control (MPC) based distributed formation control method for a multi-robot system (MRS) that would move them among dynamic obstacles to a desired goal position. Specifically, after formulating the formation control, as a distributed version of MPC, we propose and evaluate three information-sharing schemes within the MRS; namely sharing (i) positions, (ii) complete predicted trajectories, and (iii) exponentially-sampled predicted trajectories. Using a simplified kinematic model for robots, we conducted systematic simulation experiments in (a) scenarios, where the robots are instructed to switch places, as one of the most challenging forms of formation changes, and in (b) scenarios where robots are instructed to reach a goal, within environments containing dynamic obstacles. In a set of systematic experiments conducted in simulation and with mini quadcopters, we have shown that sharing of exponentially-sampled trajectories (as opposed to positions, or complete trajectories) among the robots provides near-optimal paths while decreasing the required computation cost and communication bandwidth. Surprisingly, in the presence of noise, sharing exponentially-sampled trajectories among the robots decreased the variance in the final paths. The proposed method is demonstrated on a group of Crazyflie quadcopters.

Robust and safe feedback motion planning and navigation is a critical task for autonomous mobile robotic systems considering the highly dynamic and uncertain nature scenarios of modern applications. For these reasons motion planning and navigation algorithms that have deep roots in feedback control theory has been at the center stage of this domain recently. However, the vast majority of such policies still rely on the idea that a motion planner first generates a set of open-loop possibly time-dependent trajectories, and then a set of feedback control policies track these trajectories in closed-loop while providing some error bounds and guarantees around these trajectories. In contrast to trajectory-based approaches, some researchers developed feedback motion planning strategies based on connected obstacle-free regions, where the task of the local control policies is to drive the robot(s) in between these particular connected regions. In this paper, we propose a feedback motion planning algorithm based on sparse random neighborhood graphs and constrained nonlinear Model Predictive Control (MPC). The algorithm first generates a sparse neighborhood graph as a set of connected simple rectangular regions. After that, during navigation, an MPC based online feedback control policy funnels the robot with nonlinear dynamics from one rectangle to the other in the network, ensuring no constraint violation on state and input variables occurs with guaranteed stability. In this framework, we can drive the robot to any goal location provided that the connected region network covers both the initial condition and the goal position. We demonstrate the effectiveness and validity of the algorithm on simulation studies.

Autonomous cars and car-like robots have gained huge popularity recently due to the recent advancements in technology and AI industry. Motion and path planning is one of the most fundamental problems for such systems. In the literature, kinematic models are widely adopted for planning and control for these type of robots due to their simplicity (control and analysis) and fewer computational requirements. Though, applicability of kinematic models are limited to very low speeds or some specific cases, which can be easily violated in real life scenarios. Furthermore, most of the dynamical car models found in the literature assume that they are driven only in forward direction, at constant high speeds. In this study, we present a car model that captures the dynamics of both forward and backward driving, at low and high speeds. After creating the car model, we addressed the motion planning problem on this model, where we adopted a framework which combines Sequential Composition of Controllers (SCC) and Rapidly Exploring Random Trees (RRT). We performed simulations to show the effectiveness and robustness of our method, and results are promising for future experimental studies.

Sampling based methods resulted in feasible and effective motion planning algorithms for high dimensional configuration spaces and complex environments. A vast majority of such algorithms as well as their application rely on generating a set of open-loop trajectories first, which are then tracked by feedback control policies. However, controlling a dynamic robot to follow the planned path, while respecting the spatial constraints originating from the obstacles is still a challenging problem. There are some studies which combine statistical sampling techniques and feedback control methods which address this challenge using different approaches. From the feedback control theory perspective, Reference Governors proved to be a useful framework for constraint enforcement. Very recently, Arslan and Koditschek (2017) introduced a feedback motion planner that utilizes Reference Governors that provably solves the motion planning problem in simplified spherical worlds. In this context, here we propose a “trajectory-free” novel feedback motion planning algorithm which combines the two ideas: random trees and reference governors. Random tree part of the algorithm generates a collision-free region as a set of connected simple polygonal regions. Then, reference governor part navigates the dynamic robot from one region to the adjacent region in the tree structure, ensuring it stays inside the current region and asymptotically reaches to the connected region. Eventually, our algorithm robustly routes the robot from the start location to the goal location without collision. We demonstrate the validity and feasibility of the algorithm on simulation studies.

Pronking (aka. stotting) is a gait in which all legs are used in synchrony, resulting in long flight phases and large jumping heights that may potentially be useful for mobile robots on rough terrain. Robotic instantiations of this gait suffer from severe pitch instability either due to underactuation, or the lack of sufficient feedback. Nevertheless, the dynamic nature of this gait suggests that the Spring-Loaded Inverted Pendulum Model (SLIP), a very successful predictive model for both natural and robotic runners, would be a good basis for more robust and maneuverable robotic pronking. In this paper, we describe how “template-based control”, a controller structure based on the embedding of a simple dynamical “template” within a more complex “anchor” system, can be used to achieve stable and controllable pronking for a planar, underactuated hexapod model. In this context, high-level control of the gait is regulated through speed and height commands to the SLIP template, while the embedding controller based on approximate inverse-dynamics and carefully designed passive dynamics ensures the stability of the remaining degrees of freedom. We show through extensive simulation experiments that unlike existing open-loop alternatives, the resulting control structure provides stability, explicit maneuverability and significant robustness against sensor and actuator noise.