Abstract
Safe, yet efficient, Human-robot interaction requires real-time-capable and flexible algorithms for robot control including the human as a dynamic obstacle. Even today, methods for collision-free motion planning are often computationally expensive, preventing real-time control. This leads to unnecessary standstills due to safety requirements. As nature solves navigation and motion control sophisticatedly, biologically motivated techniques based on the Wavefront algorithm have been previously applied successfully to path planning problems in 2D. In this work, we present an extension thereof using Spiking Neural Networks. The proposed network equals a topologically organized map of the work space, allowing an execution in 3D space. We tested our work on simulated environments with increasing complexity in 2D with different connection types. Subsequently, the application is extended to 3D spaces and the effectiveness and efficiency of the used approach are attested by simulations and comparison studies. Thereby, a foundation is set to control a robot arm flexibly in a workspace with a human co-worker. In combination with neuromorphic hardware this method will likely achieve real-time capability.
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References
Bing, Z., Meschede, C., Röhrbein, F., Huang, K., Knoll, A.: A survey of robotics control based on learning-inspired spiking neural networks. Front. Neurorobot. 12, 35 (2018)
Davies, M., et al.: Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1), 82–99 (2018)
De Momi, E., Kranendonk, L., Valenti, M., Enayati, N., Ferrigno, G.: A neural network-based approach for trajectory planning in robot-human handover tasks. Front. Robot. AI 3(JUN), 34 (2016)
Ewerton, M., et al.: Learning trajectory distributions for assisted teleoperation and path planning. Front. Robot. AI 6, 89 (2019)
Feldman, D.: The spike-timing dependence of plasticity. Neuron 75(4), 556–571 (2012)
Furber, S., Galluppi, F., Temple, S., Plana, L.: The SpiNNaker project. Proc. IEEE 102(5), 652–665 (2014)
Glasius, R., Komoda, A., Gielen, S.C.: Neural network dynamics for path planning and obstacle avoidance. Neural Netw. 8(1), 125–133 (1995)
Qu, H., Yang, S., Willms, A., Yi, Z.: Real-time robot path planning based on a modified pulse-coupled neural network model. Trans. NN 20(11), 1724–1739 (2009)
Janglová, D.: Neural networks in mobile robot motion. Int. J. Adv. Robot. Syst. 1(1), 15–22 (2004)
Jost, J.: Temporal correlation based learning in neuron models. Theory Biosci. 125(1), 37–53 (2006)
Khajeh-Alijani, A., Urbanczik, R., Senn, W.: Scale-free navigational planning by neuronal traveling waves. PLoS ONE 10(7), e0127269 (2015)
Koenig, S., Likhachev, M.: Incremental A*. NIPS2001, pp. 1539–1546 (2002)
Merolla, P.A., et al.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668–673 (2014)
O’Keefe, J., Dostrovsky, J.: The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34(1), 171–175 (1971)
Pal, A., Tiwari, R., Shukla, A.: A focused wave front algorithm for mobile robot path planning. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011. LNCS (LNAI), vol. 6678, pp. 190–197. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21219-2_25
Polyakova, M., Rubin, G., Danilova, Y.: Method for matching customer and manufacturer positions for metal product parameters standardization. In: AIP, vol. 1946 (2018)
Ponulak, F., Hopfield, J.: Rapid, parallel path planning by propagating wavefronts of spiking neural activity. Front. Comp. Neurosci. 7, 98 (2013)
Qureshi, A., Simeonov, A., Bency, M., Yip, M.: Motion planning networks (2019)
Raković, M., Savić, S., Santos-Victor, J., Nikolić, M., Borovac, B.: Human-inspired online path planning and biped walking realization in unknown environment. Front. Neurorobot. 13, 36 (2019)
Subashini, M., KumarSahoo, S.: Pulse coupled neural networks and its applications. Expert Syst. Appl. 41(8), 3965–3974 (2014)
Tolman, E.C.: Cognitive maps in rats and men. Psychol. Rev. 55(4), 189 (1948)
Weber, C., Triesch, J.: From exploration to planning. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008. LNCS, vol. 5163, pp. 740–749. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87536-9_76
Zeller, M., Sharma, R., Schulten, K.: Motion planning of a pneumatic robot using a neural network. IEEE Control Syst. 17(3), 89–98 (1997)
Zennir, M., Benmohammed, M., Boudjadja, R.: Spike-time dependant plasticity in a spiking neural network for robot path planning. In: AIAI, vol. 1539 (2015)
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The research leading to this paper received funding as the project NeuroReact from the Baden-Württemberg Stiftung under the research program Neurorobotik.
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Steffen, L., Liebert, A., Ulbrich, S., Roennau, A., Dillmannn, R. (2020). Adaptive, Neural Robot Control – Path Planning on 3D Spiking Neural Networks. In: Farkaš, I., Masulli, P., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2020. ICANN 2020. Lecture Notes in Computer Science(), vol 12397. Springer, Cham. https://doi.org/10.1007/978-3-030-61616-8_41
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