IROS 2020 Papers

The holonomic and omnidirectional capabilities imparted to the mobile base platform depends mainly on two factors, i.e., the wheel design and its various arrangements in the platform chassis. This paper reports on the development of a novel omnidirectional spherical sectioned wheel named Ospheel. It is modular, and the spherical sectioned geometry of the wheel is driven using two actuators placed inside the housing above the wheel that rotates it independently about two perpendicular axes. The mechanical drive system for Ospheel consists of two gear trains, namely, internal spur gear and crown gear spatially assembled in orthogonal planes and are driven by two driving pinions. The kinematics of a single Ospheel is described, followed by the kinematic equation of a robot equipped with two Ospheels. Forward and inverse kinematic equations are derived explicitly. Experiments were carried out with the two Ospheels at a fixed inclination assembled with the base to illustrate the holonomic motion. The robustness of the wheel design is experimented with different trajectories and on different terrains.

As robots and robotic things become to have more agency, IoRT which consists of robots and robotic things can be considered as a social organization. Accordingly, social organization structure of IoRT could affect users' behavior and perception of IoRT. In this study, in order to examine the effect of social organization structure on people's acceptance of IoRT, we conducted a 2 (social organization structure: flat vs. hierarchical) within-participants experiment (N=30). In the experiment, a participant was asked to take part in cooking task with the aid of a robot, a robotic measuring cup, and a robotic mixer. We executed a post-experimental survey and counted the duration of participants' following the instruction given by the platform. People gave higher scores of trustworthiness and purchase intention to the platform with flat organization structure than that with hierarchical one. On the contrary, participants were more compliant with the hierarchical IoRT service platform than a flat one. Implications for the theory and design of IoRT are discussed.

Introducing object-level semantic information into simultaneous localization and mapping (SLAM) system is critical. It not only improves the performance but also enables tasks specified in terms of meaningful objects. This work presents OrcVIO, for visual-inertial odometry tightly coupled with tracking and optimization over structured object models. OrcVIO differentiates through semantic feature and bounding-box reprojection errors to perform batch optimization over the pose and shape of objects. The estimated object states aid in real-time incremental optimization over the IMU-camera states. The ability of OrcVIO for accurate trajectory estimation and large-scale object-level mapping is evaluated using real data.

In a geometric mechanics framework, the configuration space is decomposed into a shape space and a position space. The internal motion of the system is prescribed by a closed loop in the shape space, which causes net motion in the position space. If the shape space is a simply connected domain in an Euclidean space, then with an optimal choice of the body frame, the displacement in the position space is reasonably approximated by the surface integral of the height function, a functional relationship between the internal shape and position space variables. Our recent work has extended the scope of geometric methods from limbless undulatory system to those with legs; interestingly, the shape space for such systems has a torus structure. However, to the best of our knowledge, the optimal choice of the body frame on the torus shape space was not explored. In this paper, we develop a method to optimally choose the body frame on the torus which results in good approximation of displacement by the integral of the height function. We apply our methods to the centipede locomotion system and observe quantitative agreement of our prediction and experimental results.

Continuum robots have long held a great potential for applications in inspection of remote, hard-to-reach environments. In future environments such as the Deep Space Gateway, remote deployment of robotic solutions will require a high level of autonomy due to communication delays and unavailability of human crews. In this work, we explore the application of policy optimization methods through Actor-Critic gradient descent in order to optimize a continuum manipulator’s search method for an unknown object. We show that we can deploy a continuum robot without prior knowledge of a goal object location and converge to a policy that finds the goal and can be reused in future deployments. We also show that the method can be quickly extended for multiple Degrees-of-Freedom and that we can restrict the policy with virtual and physical obstacles. These two scenarios are highlighted using a simulation environment with 15 and 135 unique states, respectively.

This paper focuses on robustness to disturbance forces and uncertain payloads. We present a novel formulation to optimize the robustness of dynamic trajectories. A straightforward transcription of this formulation into a nonlinear programming problem is not tractable for state-of-the-art solvers, but it is possible to overcome this complication by exploiting the structure induced by the kinematics of the robot. The non-trivial transcription proposed allows trajectory optimization frameworks to converge to highly robust dynamic solutions. We demonstrate the results of our approach using a quadruped robot equipped with a manipulator.

Person following is an essential task for a robot to serve a person. In an indoor environment, however, the following task can be failed due to the occlusion of the target by structures, e.g., walls or pillars. To address this problem, we propose a method that helps the robot follow the target well and rapidly re-detect the target after missing. The proposed method is an optimization-based path planning which uses a Following Field that we propose in this paper. The following field consists of two sub-fields: the repulsion field getting the robot out of the occluded area, and the target attraction field pushing the robot toward the target. We introduce how to construct the fields and how to integrate the field into a path optimization process. We show that our method works properly for following the target well in a maze consisting of various in-door features.

In this paper we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.

Legged robots have great potential to perform complex loco-manipulation tasks, yet it is challenging to keep the robot balanced while it interacts with the environment. In this paper we investigated the use of additional contact points for maximising the robustness of loco-manipulation motions. Specifically, body-ground contact was studied for its ability to enhance robustness and manipulation capabilities of quadrupedal robots. We proposed equipping the robot with prongs: small legs rigidly attached to the body which create body-ground contact at controllable point-contacts. The effect of these prongs on robustness was quantified by computing the Smallest Unrejectable Force (SUF), a measure of robustness related to Feasible Wrench Polytopes. We applied the SUF to evaluate the robustness of the system, and proposed an effective approximation of the SUF that can be computed at near-real-time speed. We developed a hierarchical quadratic programming based whole-body controller that can control stable interaction when the prongs are in contact with the ground. This novel prong concept and complementary control framework were implemented on hardware to validate their effectiveness by showing increased robustness and newly enabled loco-manipulation tasks, such as obstacle clearance and manipulation of a large object.

Rotary-wing flying machines draw attention within the UAV community for their in-place hovering capability, and recently, holonomic motion over fixed-wings. In this paper, we investigate about the power-optimality in a mono-spinner, i.e., a class of rotary-wing UAVs with one rotor only, whose main body has a streamlined shape for producing additional lift when counter-spinning the rotor. We provide a detailed dynamic model of our mono-spinner. Two configurations are studied: (1) a symmetric configuration, in which the rotor is aligned with the fuselage’s COM, and (2) an asymmetric configuration, in which the rotor is located with an offset from the fuselage’s COM. While the former can generate an in-place hovering flight condition, the latter can achieve trajectory tracking in 3D space by resolving the yaw and precession rates. Furthermore, it is shown that by introducing a tilting angle between the rotor and the fuselage, within the asymmetric design, one can further minimize the power consumption without compromising the overall stability. It is shown that an energy optimal solution can be achieved through the proper aerodynamic design of the mono-spinner for the first time.

In this work, a novel solution to the optimal motion planning problem is proposed, through a continuous, deterministic and provably correct approach, with guaranteed safety and which is based on a parametrized Artificial Potential Field (APF). In particular, Reinforcement Learning (RL) is applied to adjust appropriately the parameters of the underlying potential field towards minimizing the Hamilton-Jacobi-Bellman (HJB) error. The proposed method, outperforms consistently a Rapidly-exploring Random Trees (RRT*) method and consists a fertile advancement in the optimal motion planning problem.

Needle pose tracking is fundamental to achieve a precise and safe insertion in minimally-invasive percutaneous interventions. In this work, a method for estimating the full pose of steerable needles is presented, considering a four-segment Programmable Bevel-Tip Needle (PBN) as a case study. The method estimates also the torsion of the needle that can arise during the insertion because of the interaction forces exerted between the needle and the insertion medium. A novel 3D kinematic model of the PBN is developed and used to predict the full needle pose during the insertion through an Extended Kalman Filter. The filter uses the position measurements provided by electromagnetic sensors located at the tip of the PBN segments as measurement data. The feasibility of the proposed solution is verified through ingelatin experiments, demonstrating remarkable performance with small errors in position (RMSE<; 1 mm) and orientation (RMSE<; 3°) estimation, as well as good accuracy compared to a bespoke geometric pose reconstruction method.

In this paper, a new remote center of motion (RCM) mechanism is presented whose end-effector is able to move through an entire hemisphere. In general, minimally invasive surgery (MIS) applications, an elliptic cone workspace with vertex angles of 60° and 90° gives the surgeon enough freedom to operate. Therefore, the majority of the developed RCM mechanisms have such a cone as the workspace. However, there are still situations in which a larger workspace is required, like the breast ultrasound scanning application in which the RCM mechanisms should be able to move over a hemisphere to do the breast scanning. The proposed RCM mechanism is developed based upon a spherical scissor linkage and benefits from the high stiffness characteristics of parallel structures while eliminating the common problem of linkage collision in parallel structures. It has two rotational degrees of freedom that are decoupled from each other. The Jacobian and the stiffness of the mechanism while considering the bending of the links is calculated through the virtual joints method (VJM). The kinemato-static equations and the methodology for calculating stiffness are described in detail. The optimal arc angle of the mechanism's links is found using a multi-objective genetic algorithm optimization. A prototype of the mechanism is built and forward kinematic of the proposed mechanism is examined experimentally. The experiments indicate that the proposed mechanism is able to provide a hemisphere as its workspace while the RCM point of the mechanism is fixed in the space.

The operational space of an 8-axis arm exoskeleton is partitioned into "tasks" based on the human arm motion, and a task priority approach is implemented to perform the inverse kinematics. The tasks are prioritized in the event that singularities or other constraints such as joint limits render the full desired operational space infeasible. The task reconstruction method is used to circumvent singularities in a deterministic manner so that the arm is never physically in a singular configuration. This is especially advantageous when the arm is fully extended because it allows the hand to move smoothly along the workspace boundary. The task priority inverse kinematics approach is also more computationally efficient than full Jacobian inverse methods and naturally manages the motion of the arm in a more anthropomorphic-friendly manner. The new methodology is demonstrated with four operational tasks on the MGA Exoskeleton.

Despite the many advances in collaborative robotics, collaborative robot control laws remain similar to the ones used in more standard industrial robots, significantly reducing the capabilities of the robot when in proximity to a human. Improving the efficiency of collaborative robots requires revising the control approaches and modulating online and in real-time the low-level control of the robot to strictly ensure the safety of the human while guaranteeing efficient task realization. In this work, an openly simple and fast optimization based joint velocity controller is proposed which modulates the joint velocity constraints based on the robot's braking capabilities and the separation distance. The proposed controller is validated on the 7 degrees-of-freedom Franka Emika Panda collaborative robot.

This paper proposes a new approach for online control law gains adaptation, through the use of neural networks and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to optimize the behavior of the robot with respect to an objective function. The neural network considered takes as input the current observed state as well as its uncertainty, and provides as output the control law gains. It is trained, using the CMA-ES algorithm, on a simulator reproducing the vehicle dynamics. Then, it is tested in real conditions on an agricultural mobile robot at different speeds. The transferability of this method from simulation to a real system is demonstrated, as well as its robustness to environmental changes, such as GPS signal degradation or ground variation. As a result, path following errors are reduced, while ensuring tracking stability.

Nonlinear Model Predictive Control (NMPC) is a powerful and widely used technique for nonlinear dynamic process control under constraints. In NMPC, the state and control weights of the corresponding state and control costs are commonly selected based on human-expert knowledge, which usually reflects the acceptable stability in practice. Although broadly used, this approach might not be optimal for the execution of a trajectory with the lowest positional error and sufficiently "smooth" changes in the predicted controls. Furthermore, NMPC with an online weight update strategy for fast, agile, and precise unmanned aerial vehicle navigation, has not been studied extensively. To this end, we propose a novel control problem formulation that allows online updates of the state and control weights. As a solution, we present an algorithm that consists of two alternating stages: (i) state and command variable prediction and (ii) weights update. We present a numerical evaluation with a comparison and analysis of different trade-offs for the problem of quadrotor navigation. Our computer simulation results show improvements of up to 70% in the accuracy of the executed trajectory compared to the standard solution of NMPC with fixed weights.

As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving. Existing methods often formulate visual place recognition as feature matching, which is computationally expensive for many robotic applications with limited computing power, e.g., autonomous driving and cleaning robot. Inspired by the fact that human beings always recognize a place by remembering salient regions or landmarks that are more attractive or interesting than others, we formulate visual place recognition as saliency reidentification. In the meanwhile, we propose to perform both saliency detection and re-identification in frequency domain, in which all operations become element-wise. The experiments show that our proposed method achieves competitive accuracy and much higher speed than the state-of-the-art feature-based methods. The proposed method is open-sourced and available at https://github.com/wh200720041/SRLCD.git.

Humanoid robots are expected to play a big role at distress sites and disaster sites. There is a variety of multi-contact locomotion forms other than bipedal walking such as crawling through tightly, getting on the rubble by using its knees and elbows, or jumping in and rolling over the obstacles. If such multi-contact locomotion forms can be achieved, robots can reach environments that are currently unreachable, and be able to conduct tasks required at the environments. To achieve this, it is required for robots to bring various parts of its body into contact with the environment like a human. However, it is difficult for parts without 6-axis force sensors to achieve the target force while adapting to the environment against impact force. It is also difficult to measure contact wrenches without 6-axis force sensors. In this paper, by allowing the error of the contact state, we propose online system for realizing dynamic motion which impact force occurs on the parts of the whole body by contact to the environment. In the proposed system, we applied the current-based torque control for joints to make the whole body parts of the robot adapt to the environment, and we modified motion in real time to stabilize zmp by estimating contact wrenches at the contact positions where force sensors are not mounted. In addition, at the motion planning, we generated more feasible motions for a robot applying torque control by using evolutionary computation which advances the search with the behavior of torque control. We demonstrate that the proposed system is effective by showing experimental results of sitting posture locomotion using a JAXON robot in which impact force occur on the back of the thighs which have no force sensors.