Combining spiking motor primitives with a behaviour-based architecture to model locomotion for six-legged robots

Tieck, J. Camilo Vasquez; Rutschke, Jacqueline; Kaiser, Jacques; Schulze, Martin; Buettner, Timothee; Reichard, Daniel; Roennau, Arne; Dillmann, Rüdiger

doi:10.1109/iros40897.2019.8968128

Cited by 10 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although 25.00% of the studies did not discuss the adaptation of the parameters of the gait to execute a specific task, 35.09% of the analyzed hexapods could change their locomotion with the asperities of the ground, which enhanced the importance of these mobile robots for navigation in complex environments. Considering the information provided by Table A1, the type of soil in which the hexapods were tested ranged from flat concrete [28,29] and tiles [24] to rocks, sand [32,52,54] and grasslands [51], which scopes most of the terrains encountered in outdoor environments. Hence, these systems already provide versatile controllers to deal with the possible variation of the coefficient of friction of the ground.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, these systems already provide versatile controllers to deal with the possible variation of the coefficient of friction of the ground. Despite that, the study of the behavior of hexapods in soft terrain is less frequent [28,29,32,53,54,62]. The ability to overcome different terrain topologies also considered scenarios in which the hexapods had to detect and pass over steps and ditches.…”

Section: Discussionmentioning

confidence: 99%

“…Zhong et al [53] also presented an obstacle avoidance method with computer vision, but in this case, the output of the CPG was adjusted to pass over or surmount objects. Although its objective was space exploration, in [54], an SNN to bio-mimic the actuation of the limbs of LAURON V was implemented, and the adaptation of its trajectory to overcome obstacles and pass over them was studied.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Trends in the Control of Hexapod Robots: A Survey

et al. 2021

View full text Add to dashboard Cite

The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Trends in the Control of Hexapod Robots: A Survey

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The motion of the hand is also modelled with a motor primitive, but instead of controlling joints, it controls the activation parameters of the finger primitives. The hand primitives are organized in a hierarchy coordinating the finger primitives as in [27], [29]. The hand primitives represent different grasping affordances -sphere, cylinder and pinch according to [13] and rest position.…”

Section: B Hand Primitives and Hierarchymentioning

confidence: 99%

“…Although, there are other approaches using SNN for motion control using force feedback, to the best of our knowledge, there is no implementation of an SNN for soft grasping with a 5-finger anthropomorphic hand performing compliant control without force sensors using the standart joint interface. We can model a system using SNN for soft-grasping using an anthropomorphic robotic hand taking inspiration from biology and using the principles presented in previous work for a hierarchy of motor primitives with SNN to model the hand [27], to model finger reflexes [28], to coordinate multiple primitives [29], and to combine activation modalities [30].…”

Section: Introductionmentioning

confidence: 99%

Soft-Grasping With an Anthropomorphic Robotic Hand Using Spiking Neurons

Tieck

Secker

Kaiser

et al. 2021

IEEE Robot. Autom. Lett.

Self Cite

View full text Add to dashboard Cite

Evolution gave humans advanced grasping capabilities combining an adaptive hand with efficient control. Grasping motions can quickly be adapted if the object moves or deforms. Soft-grasping with an anthropomorphic hand is a great capability for robots interacting with objects shaped for humans. Nevertheless, most robotic applications use vacuum, 2-finger or custom made grippers. We present a biologically inspired spiking neural network (SNN) for soft-grasping to control a robotic hand. Two control loops are combined, one from motor primitives and one from a compliant controller activated by a reflex. The finger primitives represent synergies between joints and hand primitives represent different affordances. Contact is detected with a mechanism based on inter-neuron circuits in the spinal cord to trigger reflexes. A Schunk SVH 5-finger hand was used to grasp objects with different shapes, stiffness and sizes. The SNN adapted the grasping motions without knowing the exact properties of the objects. The compliant controller with online learning proved to be sensitive, allowing even the grasping of balloons. In contrast to deep learning approaches, our SNN requires one example of each grasping motion to train the primitives. Computation of the inverse kinematics or complex contact point planning is not required. This approach simplifies the control and can be used on different robots providing similar adaptive features as a human hand. A physical imitation of a biological system implemented completely with SNN and a robotic hand can provide new insights into grasping mechanisms.

show abstract

Biomorphic robot controls: event driven model free deep SNNs for complex visuomotor tasks

Dillmann

Rönnau

2022

Artif Life Robotics

View full text Add to dashboard Cite

Combining spiking motor primitives with a behaviour-based architecture to model locomotion for six-legged robots

Cited by 10 publications

References 14 publications

Trends in the Control of Hexapod Robots: A Survey

Trends in the Control of Hexapod Robots: A Survey

Soft-Grasping With an Anthropomorphic Robotic Hand Using Spiking Neurons

Biomorphic robot controls: event driven model free deep SNNs for complex visuomotor tasks

Contact Info

Product

Resources

About