Research article
Neurobiologically-based control system for an adaptively walking hexapod William A. Lewinger and Roger D. Quinn Case Western Reserve University, Cleveland, Ohio, USA Abstract Purpose – Biological systems such as insects have often been used as a source of inspiration when developing walking robots. Insects’ ability to nimbly navigate uneven terrain, and their observed behavioral complexity have been a beacon for engineers who have used behavioral data and hypothesized control systems to develop some remarkably agile robots. The purpose of this paper is to show how it is possible to implement models of relatively recent discoveries of the stick insect’s local control system (its thoracic ganglia) for hexapod robot controllers. Design/methodology/approach – Walking control based on a model of the stick insect’s thoracic ganglia, and not just observed insect behavior, has now been implemented in a complete hexapod able to walk, perform goal-seeking behavior, and obstacle surmounting behavior, such as searching and elevator reflexes. Descending modulation of leg controllers is also incorporated via a head module that modifies leg controller parameters to accomplish turning in a role similar to the insect’s brain and subesophageal ganglion. Findings – While many of these features have been previously demonstrated in robotic subsystems, such as single- and two-legged test platforms, this is the first time that the neurobiological methods of control have been implemented in a complete, autonomous walking hexapod. Originality/value – The methods introduced here have minimal computation complexity and can be implemented on small robots with low-capability microcontrollers. This paper discusses the implementation of the biologically grounded insect control methods and descending modulation of those methods, and demonstrates the performance of the robot for navigating obstacles and performing phototaxis. Keywords Robotics, Control systems, Insects Paper type Research paper
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single- and two-legged test platforms (Lewinger and Quinn, 2008), but not yet as a complete and mechanical hexapod. The hexapod described in this paper, Biologically inspired legged locomotion – Ant – autonomous (BILL-Ant-a) (Figure 1), is the first physical and autonomous hexapod that uses a distributed walking control system based on the neurobiology of stick insects. A modeled biological network coordinates the joints in each leg and generates the desired stepping actions based on internal pattern generators that are modified by external influences from the environment. By employing environmental cues to control stepping behaviors, the robot is fully adaptive to irregular terrain and is also able to navigate obstacles using insect-based reflexes such as the elevator and searching reflexes. An additional module performs some of the functions of the insect’s brain and subesophageal ganglion. This module monitors and actuates a pair of light-sensitive receptors in response to an illuminated goal. When the goal is not located before the robot, a neck actuator rotates to locate the direction of the goal. Then, the same module uses descending modulation to uniquely alter the parameters of the six-leg controllers. These modified parameters allow the robot to perform turning maneuvers, much like an actual insect.
Industrial Robot: An International Journal 38/3 (2011) 258– 263 q Emerald Group Publishing Limited [ISSN 0143-991X] [DOI 10.1108/01439911111122752]
This paper is an updated and revised version of the paper originally presented at the 13th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines (CLAWAR, 2010), Nagoya, Japan, 31 August – 3 September 2010.
1. Introduction Because of their extreme mobility and agile adaptability to irregular terrain, insects have long been an inspiration for the designers of mobile and legged robots. Early hexapod robots such as Genghis (Brooks, 1989) and later creations such as Tarry (Frik et al., 1999) implemented insect-like mobility based on observations of insect behaviors. The inter-leg coordination system developed by Holk Cruse (Cruse et al., 1991, 1998) has been widely implemented in legged hexapods and its basis is in behavioral experiments that qualitatively analyzed insect walking behaviors. These robots are impressive in their ability to navigate irregular terrain, track goal-based objects, and mimic insectlike walking. However, their functionality is based on observed insect walking, and not the underlying neurobiological control system. Neurobiological control methods have previously been demonstrated for simulated hexapods (Ekeberg et al., 2004; Lewinger et al., 2006) and mechanical subsystems, such as
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Control system for an adaptively walking hexapod
Industrial Robot: An International Journal
William A. Lewinger and Roger D. Quinn
Volume 38 · Number 3 · 2011 · 258 –263
Figure 1 BILL-Ant-a (biologically inspired legged locomotion – Ant – autonomous)
Figure 2 BILL-Ant-a front-left leg with joints and sensors labeled
ThC angle
FTi joint
ThC joint
FTi angle
CTr angle CTr joint Obstacle contact switch Foot FSR
the number of motor and sensor ports (four pulse width modulation servo controller, five digital I/O, and five analog to digital converter ports). The distributed controller network communicates via inter-integrated circuit bus. A seventh BrainStem microcontroller serves two functions. The primary role is to act as the brain and subesophageal ganglion where it processes sensory information from two phototransistors mounted on the neck. It actuates the neck in order to maintain balanced light levels between the two sensors (the visual stimulus and light-based goal for the robot). The neck position is then used to modulate setpoint values in each of the leg controllers, which causes the robot to alter its course and walk toward the light-based goal.
Note: An autonomous hexapod with a neurobiologically-based control system for adaptive walking
2. Robot design BILL-Ant-a is an autonomous hexapod constructed from aluminum and carbon fiber. Its six legs each have three active degrees-of-freedom (DoF) and two passive DoF, and a oneDoF articulated neck carries two optical sensors. The central chassis, legs, and batteries are the same as BILL-Ant-p, a prototype walking hexapod with tethered, off-board control employing inverse kinematics for joint movements, and a subset of Cruse’s rules for leg coordination for gait generation (Lewinger et al., 2005). Active DoF in the legs and neck are actuated by MPIMX450HP hobby servo motors (Maxx Products, Inc., Lake Zurich, IL, USA). The motors are capable of delivering up to 116oz-in (0.82 Nm) of torque and directly drive the joints of the legs and the neck with a ^ 458 range of motion. This torque is sufficient to allow the 5.1 lbs (2.32 kg) robot to walk with a payload of up to 7.0 lbs (3.18 kg). The motors power three joints in each leg in a configuration based on a subset of insect leg joints. The most proximal joint is the thorax-coxa (ThC) joint responsible for protraction and retraction of the leg. The next is the coxa-trochanter (CTr) joint that levates and depresses the leg. Finally, the femur-tibia (FTi) joint allows the tibia to extend and flex relative to the femur (Figure 2). Within each leg are two passive DoF. The first is a forcesensitive resistor mounted in the foot that is used to identify ground contact. A switch mounted on the front of the lower tibia indicates when contact is made with obstacles in front of the leg. Each leg is controlled by a low-end PIC-based microcontroller (BrainStem GP 1.0 by Acroname, Inc. Boulder, CO, USA). These microcontrollers implement the role of the thoracic ganglia and are responsible for: motor control of the three joints in the leg, reading sensory information from the two passive DoF, and sharing leg influence data during its stepping cycle with its adjacent neighboring legs, in order to create coordinated walking gaits. These microcontrollers were chosen specifically for their limited computing ability in order to illustrate the mathematical simplicity of the neurobiologically-based control method, and for
3. Control of walking 3.1 Intra-leg joint coordination As has been observed in the neurobiology of stick insects, each joint of a multi-jointed leg is controlled by its own pattern generator. These pattern generators cyclically oscillate individual joints at a natural frequency. By coupling sensory information associated with a joint, and/or sensory information associated with the other joints in the leg, the pattern generators can be influenced in order to create smooth and synchronized stepping movements. These neurobiological studies have been described in Hess and Bu¨schges (1997), modeled in Ekeberg et al. (2004), and have been condensed into a walking control method for three-DOF legs (Lewinger and Quinn, 2008) (Figure 3). Sensory feedback from leg load and joint angles coordinate the joints and generate stepping motions. Environmental cues, such as sensing ground contact on irregular terrain, cause the stepping pattern to dynamically alter itself, without the need for either complex calculations or high-level micromanagement of joint actions, thus producing a robustly adaptive robot. 3.2 Inter-leg gait generation Another consideration for insects and walking robots is the coordination of stepping actions to produce stable, yet adaptive gaits. Work done throughout the years by Holk Cruse (Cruse et al., 1991, 1998) has identified mechanisms that may be responsible for coordinating the switching between swing and stance phases such that smooth and speed-dependent transitions between 259
Control system for an adaptively walking hexapod
Industrial Robot: An International Journal
William A. Lewinger and Roger D. Quinn
Volume 38 · Number 3 · 2011 · 258 –263
Figure 3 Sensory-coupled joint controllers for a three-DoF of a stick insect middle leg Thorax FTi ≤ FTi_FLX and Ground contact
No ground contact
FLX
RET
PRO
FTi ≤ FTi_EXT or No ground contact
FTi > FTi_LEV or ThC < ThC_LEV
Ground contact
LEV
ThC angle
EXT
FTi angle
CTr angle
DEP
FTi < FTi_DEP
Joint load
ThC FTi CTr
Notes: Arrows indicate sensory influences; arrows from a joint to itself are intra-joint influences and arrows from one joint to another are inter-joint influences
information with orthogonal neighbors. These data are then used to influence the stepping cycles of neighboring legs in order to create stable gaits. Gait coordination relies on two simple rules (Figure 4). The first rule uses the stance/swing state to prevent legs from entering swing phase while at least one neighbor is already in swing. By delaying the stance/swing transition, neighboring legs are prevented from being in swing simultaneously, which maintains static stability.
different gaits can emerge (such as migrating from a wave gait to a tetrapod gait to an alternating tripod gait). The implementation of this method, however, can be computationally intensive. A new method based on Cruse’s work has been developed (Lewinger et al., 2006) that simplifies gait generation, while maintaining the key factor of speed-dependent transitions between gait patterns. Although this new method is not as extensive in its capabilities as Cruse’s method, it is nonetheless sufficient for generating dependable, stable gaits for a legged hexapod.
Figure 4 Leg influences that generate coordinated and speeddependent gaits
3.3 Implementation of stepping and gait generation Both intra-leg joint coordination and inter-leg gait generation are implemented on BILL-Ant-a through a network of distributed and low-computation-capable microcontrollers; each microcontroller is responsible for a single leg, akin to the function of thoracic ganglion segments in insects. While there are many microcontrollers currently available that provide impressive computing abilities, the low-end microcontrollers used by this robot were specifically chosen to demonstrate the computational simplicity of this leg control method. Each leg controller cyclically performs three sets of actions: gathering leg data (joint load and joint angle information); deciding in what direction each joint will be moving based on the coupled sensory information from self-, inter-joint, and environmental-influencing signals; and then actuating each joint according to base-level muscle activation values (responsible for determining the maximum speed at which a joint can move) and a simple, piece-wise linear muscle model (used to create smoother walking movements). During the data gathering phase, legs share stance/swing state and ThC joint (used to protract and retract the leg) angle
Front 1, 2 lf 2 1, 2 1, 2
1. Leg in swing prevents neighbor legs from entering swing phase by rf extending neighbor PEP in direction of foot travel (i.e., rearward for forward 2 walking) 1, 2 2. Distance along foot path from AEP to PEP promotes legs to enter swing phase sooner
rm
lm 2
2 1, 2
2
Leg influences
1
2
Nominal step length
1, 2 1, 2
lr
2
–3 ThC RoM
rr 2
Stance
Swing
Stance
Notes: The two mechanisms involved represent the angle of the ThC joint, which is used to determine the amount of a single step that has been completed (mechanism 2); and a large value for when the leg is in swing phase, which is used to inhibit the initiation of the swing phase in neighboring legs (mechanism 1)
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Control system for an adaptively walking hexapod
Industrial Robot: An International Journal
William A. Lewinger and Roger D. Quinn
Volume 38 · Number 3 · 2011 · 258 –263
The second rule employs a ramp function generated by the ThC joint angle. This is used to determine how far along a nominal step length a leg has moved. If both the current and neighbor legs are approaching their respective stance/swing transition points and the neighboring leg slightly lags in the completion of its stance phase, the current leg will step early. This action minimizes, or eliminates, the need for the neighboring leg to remain in stance longer while the current leg completes its swing phase (an enforcement dictated by the first rule).
Owing to limited capabilities of the leg microcontrollers, BILL-Ant-a uses a contact switch located on the front of each foot to detect collisions with obstacles. When a foot impacts an obstruction during its swing phase, joint directions are briefly altered to become retraction, levation, and extension actions. These actions retract the foot slightly from the face of the obstacle, in order to remove contact with the obstacle face, and employ a levating action with a greatly increased muscle activation level. The higher levation activation propels the leg upward in an effort to clear the obstacle. After this brief alteration to the leg behavior, the nominal and sensory-coupled joint actions resume. If the obstacle has been cleared, nominal joint actions continue; if not, the process repeats.
4. Reflexes for adaptive stepping Walking robots need the ability to perform extreme actions to adapt to severely irregular terrain and navigate obstacles. By initiating these actions via sensory cues and a cascade of reflexes (Mu and Ritzmann, 2008), new, emergent, behaviors can be implemented through the addition of biologically plausible hypothesized sensory pathways to the same neurobiologicallybased walking control method, without the necessity of creating bolt-on, independent reflex actions (Figure 5).
4.2 Searching reflex When a leg steps into a hole or gap, the foot moves lower than the expected level of the ground (Arena et al., 2008; Beer et al., 1997); this triggers the searching reflex (Figure 6, bottom) where the leg alters its movement pattern in order to locate a suitable purchase beyond the opening. As with the elevator reflex, this behavior is initiated by sensory cues (the lack of ground contact at the expected height), which causes a reflex cascade that elicits a searching rather than stepping behavior, that modifies the stepping actions within the existing framework of the walking controller. The searching reflex is initiated when the foot drops below a level where the ground is expected. As with the elevator reflex, the searching reflex results in the joint directions becoming retraction, levation, and extension. The slight retraction action, as before, clears the leg for executing a levation action with increased muscle activation. Since the foot is at a lower level than is normally seen during stepping, the increased levation
4.1 Elevator reflex The elevator reflex allows the robot to step over obstacles that are surmountable, but higher than the normal step height while walking (Arena et al., 2008; Beer et al., 1997) (Figure 6, top). Sensory information from increased loading of ThC (responsible for protracting and retracting the leg) and CTr (responsible for levating and depressing the leg) joints can indicate the presence of such obstacles. Additional, hypothesized neural pathways in the model of the stick insect thoracic ganglia trigger a cascade of reflexes that raise the stepping height in order to bypass the hurdle.
Figure 5 The addition of two biologically plausible hypothesized sensory pathways representing the load of the ThC joint (joint load (obstacle)) and the joint angle of the CTr (joint angle (searching reflex)) can be used to initiate reflex behaviors necessary to navigate past obstructions Thorax Joint load (obstacle)
FTi ≤ FTi_FLX and Ground contact
No ground contact
PRO
FLX
RET
Ground contact hole found or obstacle
FTi > FTi_LEV or ThC < ThC_LEV or Hole found or obstacle
LEV ThC angle
DEP
FTi < FTi_EXT or No ground contact
FTi angle CTr angle
FTi > FTi_DEP
Joint load (ground contact)
ThC FTi CTr
261
EXT
Joint angle (searching reflex)
Control system for an adaptively walking hexapod
Industrial Robot: An International Journal
William A. Lewinger and Roger D. Quinn
Volume 38 · Number 3 · 2011 · 258 –263
Figure 6 BILL-Ant-a performing an elevator reflex (top) and a searching reflex (bottom)
severely than do the hind legs. Also, legs on the inside of the turn diverge more from the sagittal plane than do outside legs. This alteration in stepping actions is accomplished by modulation of the joint direction transition values (leg load and joint angles responsible for influencing in which direction each leg joint should move) and the muscle activation values that determine joint speed. For example, bringing the anterior extreme position and posterior extreme position closer together shortens the sweep of the ThC joint. Increasing the FTi joint angle setpoint that causes the FTi joint to change from flexion to extension, and similarly reducing the FTi joint angle responsible for causing a transition from extension to flexion promotes larger excursions of the FTi joint. Combining these new setpoints with decreased protraction and retraction muscle activations and increased extension and flexion muscle activations causes the leg to change from nominal stepping in the sagittal plane to angled stepping, which then induces turning in the robot.
6. Conclusion This paper demonstrates the successful implementation of a neurobiological-based control system in a physical hexapod robot. A distributed collection of leg control modules is responsible for the manipulation of individual leg joints, much like the function of thoracic ganglia. Leg load and joint angle data are used to coordinate joint movements such that stepping patterns are produced. And, interaction with the environment causes dynamic alterations to those patterns, which allows the robot to navigate past obstacles through a pair of reflexes. Sharing a limited amount of information between these modules provides the ability for stable and speed-dependent gait generation to occur. A head module that processes data from a pair of neck-mounted light sensors provides the robot with the ability to perform phototaxis. This module uses visual information to both actuate the neck in order to locate a goal and then uniquely adjusts parameters on each of the six-leg control modules, in a manner similar to the descending modulation function of the insect brain and subesophageal ganglion, which enable the robot to perform turning maneuvers. This collection of features and abilities represents the first implementation of such a neurobiological-based control system on a physical hexapod.
Notes: For the elevator reflex (top) the front-right leg alters its nominal 0.5" (1.72 cm) step height to clear the 0.79" (2.0 cm) obstacle. The bottom image shows the middle-right leg in the middle of performing a searching reflex to clear the gap, while the middle-left leg is about to initiate the same reflex. The increased levation muscle activation is responsible for high stepping seen in the elevator and searching reflexes
muscle activation promotes the returning of the foot to its nominal height during a step.
5. Modulation of walking control and goal seeking Straight walking is sufficient to test the implementation of stepping, gait generation, and reflex behaviors, but does not produce an interesting mobile robot. In order to make a walking robot useful, it needs the ability to turn. Also, a level of autonomy that allows the robot to internally decide its actions greatly improves upon its usefulness. A high-level head module is able to detect visual sensory information and, based on this information, actuates a neck motor holding the visual sensors and uniquely modulates the parameters of the leg controllers. This module does not micromanage or dictate the generation of stepping actions, but alters stepping parameters such that turning occurs, reminiscent of the way that the central body complex is thought to modulate the local controller in the thoracic ganglia in insects (Wessnitzer and Webb, 2006). Unique modulations are used since each of the six legs requires different changes in stepping motion to perform a turning behavior. For example, front legs alter their path from the sagittal plane much more
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Control system for an adaptively walking hexapod
Industrial Robot: An International Journal
William A. Lewinger and Roger D. Quinn
Volume 38 · Number 3 · 2011 · 258 –263
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Corresponding author William A. Lewinger can be contacted at: william.lewinger@ case.edu
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