IROS2019国际学术会议论文集 2584

Neural Control with an Artifi cial Hormone System for Energy Effi cient Compliant Terrain Locomotion and Adaptation of Walking Robots Jettanan Homchanthanakul1 Potiwat Ngamkajornwiwat2 Pitiwut Teerakittikul2 and Poramate Manoonpong1 3 Abstract In order to use walking robots for exploration in a real complex environment an adaptive control system is required to allow them to successfully and effi ciently traverse the terrains To achieve this we propose here our adaptive locomotion control technique of a walking robot It is based on a modular structure combining neural control with an artifi cial hormone system The neural control coordinates all leg joints of the robot and generates its locomotion with various insect like gaits In parallel the artifi cial hormone system uses the motor commands from the neural control and foot contact feedback to estimate the walking state and automatically adapt the joint movements with respect to the terrain The adaptability is quickly achieved in an online manner within a few seconds Robot walking experiments show that this adaptive control technique enables a six legged robot to adapt to various diffi cult terrains with energy effi ciency Such terrains include sand loose ground sponge with different softness levels soft compliant ground grass vegetated ground and fl oor pavement hard ground The technique does not require robot kinematics or an environmental model and can therefore be potentially applied to different legged robots to achieve stable online adaptation and energy effi cient locomotion on unpredictable compliant terrains I INTRODUCTION Legged robots have been mostly used for exploration in hazardous areas and unpredictable terrains This is due to their versatility and ability to perform different movements to walk effectively over such terrains Recently legged robots like hexapod have been devel oped with statically stable gaits to ensure stability during locomotion 1 Most of them are inspired by insects e g cockroaches 2 3 and ants 4 for traversing through uneven terrains and dung beetles 5 for forming and manip ulating an object To achieve this their control mechanisms should be able to generate different basic movements walk ing forward backward turning left right According to 6 a biologically inspired control mechanism based on a central pattern generator CPG has been developed to allow the robot to perform insect like leg movements and a multitude of different behavioral patterns such as different gaits and different walking directions 1J Homchanthanakul is with School of Information Science and Tech nology at Vidyasirimedhi Institute of Science and Technology Thailand e mail jettanan h s18 vistec ac th 2P Ngamkajornwiwat and P Teerakittikul are with Institute of Field Robotics King Mongkut s University of Technology Thonburi Bangkok Thailand e mail potiwat n pitiwut fi bo kmutt ac th 1 3P Manoonpong is with the Embodied AI and Neurorobotics Labo ratory Center for Biorobotics The M rsk Mc Kinney M ller Institute University of Southern Denmark Odense Denmark and also with School of Information Science and Technology at Vidyasirimedhi Institute of Science and Technology Thailand e mail poma mmmi sdu dk When the robot faces an unexpected situation adaptive control mechanisms must be introduced to allow it to deal with the situation appropriately For example in 7 kine matic and dynamic robot models are used to plan its foothold position for avoiding obstacles In this case they ignore the robot s body that it might get stuck or hit the obstacle while passing through it To overcome this problem one should be able to raise the body as high as possible and this requires high energy consumption leading to locomotion energy ineffi ciency In 8 an error signal is used between a joint command signal and the actual joint position together with a support vector machine SVM classifi er to differentiate the terrains and with the leg movement adjusted accordingly Since this technique must fi rst be trained in an offl ine mode it lacks the online adaptability to deal with unpredictable terrains In 9 inverse kinematics are used to generate the trajectory of the leg and tactile sensors These are simple on off switch sensors to detect the ground While this technique can allow the robot to navigate on uneven terrains it is diffi cult to allow the robot to effectively adjust its leg movement when walking on compliant terrains This is because the switch based tactile sensors cannot provide suffi cient information about the terrains In 10 the re searchers developed a closed loop control approach based on the inverse kinematic model and force sensor feedback at the robot s feet to enable it to sense the softness of the terrain and react accordingly However the study does not focus on energy effi ciency To address the remaining problems in terms of energy effi cient locomotion with online adaptation on unpredictable compliant terrains here we propose the adaptive locomo tion control technique of a legged robot combining neural control with an artifi cial hormone system Other researchers 11 have shown that the artifi cial hormone system can help a wheeled robot to increase the mission success rate on unpredictable terrains by adapting its moving speed In 12 the artifi cial hormone system is used for the online gait adaptation to deal with unexpected leg damage in a simulated hexapod robot In our study for the fi rst time the use of an artifi cial hormone system with neural control is proposed for the energy effi cient locomotion and stable online adaptation of a legged robot on unpredictable compliant terrains Thus the main contribution of this paper is to propose a new bio inspired adaptive locomotion control technique based on neural control and an artifi cial hormone system The tech nique does not require robot kinematics or an environmental model consequently it can potentially be applied to different legged robots to achieve stable online adaptation and energy 2019 IEEE RSJ International Conference on Intelligent Robots and Systems IROS Macau China November 4 8 2019 978 1 7281 4003 2 19 31 00 2019 IEEE5475 effi cient locomotion on unpredictable compliant terrains In addition we also propose a different walking strategy to other walking robots Here the neural control acts as an open loop control system to generate basic locomotion making the robot walk with a very low center of mass i e facilitating low ground clearance while the adaptive hor mone system acts as a closed loop control system which will automatically adjust the leg joint movements according to the terrain such that low ground clearance can be maintained In this way the robot can perform energy effi cient locomotion and walk with stability on different compliant terrains II MATERIALS ANDMETHODS In order to explore and test the performance of the novel control mechanism in a physical system we used a biologically inspired walking robot as our experimental platform Thus the fi rst section describes the biomechanical setup of the robot followed by details of the proposed neural control with an artifi cial hormone system NCAS which represents the main contribution of this work Here some results are described alongside the introductory components from which they are mostly derived since this provides a better understanding of their functionalities A Biologically Inspired Walking Robot The walking robot consists of six identical legs Fig 1 a Each leg has three joints three degrees of freedom the thoraco coxal TC joint enables forward and backward movements the coxo trochanteral CTr joint enables elevation and depression of the leg and the femorotib ial FTi joint enables extension and fl exion of the tibia Figs 1 b and c The morphology of this multi jointed leg is modeled on the basis of an insect leg but the tarsal segments are ignored The robot body consists of two segments a front segment where two front legs are installed and a central body segment to which the two middle and two hind legs are attached connected by one active backbone joint This backbone joint can lean the front body segment upwards and bend it downwards for climbing over an obstacle 13 although when walking it stays at zero degrees In total the robot has 19 active joints three joints at each leg and one backbone joint driven by servomotors HSR 5990 TG Besides the motors the robot has various sensors including two ultrasonic sensors US at the front body part six foot contact force FC sensors in its legs six infrared refl ex IR sensors at the front of its legs one current sensor CS and one inclinometer IM sensor inside the body as well as three light dependent LD sensors These sensors are used to generate stimulus induced behavior such as phototropism and obstacle avoidance 14 In this study only foot contact sensors are used to achieve energy effi cient locomotion and stable adaptation We use a Multi Servo IO Board MBoard to digitize all sensory input signals and generate a pulse width modulated signal to control the servomotor position To conduct the TC joint Forward Max Backward Min 3 2 1 R1 R2 R3 L1 L2 L3 TR BJ CLCR FL FR TR TR 3 2 1 TL TL TL CR CR 1 2 3 1 2 3 FR FR CL CL FL FL 1 2 3 1 2 3 Min 50 Max 50 Min 60 Max 60 Min 135 Max 0 Min 60 Max 60 Min 40 Max 40 Min 60 Max 60 Min 50 Max 50 o o oo oo oo oo oo oo FTi joint CTr joint L1 Extension Max Flexion Min Elevation Max Depression Min TL FL 1 1 1 c b a R1 R2 R3 L1 L2 L3 Foot contact sensor Current sensor TC joint CTr joint FTi joint Tibia CL 0 deg 0 deg 0 deg 0 deg 0 deg Min 135 Max 0 o o Min 135 Max 0 o o Fig 1 a A biologically inspired walking robot consisting of six three jointed legs a segmented body structure with one active backbone joint and various sensors Each tibia contains a spring compliant element to absorb the impact force during touchdown In addition a passive coupling is installed at each joint in order to yield passive compliance and protect the motor shaft b Movement of the CTr and FTi joints c Location of the motor neurons on the robot and movement of the TC joints Minimum and maximum angles can be observed at all joints of the right and left legs for which the same values are set Abbreviations BJ backbone joint TL R thoraco coxal joints of left right legs CL R coxa trochanteral joints of left right legs and FL R femur tibia joints of left right legs robot walking experiments the MBoard was connected to a personal computer on which to implement the robot controller The update frequency was 27 Hz Electrical power supply was provided by batteries one 11 1 V lithium poly mer 3200 mAh for all servomotors and two 11 1 V lithium polymers 910 mAh for the electronic board MBoard and all sensors for more details see 6 B Adaptive Locomotion Control Architecture The control architecture for the energy effi cient locomo tion and stable adaptation of a biologically inspired walking robot has been developed on the basis of a modular structure Fig 2 It consists of two main components neural control gray area in Fig 2 and an artifi cial hormone system yellow area in Fig 2 The neural control basically coordinates all leg joints of the robot and generates its locomotion with var ious insect like gaits In the yellow area the local leg control was used as a refl exive system to allow the robot to be able to deal with the change of terrains or losing ground contact 6 In this study the artifi cial hormone system is used to replace the local leg control as an adaptive system It uses the motor commands generated by the neural control efference copies and foot contact feedback for online adaptation of the leg joint movements while walking on unpredictable compliant 5476 Error E Body Height Control BH Changing CTr FTi joints Hormone concentration Hc Hormone gland Hg Expected foot contact feedback EFC Real foot contact feedback FC Efference copies CTr joint signals Receptor While the receptor receives the Hc to control the target the Hc is being reduced by the hormone receptor binding effect Hr Hormone stimulus S Fig 2 Adaptive locomotion controller The controller generates energy effi cient robot locomotion and stable adaptation on complex compliant terrain This adaptive closed loop controller consists of neural control and an artifi cial hormone system see text for the functional description terrains In doing so we can achieve adaptive energy effi cient locomotion In the following section we describe our neural control and the newly introduced artifi cial hormone system in detail C Neural Control for Locomotion Generation This robot uses the neural control to generate basic walk ing patterns The neural control has four main components Fig 2 1 a central pattern generator CPG with neu romodulation 2 a CPG postprocessing CPG post 3 a phase switching network PSN and 4 velocity regu lating networks VRNs These components are described only briefl y since although not key contributors here they are necessary for supporting the artifi cial hormone system described below to generate adaptive energy effi cient lo comotion Further details of the neural components can be observed in our previous work 6 All these neuron components are standard additive non spiking neurons Their activity aidevelops according to ai t n j 1 Wij tanh aj t 1 Bii 1 n 1 where n denotes the number of neurons Birepresents a fi xed internal bias term together with a stationary input to neuron i andWijthe synaptic strength of the connection from neuron j to neuron i The output oiof the neuron is given by the hyperbolic tangent tanh transfer function tanh i e oi tanh ai Note that the update frequency of the neural controller is approximately 27 Hz The CPG the main network to generate periodic signals for walking is a recurrent neural network with two neurons Fig 3 Recurrent weightsW11 22are set to 1 4 while weights between both neurons are determined by W12 0 18 MI W21 W12 MI is the modulatory input used to generate different walking patterns or gaits Figure 4 shows examples of four different animal like gaits the slow wave gait MI 0 03 caterpillar gait MI 0 10 tetrapod gait MI 0 13 MI C1C2 W W WW 12 21 1122 Fixed synapses Excitatory Inhibitory Modulated synapses Excitatory Inhibitory Fig 3 CPG mechanism with neuromodulation The modulatory input MI modifi es the synaptic weights of the CPG modulated synapses resulting in the modulation of CPG outputs and fast tripod gait MI 0 14 It should be noted that other MI values can generate different gaits We observed up to 20 different gaits during MI changes However in this study four main animal like gaits were used to show the proposed adaptive locomotion control function Fig 2 The CPG post is a mechanism for receiving the periodic CPG signals converting them into asymmetric ascending and descending slope signals Fig 4 a Such signals lead to smooth leg movements The outputs of the CPG post are transmitted further to the motor neurons through the PSN and VRNs The PSN is a feed forward network consisting of four hierarchical layers with 12 neurons 6 It can reverse the phase of the CPG post signals driving the CTr and FTi joints to obtain sideways walking 15 The VRNs are two feed forward networks 6 Each network consisting of seven neurons and one hidden layer receives the PSN output and can regulate it to control the three ipsilateral TC joints on one side Using the VRNs various walking directions can be achieved such as forward backward turning left right turning in different radians or curve walking in forward and backward directions 15 Final control signals from the PSN and VRNs are transmitted to the motor neurons The motor neuron outputs are sent to all leg joints for position control Taken together this setup leads to bio inspired leg coordi nation While the CPG and CPG post set different asymmet ric periodic movements for the legs leading to different gaits the PSN and VRNs provide a certain steering capability As a consequence acting as an open loop system this neural control enables the robot to move with different gaits in any direction by manually setting its control parameters 6 D Artifi cial Hormone System for Stable Adaptation While the neural control described above can generate multiple gaits Fig 4 and different walking directions 15 its setup does not have an adaptive mechanism with sensory feedback to allow the robot to effi ciently walk on unpre dictable compliant terrains One strategy without adaptation involves letting it walk on unpredictable terrains with high ground clearance This strategy has been commonly used in most walking robots 16 17 18 and while it could ensure that the robot will not get stuck it might lead to high power consumption since the motors of the CTr joints have to produce high torque to carry a huge load continuously 5477 0 20 40 60 0 20 40 60 180 120 60 0 R1 R2 R3 L1 L2 L3 0 20 40 60 180 120 60 0 180 120 60 0 0 20 40 60 180 120 60 0 0 20 40 60 0 20 40 60 0 20 40 60 0 20 40 60 Wave Gait MI 0 03 Caterpillar Gait MI 0 1 Tetrapod Gait MI 0 13 Fast Tripod Gait MI 0 14 a b c d a b c d 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps R1 R2 R3 L1 L2 L3 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps a b c d a b c d R1 R2 R3 L1 L2 L3 R1 R2 R3 L1 L2 L3 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps 0 50 100 150 200 250 300 350 400 450 500 Time steps Fig 4 Examples of four hexapod gaits generated