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Introduction to Biologically Inspired Robotics
Published in Yunhui Liu, Dong Sun, Biologically Inspired, 2017
The behaviors of animals and other living creatures inspire the development of new ideas for controlling the motion or behaviors of robots. In principle, robotic control and biological control systems are similar. They all work on the basis of sensory motor control. Biological systems are controlled by expansion and contraction of the muscles based on information collected by the biological sensors such as eyes, skin, ears, nose, etc. Robotic systems are controlled based on information feedback from robotic sensors using their actuators. The underlying principle for both robotic and biological systems is feedback control. Traditionally, researchers have designed control algorithms for robots using conventional methodologies and theories in controlling engineering. Different from traditional approaches, biologically inspired controllers are designed based on new philosophy inspired by biological systems Typical examples of biologically inspired approaches for robot control are behavior control, proposed by Brooks at the Massachusetts Institute of Technology (Brooks 1987); iterative learning control, developed by Arimoto et al. (1985); and intelligent control methods including genetic algorithms (Parker, Khoogar, and Goldberg 1989) and swarm control (Fukuda and Kawauchi 1990).
A fast optical proximity sensor skin that contains an analog computing circuit and can cover an entire link
Published in Advanced Robotics, 2023
The sensor proposed in this paper has an analog computing circuit that expanded the one developed in [11,12] to meet the sensor skin requirements described in Section 1.1. The prior computing circuit only consists of resistors connecting distributed detectors and can rapidly calculate the magnitude and location of the centroid of the output distribution in 1D or 2D space. Moreover, it only needs a few wires to transfer centroid information. The basic idea of this circuit is presented in [33]. The context of using the circuit for proximity sensors was investigated in [34] in detail. The papers mentioned earlier [11,12] expanded the circuit to a proximity palm sensor and finger sensor, respectively. The properties of these sensors with regard to fast response (1 kHz), distributed nature of sense, and less wiring are appropriate for robotic sensors, especially with regard to grasping objects. Thus, ensuing studies were continued, and they were summarized in [35]. Although a proximity sensor with the above-mentioned computing circuit achieved most of the above requirements for sensor skin, it cannot cover entire links because it is based on a square-shaped sensor (Figure 1). There is a gap if the sensor mounted on a flexible substrate covers around a link (Figure 1(a)). The discontinuity might cause misdetection because the sensor calculates a centroid position. A cylindrical sensor is introduced in [34], to address the discontinuity problem, and it is thoroughly discussed in [36]. The sensor covers a cylinder from upward rather than around it (Figure 1(b)). As the sensor only provides two-dimensional information for the centroid position, it should be referred to as a ‘ring sensor’. Although [36] proposed connecting multiple detectors parallel to expand the sensing area in the height direction, the sensor cannot detect height information because the parallel detectors are assumed to be a single detector while computing. The multistage ring sensor (described in [36]) is adequate for mobile robots, but it is insufficient to cover a link. Arita et al. [37] developed a slightly different circuit. The circuit is divided into two layers and has a structure similar to the aforementioned ring-shaped circuit, allowing for simultaneous azimuth and elevation angle detection. Although the two-layered circuit can obtain height information, it cannot cover a large area because the element number in the height direction is only two.