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"Optimal Robot Path Planning with Cellular Neural Network" featured for open access

Reading Your Mind: IBM's New Computer Chip Copies Brain Function

By IGI Global on Sep 2, 2014

Last month, The New York Times reported that IBM recently developed a computer chip that replicates the incredible functions of the brain. IBM's New Computer Chip Copies Brain Function

IBM's New Computer Chip Copies Brain Function

Relying on densely connected webs of transistors similar to the brain’s neural networks, the chip, called TrueNorth, uses the power of an average hearing aid. According to the article, the invention is on a path to excel at calculations that stump today’s supercomputers. “It is a remarkable achievement in terms of scalability and low power consumption,” said Horst Simon, deputy director of the Lawrence Berkeley National Laboratory. He compared the new processor to the introduction of the supercomputer in the 80’s, which he recalled was like moving from a two-lane road to a superhighway.

TrueNorth is not yet commercially available, but recently researchers have been uncovering incredible new methodologies based on this replication of neural dynamics in computers and robotics. Referred to as neuromorphic or cognitive computing, this drawing of analogies between the functions of neural networks and computers and robotics is quickly becoming the way of the future.

The IGI Global article, “Optimal Robot Path Planning with Cellular Neural Network,” explores this same methodology, presenting a new cellular neural network-based tactic for optimal robot path generation with obstacle avoidance in an arbitrarily dynamic environment. Written by Yongmin Zhong (Curtin University of Technology, Australia), Bijan Shirinzadeh (Monash University, Australia) and Xiaobu Yuan (University of Windsor, Canada), this article is featured in the International Journal of Intelligent Mechatronics and Robotics (IJIMR), and presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. This article has been chosen for feature as IGI Global’s monthly complimentary journal article for September. Click here to view the full article “Optimal Robot Path Planning with Cellular Neural Network.”

The International Journal of Intelligent Mechatronics and Robotics (IJIMR) publishes original and innovative research and practical developments in the field of mechatronics and robotics. The journal covers a wide range of application areas, such as robotic-assisted manufacturing; advanced mechanisms and robotics; systems modelling and analysis; instrumentation and device control; automation systems; intelligent sensing and control; medical robotics; and autonomous and complex systems. IJIMR publishes original research papers, review papers, case studies, and patent alerts on the latest innovations in methodologies, technologies, and products within the fields of mechatronics and robotics.

This article is one of the thousands available on IGI Global's InfoSci®-OnDemand, which allows full-text searching through IGI Global's entire collection of research articles, book chapters, and teaching cases. Refer to the previous link for additional information, or contact cust@igi-global.com. Some of IGI Global’s other publications discussing robotics and computer technologies include the following:

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