IROS 2023 Papers

Contact force Jacobian relates the changes in the contact force to the changes in the actuation of a robotic catheter in contact with a surface. In this paper, we present an analytical method for calculating the contact force Jacobian for the Cosserat rod model of an MRI-actuated robotic catheter. First, the Cosserat rod model of the MRI-actuated robotic catheter under tip contact position constraint is introduced. For the analytical derivation of contact force Jacobian, the initial value problem parameter derivatives are defined and calculated analytically. Finally, simulation results show that the presented analytical method calculates the contact force Jacobian in significantly shorter computation time with comparable accuracy, compared to direct numerical computation.

A strap is a frequently utilized component for securing wearable robots to their users in order to facilitate force transmission between humans and the devices. For the appropriate function of the wearable robot, the pressure between the strap and the skin should be maintained appropriately. Due to muscle contraction, the cross-section area of the human limb changes according to the movement of the muscle. The cross-section area change causes the change in the pressure applied by the strap. Therefore, for a new strap design to resolve this, it is necessary to understand the shape change characteristics of the muscle where the strap is applied. In this paper, the change in the circumference of the thigh and the calf during walking was measured and analyzed by multiple string pot sensors. With a treadmill and string pot sensors using potentiometers, torsion springs, and leg circumference changes were measured for different walking speeds and slopes. And, gait cycles were divided according to a signal from the FSR sensor inserted in the right shoe. From the experimental results, there were changes in the circumference of about 8.5mm and 3mm for the thigh and the calf, respectively. And we found tendencies in various walking circumstances such as walking speed and degree of the slope. It is confirmed that they can be used for estimation algorithms of gait cycles or gait circumstances.

We propose an orthogonal collocation method (CM) for solving Cosserat rod Dirichlet-Neumann boundary value problems in static and dynamic modes. We interpolate the internal loading and collocate the strong form of the differential equations. The method uses Chebyshev polynomials in order to minimize Runge's phenomenon. The time derivatives are implicitly discretized using the backward differentiation formula BDF- \alpha\alpha We compare our method with the shooting method (SM), multiple shooting method (MSM) and two isogeometric CM against three static and one dynamic applications. The results show that our CM is more stable than SM and faster than MSM.

This paper develops a novel approach to design, actuate, and manufacture a space debris collector based on the conical Kresling origami pattern. The deployable nature of origami structures and the radial closability of the conical Kresling pattern are leveraged to form an enclosure volume for collecting space debris at different sizes. We first introduce the geometric, volume, and energy models of the conical Kresling pattern. Based on these models, the debris collector design problem is formulated as a parameter optimization problem to minimize the actuation energy for the folding process while satisfying the minimum volume constraint and geometric/functional constraints. To automatically capture debris in space, an actuation system is designed, which is compatible with space environments. Moreover, the multi-material three-dimensional printing technology is applied to build the designed debris collector, which makes it feasible for manufacturing the product in orbit. The proposed design, actuation, and manufacturing approaches are verified with experimental tests using a designed prototype.

A small-scale jumping robot can reach obstacles much larger than its size. It is important for a jumping robot to perform intermittent jumps to cross through rough terrains. However, the limitations of conventional structures hinder the further integration of functions to a miniature (sub-50 g) jumping robot. No sub-50 g jumpers could perform intermittent jumps with adjustable jumping trajectories. In this work, we proposed an origami-based miniature jumper, which performed intermittent jumps with adjustable omni-directional trajectories. The intermittent jumps were achieved by the jumping and self-righting mechanisms, which were actuated by a single motor. The clockwise and counterclockwise rotation of the motor actuated the loading, self-righting and triggering process, respectively. The jumping height was adjustable by adjusting the rotation angle of the motor. Meanwhile, the take-off pitch & yaw angle adjustment methods were integrated into the robot. Therefore, we demonstrated a 9 cm, 13.5 g prototype with functions of re-loading, self-righting, jumping height adjustment and take-off pitch & yaw angle adjustment. The robot could adjust jumping height from 16 to 34 cm and self-right for the next jump. The results revealed that our robot could jump across different obstacles with different scales and directions. The mobility was greatly increased compared with other miniature jumping robots.

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://github.com/Star-Robot/chinese-chess-robot.

We propose a novel methodology for robotic follow-ahead applications that address the critical challenge of obstacle and occlusion avoidance. Our approach effectively navigates the robot while ensuring avoidance of collisions and occlusions caused by surrounding objects. To achieve this, we developed a high-level decision-making algorithm that generates short-term navigational goals for the mobile robot. Monte Carlo Tree Search is integrated with a Deep Reinforcement Learning method to enhance the performance of the decision-making process and generate more reliable navigational goals. Through extensive experimentation and analysis, we demonstrate the effectiveness and superiority of our proposed approach in comparison to the existing follow-ahead human-following robotic methods. Our code is available at https://github.com/saharLeisiazar/follow-ahead-ros.

Beyond programming robots to accomplish a single high-level task at a time, people also hope robots follow instructions and complete a series of tasks while meeting their requirements. This paper presents an interactive software system that consists of a multiple-task linear temporal logic (LTL) path planner and a human-machine interface (HMI). The HMI transforms human oral instructions into task commands that can be understood by the machine. The planner grows a rapid random exploring tree to search for solutions for multiple tasks. When switching tasks, the search tree is re-initialized and reconnected to utilize the information gathered during the exploration of the workspace. The feasibility of the improved planner is theoretically guaranteed, and profiling in simulation shows an acceleration in planning. An experiment with a quadcopter is conducted to show that the combination of the multiple-task LTL planner and the HMI results in a synergistic effect in real-world applications.

This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

We present an inflatable finger pad that allows regular parallel-jaw grippers to vary their grasp stiffness while maintaining a contact force and contact to non-planar surfaces. An eversible radial bellows structure made of silicone rubber allows the pad to extend to four times its original height and to retract into a rigid pod when not needed. The bellows act as passive universal joints when everted, enabling aerial contact with surfaces inclined by up to 45 degree. The bellow geometry is intentionally nonlinear but avoids bistable configurations to facilitate control. We find that the nonlinear stiffness behavior allows a pair of opposing pads to increase the compliance of a grasp twenty fold, resulting in a total of two orders of magnitude difference between the most stiff and most compliant configuration. Crucially, high compliance can be achieved while exerting a contact force between 0.7 N and 5 N, allowing for compliant but firm grasps. Pads are manufactured using printed molds and sacrificial-mold casting.

Mobility, a critical factor in quality of life, is often rehabilitated using simplistic solutions, such as walkers. Exoskeletons (wearable robotics) offer a more sophisticated rehabilitation approach. However, non-adherence to externally worn mobility aids limits their efficacy. Here, we present the concept of a fully implantable assistive limb actuator that overcomes non-adherence constraints, and which can provide high-precision assistive force. In a bipedal animal model (fowl), we have developed a variable length isometric actuator (measuring ϕ9 x 30 mm) that is able to be directly implanted within the leg via a bone anchor and tendon fixation, replacing the lateral gastrocnemius muscle belly. The actuator is able to generate isometric force similar to the in vivo force of the native muscle, designed to generate assistive torque at the ankle and reduce muscular demand at no additional energy cost. The device has a stroke of 10 mm that operates up to 770 mm/s (77 stroke lengths/s), capable of acting as a clutch (disengaging when needed) and with a tunable slack length to modulate the timing and level of assistive force during gait. Surgical techniques to attach the actuator to the biological system, the Achilles tendon and tibia, have been established and validated using survival surgeries and cadaveric specimens.

In this paper, we present an event-based control framework for the efficient tracking of contour-based areas, such as road pavements, using a multirotor aerial vehicle equipped with a bio-inspired Dynamic Vision Sensor (DVS). Concerning the detection part, the DVS camera captures events, which are asynchronously fed into a Neuromorphic Hough Transform algorithm running on a SpiNN-3 board and implemented as a Spiking Neural Network (SNN). Next, the asynchronous output of the detection module is fed into an analytically formulated event-based Partitioned Visual Servoing (PVS) algorithm, running on conventional processing hardware, which allows the multirotor to autonomously track and navigate along the detected contour. The proposed architecture achieves efficient tracking of contour-based areas, while constantly maintaining the latter inside the DVS camera's field of view. A set of real-time experiments in various settings employing an octorotor equipped with a downward-looking DVS and a SpiNN-3 board demonstrate the effectiveness of the suggested framework.

Humans naturally execute many everyday manipulation actions with both arms simultaneously. Similarly, endowing robots with bimanual manipulation task models is key to efficiently perform complex manipulation tasks. To do so, a promising approach is to learn a library of task models from human demonstrations. However, this requires human motions to be meaningfully segmented. In this paper, we propose to segment the motion of each hand individually to account for different bimanual coordination patterns and provide a thorough evaluation of state-of-the-art segmentation algorithms on bimanual manipulation datasets. In particular, we compare segmentation algorithms at trajectory and semantic level with hierarchical algorithms. Moreover, our evaluation extensively studies the performances of various segmentation algorithms over a novel extension of the KIT Bimanual Manipulation Dataset featuring ~ 176 minutes of human motion recordings in household scenarios.

In this paper we report an experimental evaluation of three popular methods for online system identification of unmanned surface vehicles (USVs) which were implemented as an ensemble: certifiably stable shallow recurrent neural network (RNN), adaptive identification (AID), and recursive least squares (RLS). The algorithms were deployed on eight USVs for a total of 30 hours of online estimation. During online training the loss function for the RNN was augmented to include a cost for violating a sufficient condition for the RNN to be stable in the sense of contraction stability. Additionally we described an efficient method to calculate the equilibrium points of the RNN and classify the associated stability properties about these points. We found the AID method had lowest mean absolute error in the online prediction setting, but a weighted ensemble had lower error in offline processing.

Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic processors. SNNs have also been successfully deployed on robots to solve autonomous navigation problems. However, lane keeping with a LiDAR sensor is still an open problem for SNNs. In this work, we propose an end-to-end approach based on an SNN to solve the lane-keeping problem using a 3D LiDAR sensor. For the first time, we explore the capability of the proposed SNN controller to perceive the LiDAR input and exploit the features to perform reward-based feedback learning. To ensure the effectiveness of the controller, the proposed method is deployed and evaluated on two high-fidelity simulators. The experimental results demonstrate the high applicability and performance in different scenarios. Furthermore, experiments show that the SNN is capable of performing lane keeping in a simulated urban environment with only 18 control neurons and 32 synapse connections, producing on average only a 17cm deviation from lane center, which is 4.3 % of the lane width.

Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories. In previous work, we showed that an optimally task-invariant energy shaping controller implemented on a knee-ankle ex-oskeleton reduced the effort of certain muscles for a range of tasks. In this paper, we extend this approach to the sensor suite available at the ankle and present its implementation on a commercially-available, bilateral ankle exoskeleton. An experiment with three healthy subjects walking on a circuit and on a treadmill showed that the controller can approximate biomimetic profiles for varying terrains and task transitions without classifying tasks or switching control modes.

Autonomous navigation of ground robots on uneven terrain is being considered in more and more tasks. However, uneven terrain will bring two problems to motion planning: how to assess the traversability of the terrain and how to cope with the dynamics model of the robot associated with the terrain. The trajectories generated by existing methods are often too conservative or cannot be tracked well by the controller since the second problem is not well solved. In this paper, we propose terrain pose mapping to describe the impact of terrain on the robot. With this mapping, we can obtain the SE(3) state of the robot on uneven terrain for a given state in SE(2). Then, based on it, we present a trajectory optimization framework for car-like robots on uneven terrain that can consider both of the above problems. The trajectories generated by our method conform to the dynamics model of the system without being overly conservative and yet able to be tracked well by the controller. We perform simulations and real-world experiments to validate the efficiency and trajectory quality of our algorithm.

Although telepresence assistive robots have made significant progress, they still lack the sense of realism and physical presence of the remote operator. This results in a lack of trust and adoption of such robots. In this paper, we introduce an Avatar Robot System which is a mixed real/virtual robotic system that physically interacts with a person in proximity of the robot. The robot structure is overlaid with the 3D model of the remote caregiver and visualized through Augmented Reality (AR). In this way, the person receives haptic feedback as the robot touches him/her. We further present an Optimal Non-Iterative Alignment solver that solves for the optimally aligned pose of 3D Human model to the robot (shoulder to the wrist non-iteratively). The proposed alignment solver is stateless, achieves optimal alignment and faster than the baseline solvers (demonstrated in our evaluations). We also propose an evaluation framework that quantifies the alignment quality of the solvers through multifaceted metrics. We show that our solver can consistently produce poses with similar or superior alignments as IK-based baselines without their potential drawbacks.

The use of mobile robots in unstructured environments like the agricultural field is becoming increasingly common. The ability for such field robots to proactively identify and avoid failures is thus crucial for ensuring efficiency and avoiding damage. However, the cluttered field environment introduces various sources of noise (such as sensor occlusions) that make proactive anomaly detection difficult. Existing approaches can show poor performance in sensor occlusion scenarios as they typically do not explicitly model occlusions and only leverage current sensory inputs. In this work, we present an attention-based recurrent neural network architecture for proactive anomaly detection that fuses current sensory inputs and planned control actions with a latent representation of prior robot state. We enhance our model with an explicitly-learned model of sensor occlusion that is used to modulate the use of our latent representation of prior robot state. Our method shows improved anomaly detection performance and enables mobile field robots to display increased resilience to predicting false positives regarding navigation failure during periods of sensor occlusion, particularly in cases where all sensors are briefly occluded. Our code is available at: https://github.com/andreschreiber/roar.