A Neuromechanical Controller of a Hexapod Robot for Walking on Sponge, Gravel and Snow Surfaces

Physiological studies suggest that the integration of neural circuits and biomechanics (e.g., muscles) is a key for animals to achieve robust and efficient locomotion over challenging surfaces. Inspired by these studies, we present a neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. It consists of a modular neural network (MNN) and virtual agonist-antagonist mechanisms (VAAM, i.e., a muscle model). The MNN coordinates 18 joints and generates basic locomotion while variable joint compliance for walking on different surfaces is achieved by the VAAM. The changeable compliance of each joint does not depend on physical compliant mechanisms or joint torque sensing. Instead, the compliance is altered by two internal parameters of the VAAM. The performance of the controller is tested on a physical hexapod robot for walking on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. The experimental results show that the controller enables the hexapod robot to achieve variably compliant leg behaviors, thereby leading to more energy-efficient locomotion on different surfaces. In addition, a finding of the experiments complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces. Introduction There are increasing demands for robots to walk on a series of diverse terrains (Ozcan et al., 2010; Qian et al., 2012). However, few robots can walk on soft elastic (e.g., sponge) and loose (e.g., gravel and snow) surfaces. This is because traversing these surfaces efficiently requires variable compliance of legs (Spence, 2011; Bermudez et al., 2012). Traditionally, the variable compliance of legged robots can be achieved by passive compliance mechanisms (Ham et al., 2009) and/or active compliance control (Gorner and Hirzinger, 2010). For example, by using active compliance control with joint torque feedback, a hydraulically actuated quadruped robot (i.e., HyQ, 90 kg) has been developed for moving over terrains (Boaventura et al., 2012). Nevertheless, the complex mechanical and sensing components of the HyQ robot greatly increase its size and mass, thereby not fitting for developing small legged robots. Yet a small six-legged robot (i.e., EduBot, 3 kg) has been designed by using physically passive variable compliant legs (Galloway et al., 2011). The experimental results show that stiffer legs allow its faster locomotion on soft surfaces. In contrast to the robot experimental results, owing to energy efficiency, biological study has shown that cockroaches (i.e., Blaberus discoidalis) use their softer legs on soft surfaces (Spence et al., 2010; Spence, 2011). This finding reveals a neuromehcanical control strategy of hexapod locomotion on soft surfaces. In fact, the strategy is not the result of a single component rather interactions between a nervous system, a musculoskeletal system and the environment. Inspired by this, the work here proposes a novel neuromechanical controller of a hexapod robot for walking on soft elastic and loose surfaces. The neuromechanical controller consists of a modular neural network (MNN) coordinating leg movement and virtual agonist-antagonist mechanisms (VAAM) changing the compliance of legs. The changeable compliance is simply achieved by altering two internal parameters of the VAAM without physical passive compliant mechanisms (Ham et al., 2009) or joint torque sensing (Gorner and Hirzinger, 2010). Employing this controller allows the robot to walk on different surfaces with energy efficiency. Besides, a finding of robot walking complies with the finding of physiological experiments on cockroach locomotion on soft elastic surfaces (Spence et al., 2010; Spence, 2011). Neuromechanical Controller of a Hexapod Robot The experimental robot is a hexapod robot (5.4 kg) (see Fig. 1 (a)). Each three-jointed leg has a TC (Thoraco Coxal) joint allowing the motions of forward and backward, a CTr (Coxa Trochanteral) joint allowing the motions of elevation and depression, and a FTi (Femur Tibia) joint allowing the motions of extension and flexion (see Fig. 1 (b)). Each joint is physically driven by a standard servo motor. There is a force sensor used for detecting the analog signal at each leg (see fc1−6 in Fig. 1 (a)). A current sensor installed inside the body of the hexapod robot is used to detect electrical current used for all motors and sensors of the hexapod robot. For more details of the hexapod robot, we refer to (Manoonpong et al., 2013). Figure 1: A hexapod robot (a) Six legs and six foot sensors fc(1−6). (b) A three-jointed leg. Modular Neural Network (MNN) The modular neural network (MNN) is a biologicallyinspired hierarchical neural controller (McCrea and Rybak, 2008), which generates signals for leg and joint coordination of the hexapod robot. The MNN consists of a central pattern generator (CPG, see Fig. 2 (a)), a phase switch module (PSM, see Fig. 2 (b)) and two velocity regulating modules (VRMs, see Fig. 2 (c)). All neurons of the MNN are modelled as discrete-time non-spiking neurons. The activity Hi of each neuron develops according to: