- Award ID(s):
- 2024247
- NSF-PAR ID:
- 10379139
- Date Published:
- Journal Name:
- International Conference on Robotics and Automation
- Page Range / eLocation ID:
- 1190 to 1196
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Numerous soft and continuum robotic manipulators have demonstrated their potential for compliant operation in highly unstructured environments or near people. Despite their recent popularity, modeling of their smooth bending deformation remains a challenge. For soft continuum manipulators, the widespread, constant curvature approach to modeling is inadequate for modeling some deformations that occur in practice, such as combined bending and twisting deformations. In this paper, we extend the classical Cosserat rod approach to model a variable-length, pneumatic soft continuum arm. We model the deformation of a pneumatically driven soft continuum manipulator, and the model is then compared against experimental data collected from a three degree of freedom, pneumatically actuated, soft continuum manipulator. The model shows good agreement in capturing the overall behavior of the bending deformation, with mean Euclidean error at the tip of the robot of 2.48 cm for a 22 cm long robot. In addition, the model shows good numerical stability for simulating long duration computations.more » « less
-
null (Ed.)Abstract An extensible continuum manipulator (ECM) has specific advantages over its nonextensible counterparts. For instance, in certain applications, such as minimally invasive surgery or pipe inspection, the base motion might be limited or disallowed. The additional extensibility provides the robot with more dexterous manipulation and a larger workspace. Existing continuum robot designs achieve extensibility mainly through artificial muscle/pneumatic, extensible backbone, concentric tube, and base extension, etc. This article proposes a new way to achieve this additional motion degree-of-freedom by taking advantage of the rigid coupling hybrid mechanism concept and a flexible parallel mechanism. More specifically, a rack and pinion set is used to transmit the motion of the i-th subsegment to drive the (i+1)-th subsegment. A six-chain flexible parallel mechanism is used to generate the desired spatial bending and one extension mobility for each subsegment. This way, the new manipulator can achieve tail-like spatial bending and worm-like extension at the same time. Simplified kinematic analyses are conducted to estimate the workspace and the motion nonuniformity. A proof-of-concept prototype was integrated to verify the mechanism’s mobility and to evaluate the kinematic model accuracy. The results show that the proposed mechanism achieved the desired mobilities with a maximum extension ratio of 32.2% and a maximum bending angle of 80 deg.more » « less
-
null (Ed.)
Abstract An extensible continuum manipulator (ECM) has specific advantages over its non-extensible counterparts. For instance, in certain applications, such as minimally invasive surgery or tube inspection, the base motion might be limited or disallowed. The additional extensibility provides the robot with more dexterous manipulation and larger workspace. Existing continuum robot designs achieve extensibility mainly through artificial muscle/pneumatic, extensible backbone, concentric tube, and base extension etc. This paper proposes a new way to achieve this additional motion degree of freedom by taking advantage of the rigid coupling hybrid mechanism concept and a flexible parallel mechanism. More specifically, a rack and pinion set is used to transmit the motion of the i-th subsegment to drive the (i+1)-th subsegment. A six-chain flexible parallel mechanism is used to generate the desired spatial bending and one extension mobility for each subsegment. This way, the new manipulator is able to achieve tail-like spatial bending and worm-like extension at the same time. A proof-of-concept prototype was integrated to verify the mobility of the new mechanism. Corresponding kinematic analyses are conducted to estimate the workspace and the motion non-uniformity.
-
Unmanned aerial manipulators have been growing in popularity over the years, alongside the complexity of the tasks they undertake. Many of these tasks include physical interaction with the environment, where a force control or sensing component is desirable. In these types of applications, the forces and torques, or the wrench, acting on the robot by the environment must be known. This paper presents a wrench observer based on an Extended Kalman filter (EKF), and compares it against acceleration-based, momentum-based, and hybrid wrench observers. Simulations using each of these observers are conducted with an underactuated aerial manipulator composed of a hexarotor with coplanar propellers and a 2-DOF manipulator. Measurement noise on par with what is expected in real-world applications is added to the sensor signals, and results show that the EKF-based wrench observer is superior at noise reduction and wrench estimation in many cases compared to the other observers.more » « less
-
Abstract Autonomous aerial manipulators have great potentials to assist humans or even fully automate manual labor-intensive tasks such as aerial cleaning, aerial transportation, infrastructure repair, and agricultural inspection and sampling. Reinforcement learning holds the promise of enabling persistent autonomy of aerial manipulators because it can adapt to different situations by automatically learning optimal policies from the interactions between the aerial manipulator and environments. However, the learning process itself could experience failures that can practically endanger the safety of aerial manipulators and hence hinder persistent autonomy. In order to solve this problem, we propose for the aerial manipulator a self-reflective learning strategy that can smartly and safely finding optimal policies for different new situations. This self-reflective manner consists of three steps: identifying the appearance of new situations, re-seeking the optimal policy with reinforcement learning, and evaluating the termination of self-reflection. Numerical simulations demonstrate, compared with conventional learning-based autonomy, our strategy can significantly reduce failures while still can finish the given task.