Biologically inspired reactive climbing behavior of hexapod robots

Goldschmidt, Dennis; Hesse, Frank; Wörgötter, Florentin; Manoonpong, Poramate

doi:10.1109/iros.2012.6386135

Cited by 25 publications

(20 citation statements)

References 16 publications

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“…The body consists of two parts: two front legs belong to the front part and the middle and hind legs belong to the hind part.The two body parts are connected by an active backbone joint which enables the rotation around the lateral or transverse axis. This backbone joint is mainly used for climbing which is not the main focus here (but see [13]). All leg joints as well as its backbone joint are driven by digital servo motors.…”

Section: The Walking Machine Platform Amosiimentioning

confidence: 99%

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

Ren

Chen

Kolodziejski

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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In chaos control, an originally chaotic system is modified so that periodic dynamics arise. One application of this is to use the periodic dynamics of a single chaotic system as walking patterns in legged robots. In our previous work we applied such a controlled chaotic system as a central pattern generator (CPG) to generate different gait patterns of our hexapod robot AMOSII. However, if one or more legs break, its control fails. Specifically, in the scenario presented here, its movement permanently deviates from a desired trajectory. This is in contrast to the movement of real insects as they can compensate for body damages, for instance, by adjusting the remaining legs' frequency. To achieve this for our hexapod robot, we extend the system from one chaotic system serving as a single CPG to multiple chaotic systems, performing as multiple CPGs. Without damage, the chaotic systems synchronize and their dynamics is identical (similar to a single CPG). With damage, they can lose synchronization leading to independent dynamics. In both simulations and real experiments, we can tune the oscillation frequency of every CPG manually so that the controller can indeed compensate for leg damage. In comparison to the trajectory of the robot controlled by only a single CPG, the trajectory produced by multiple chaotic CPG controllers resembles the original trajectory by far better. Thus, multiple chaotic systems that synchronize for normal behavior but can stay desynchronized in other circumstances are an effective way to control complex behaviors where, for instance, different body parts have to do independent movements like after leg damage.

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Section: The Walking Machine Platform Amosiimentioning

confidence: 99%

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

Ren

Chen

Kolodziejski

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

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“…For this experiment, the CPG-based control generated a basic walking pattern [e.g., a slow wave gait ( MI = 0.02)] while the local leg control adapted the legs individually for foothold searching and elevation, thereby enabling effective locomotion and supporting the body of AMOS II during climbing. Note that the slow wave gait was used in this experiment because it is the most effective gait for climbing which allows AMOS II to negotiate the highest climbable obstacle (13 cm height which equals 75% of its leg length) [see Goldschmidt et al (2012) for details]. In addition to the locomotion control, reactive BJ control was also applied to control the BJ for climbing [see Goldschmidt et al (2012) for details].…”

Section: Resultsmentioning

confidence: 99%

“…Note that the slow wave gait was used in this experiment because it is the most effective gait for climbing which allows AMOS II to negotiate the highest climbable obstacle (13 cm height which equals 75% of its leg length) [see Goldschmidt et al (2012) for details]. In addition to the locomotion control, reactive BJ control was also applied to control the BJ for climbing [see Goldschmidt et al (2012) for details]. The controller produces an abstraction of body flexion observed in cockroach climbing.…”

Section: Resultsmentioning

confidence: 99%

“…Besides the physical components of AMOS II that follow biomechanics of walking animals, another special trait of AMOS II is that we configured the ranges of the joint movements of AMOS II such that it has a very low center of mass (i.e., low ground clearance) and its body falls to the ground before taking the next step during normal walking. When negotiating a large obstacle, AMOS II uses its BJ together with additional reactive BJ control (Goldschmidt et al, 2012) to climb over it while its leg movements automatically adapt accordingly ( Video S12 ).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the controller, using a single CPG, sensory feedback, and forward model predictions providing an internal state, can generate a multitude of walking patterns (e.g., 20 walking patterns), insect-like leg movements, energy-efficient locomotion, and adaptable locomotion (like searching and elevation actions including reactions when stepping on an obstacle during swing phase). It can also handle leg damage and even generate cockroach-like climbing behavior ( Video S12 ) when additional reactive BJ control is applied (Goldschmidt et al, 2012). The controller can also be simply transferred to another six-legged walking machine having a different morphology but leg lengths with similar proportion to AMOS II.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Manoonpong¹,

Parlitz²,

Wörgötter³

2013

Front. Neural Circuits

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100

165

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Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs) and sensory feedback (afferent-based control) but also on internal forward models (efference copies). They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.

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Enhancing Adaptability of a Legged Walking Robot with Limit-Cycle Based Local Reflex Behavior

Gao

et al. 2018

Intelligent Robotics and Applications

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Biologically inspired reactive climbing behavior of hexapod robots

Cited by 25 publications

References 16 publications

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot

Neural control and adaptive neural forward models for insect-like, energy-efficient, and adaptable locomotion of walking machines

Enhancing Adaptability of a Legged Walking Robot with Limit-Cycle Based Local Reflex Behavior

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