Surya Girinatha Nurzaman

Active sensing system with in situ adjustable sensor morphology.

Background Despite the widespread use of sensors in engineering systems like robots and automation systems, the common paradigm is to have fixed sensor morphology tailored to fulfill a specific application. On the other hand, robotic systems are expected to operate in ever more uncertain environments. In order to cope with the challenge, it is worthy of note that biological systems show the importance of suitable sensor morphology and active sensing capability to handle different kinds of sensing tasks with particular requirements. Methodology This paper presents a robotics active sensing system which is able to adjust its sensor morphology in situ in order to sense different physical quantities with desirable sensing characteristics. The approach taken is to use thermoplastic adhesive material, i.e. Hot Melt Adhesive (HMA). It will be shown that the thermoplastic and thermoadhesive nature of HMA enables the system to repeatedly fabricate, attach and detach mechanical structures with a variety of shape and size to the robot end effector for sensing purposes. Via active sensing capability, the robotic system utilizes the structure to physically probe an unknown target object with suitable motion and transduce the arising physical stimuli into information usable by a camera as its only built-in sensor. Conclusions/Significance The efficacy of the proposed system is verified based on two results. Firstly, it is confirmed that suitable sensor morphology and active sensing capability enables the system to sense different physical quantities, i.e. softness and temperature, with desirable sensing characteristics. Secondly, given tasks of discriminating two visually indistinguishable objects with respect to softness and temperature, it is confirmed that the proposed robotic system is able to autonomously accomplish them. The way the results motivate new research directions which focus on in situ adjustment of sensor morphology will also be discussed.

SVAS3: Strain Vector Aided Sensorization of Soft Structures.

Soft material structures exhibit high deformability and conformability which can be useful for many engineering applications such as robots adapting to unstructured and dynamic environments. However, the fact that they have almost infinite degrees of freedom challenges conventional sensory systems and sensorization approaches due to the difficulties in adapting to soft structure deformations. In this paper, we address this challenge by proposing a novel method which designs flexible sensor morphologies to sense soft material deformations by using a functional material called conductive thermoplastic elastomer (CTPE). This model-based design method, called Strain Vector Aided Sensorization of Soft Structures (SVAS3), provides a simulation platform which analyzes soft body deformations and automatically finds suitable locations for CTPE-based strain gauge sensors to gather strain information which best characterizes the deformation. Our chosen sensor material CTPE exhibits a set of unique behaviors in terms of strain length electrical conductivity, elasticity, and shape adaptability, allowing us to flexibly design sensor morphology that can best capture strain distributions in a given soft structure. We evaluate the performance of our approach by both simulated and real-world experiments and discuss the potential and limitations.

Guided Self-Organization in a Dynamic Embodied System Based on Attractor Selection Mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics.

Soft Robotics [TC Spotlight]

The use of soft and deformable materials in robotic systems has increasingly gained interest in recent years. Because of its potential to deal with uncertain and unstructured task environments, soft-body robotic systems are expected to be able to accomplish tasks such as grasping and manipulation of unknown objects, locomotion in rough terrains, and performing flexible interactions between robots and living cells or human bodies. In addition, soft robotics pushes the boundary of visionary research topics such as growing, self-repairing, and self-replicating robots. Examples of the recent major achievements in soft robotics are shown in Figure 1.

A Sliding Mode Observer for Infinitely Unobservable Descriptor Systems

In existing work of sliding mode observers (SMOs) for descriptor systems, a necessary condition is that the system must be infinitely observable. This paper presents a scheme that circumvents that condition, by reformulating the system as a reduced-order system where certain structures in the system matrix are manipulated and certain states are treated as unknown inputs. Following that, an SMO is implemented on the reduced-order system where state and fault estimation can be achieved. Necessary and sufficient conditions of this scheme are also presented. Finally, a simulation example shows the effectiveness of the proposed scheme.

Soft-Material Robotics

There has been a boost of research activities in robotics using soft materials in the past ten years. It is expected that the use and control of soft materials can help realize robotic systems that are safer, cheaper, and more adaptable than the level that the conventional rigid-material robots can achieve. Contrary to a number of existing review and position papers on soft-material robotics, which mostly present case studies and/or discuss trends and challenges, the review focuses on the fundamentals of the research field. First, it gives a definition of softmaterial robotics and introduces its history, which dates back to the late 1970s. Second, it provides characterization of soft-materials, actuators and sensing elements. Third, it presents two general approaches to mathematical modelling of kinematics of soft-material robots; that is, piecewise constant curvature approximation and variable curvature approach, as well as their related statics and dynamics. Fourth, it summarizes control methods that have been used for soft-material robots and other continuum robots in both model-based fashion and model-free fashion. Lastly, applications or potential usage of soft-material robots are described related to wearable robots, medical robots, grasping and manipulation.

Goal-directed multimodal locomotion through coupling between mechanical and attractor selection dynamics.

One of the most significant challenges in bio-inspired robotics is how to realize and take advantage of multimodal locomotion, which may help robots perform a variety of tasks adaptively in different environments. In order to address the challenge properly, it is important to notice that locomotion dynamics are the result of interactions between a particular internal control structure, the mechanical dynamics and the environment. From this perspective, this paper presents an approach to enable a robot to take advantage of its multiple locomotion modes by coupling the mechanical dynamics of the robot with an internal control structure known as an attractor selection model. The robot used is a curved-beam hopping robot; this robot, despite its simple actuation method, possesses rich and complex mechanical dynamics that are dependent on its interactions with the environment. Through dynamical coupling, we will show how this robot performs goal-directed locomotion by gracefully shifting between different locomotion modes regulated by sensory input, the robots mechanical dynamics and an internally generated perturbation. The efficacy of the approach is validated and discussed based on the simulation and on real-world experiments.

Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective.

Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perception.

Soft Robotics and Morphological Computation [From the Guest Editors]

The articles in this special section focus on the development and manufacture of soft robotics.

A self organization approach to goal-directed multimodal locomotion based on Attractor Selection Mechanism

The realization and utilization of multimodal locomotion to enable robots to accomplish useful tasks is a significantly challenging problem in robotics. Related to the challenge, it is crucial to notice that the locomotion dynamics of the robots is a result of interactions between a particular control structure and its body-environment dynamics. From this perspective, this paper presents a simple control structure known as Attractor Selection Mechanism that enables a robot to self organize its multiple locomotion modes for accomplishing a goal-directed locomotion task. Despite the simplicity, the approach enables the robot to automatically explore different body-environment dynamics and stabilize onto particular attractors which corresponds to locomotion modes relevant to accomplish the task. The robot used throughout the paper is a curved-beam hopping robot, which despite its simple actuation method, possesses rich and complex body-environment dynamics.