The Control of Turning in Real and Simulated Stick Insects

Rosano, Hugo; Webb, Barbara

doi:10.1007/11840541_13

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…This means that the body is moving slightly sideways at the beginning of the turn. Results reported previously in (Rosano and Webb 2006) are slightly different because only the body orientation was controlled. Introducing the target meant that the simulation had to correct more for deviations and had less time to do so.…”

Section: Results With Passive Rear Leg Controlmentioning

confidence: 76%

“…This lag removed noise and 2 DCR-TRVHE with a resolution of 720 ×576 at 25 frames per second. 3 This approach was not used in (Rosano and Webb 2006); this accounts for small differences in figures reported there. At least two different velocity vectors v = (direction θ, speed r ) are needed to describe the entire body motion, e.g., that of the prothorax v P and metathorax v M , defined as the point on the body between coxae of the legs on that segment.…”

Section: Methodsmentioning

confidence: 90%

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A dynamic model of thoracic differentiation for the control of turning in the stick insect

Rosano

Webb

2007

Biol Cybern

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Leg movements of stick insects (Carausius morosus) making turns towards visual targets are examined in detail, and a dynamic model of this behaviour is proposed. Initial results suggest that front legs shape most of the body trajectory, while the middle and hind legs just follow external forces (Rosano H, Webb B, in The control of turning in real and simulated stick insects, vol. 4095, pp 145-156, 2006). However, some limitations of this explanation and dissimilarities in the turning behaviour of the insect and the model were found. A second set of behavioural experiments was made by blocking front tarsi to further investigate the active role of the other legs for the control of turning. The results indicate that it is necessary to have different roles for each pair of legs to replicate insect behaviour. We demonstrate that the rear legs actively rotate the body while the middle legs move sideways tangentially to the hind inner leg. Furthermore, we show that on average the middle inner and hind outer leg contribute to turning while the middle outer leg and hind inner leg oppose body rotation. These behavioural results are incorporated into a 3D dynamic robot simulation. We show that the simulation can now replicate more precisely the turns made by the stick insect.

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Section: Results With Passive Rear Leg Controlmentioning

confidence: 76%

Section: Methodsmentioning

confidence: 90%

A dynamic model of thoracic differentiation for the control of turning in the stick insect

Rosano

Webb

2007

Biol Cybern

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“…However, in the other two strategies (SAR, GPR), all legs inside the turning curve change simultaneously in response to changes in the sensory inputs from the environment during turning. The SAR strategy is based on insect turning (Rosano and Webb, 2006), while the GPR strategy is a typical engineering approach (Manoonpong et al, 2007). To investigate their behaviors, we first investigated the turning behavior using each leg-body coordination strategy against a solid wall.…”

mentioning

confidence: 99%

Leg-body coordination strategies for obstacle avoidance and narrow space navigation of multi-segmented, legged robots

Mingchinda,

Jaiton,

Leung

et al. 2023

Front. Neurorobot.

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IntroductionMillipedes can avoid obstacle while navigating complex environments with their multi-segmented body. Biological evidence indicates that when the millipede navigates around an obstacle, it first bends the anterior segments of its corresponding anterior segment of its body, and then gradually propagates this body bending mechanism from anterior to posterior segments. Simultaneously, the stride length between pairs of legs inside the bending curve decreases to coordinate the leg motions with the bending mechanism of the body segments. In robotics, coordination between multiple legs and body segments during turning for navigating in complex environments, e.g., narrow spaces, has not been fully realized in multi-segmented, multi-legged robots with more than six legs.MethodTo generate the efficient obstacle avoidance turning behavior in a multi-segmented, multi-legged (millipede-like) robot, this study explored three possible strategies of leg and body coordination during turning: including the local leg and body coordination at the segment level in a manner similar to millipedes, global leg amplitude change in response to different turning directions (like insects), and the phase reversal of legs inside of turning curve during obstacle avoidance (typical engineering approach).ResultsUsing sensory inputs obtained from the antennae located at the robot head and recurrent neural control, different turning strategies were generated, with gradual body bending propagation from the anterior to posterior body segments.DiscussionWe discovered differences in the performance of each turning strategy, which could guide the future control development of multi-segmented, legged robots.

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“…Beside the self-organising walking pattern and the robot's capability to handle leg damages during straight forward walking with an emergent gait [6], the walking machine's curve walking was tested in [7].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Further biological experiments and computer simulations showed that decentralized control architecture combined with local rules lead to curve walking based on individual stance trajectories for each leg without explicit calculations [6]. In the ORCA project a mixture of both approaches is used.…”

Section: Turning and Curve Walkingmentioning

confidence: 99%

Distributed Fault-Tolerant Robot Control Architecture Based on Organic Computing Principles

Auf

Litza

Maehle

Biologically-Inspired Collaborative Computing

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Walking animals like insects show a great repertoire of reactions and behaviours in interaction with their environment. Moreover, they are very adaptive to changes in their environment and to changes of their own body like injuries. Even after the loss of sensors like antennas or actuators like legs, insects show an amazing fault tolerance without any hint of great computational power or complex internal fault models. Our most complex robots in contrast lack the insect abilities although computational power is getting better and better. Understanding biological concepts and learning from nature could improve our approaches and help us introduces a control architectural approach based on organic computing principles using concepts of decentralization and self-organization, which is demonstrated and tested on a six-legged robotic platform. Beside explaining the organic robot control architecture, this study presents a leg coordination architecture extension to improve the robustness and dependability towards structural body modifications like leg amputations and compares experimental results with previous studies.

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The Control of Turning in Real and Simulated Stick Insects

Cited by 5 publications

References 11 publications

A dynamic model of thoracic differentiation for the control of turning in the stick insect

A dynamic model of thoracic differentiation for the control of turning in the stick insect

Leg-body coordination strategies for obstacle avoidance and narrow space navigation of multi-segmented, legged robots

Distributed Fault-Tolerant Robot Control Architecture Based on Organic Computing Principles

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