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Page 59                             Li et al. Intell Robot 2021;1(1):58-83  I http://dx.doi.org/10.20517/ir.2021.08



               1. INTRODUCTION
               From the first stirrings of life, nature has been providing a suitable breeding ground for the intelligence of
               organisms. Biological intelligence enables organisms to adapt the extreme or changing environments. For
               instance, a group of birds and fishes can efficiently sense the surrounding dynamic environments and take
               effective actions based on those inputs often with very simple mechanisms and with limited availability of
               information. Some species exhibit collective behaviors and can cooperatively accomplish tasks that are beyond
               the capabilities of a single individual under limited implicit communication. Organisms with such beneficial
               traits can pass on these traits to offspring, exhibiting high adaptability to environments. The nervous system
               in the brain gives human abilities of feeling, thinking, and learning abilities.


               Recently, therehasbeenageneralmovementtowardsservice-orientedrobotsthatrequiretheabilitytoadaptto
               complex dynamic situations and to handle various uncertainties. Due to the desirable properties of biological
               organisms, such as adaptability, robustness, versatility, and agility, the researchers have been trying to infuse
               robots with biological intelligence that will enable safe navigation and efficient cooperation among the au-
                                                    [1]
               tonomous robots in changing environments . The approaches inspired by biological intelligence are known
                                                                                                        [2]
               asbiologicallyinspiredintelligence,whichhasbeenexploredandstudiedformanyyearsinroboticsresearch .
               The fundamental idea of biologically inspired intelligence is to incorporate useful biological strategies, mecha-
               nisms, and structures into the development of new methodologies and technologies to solve existing problems
               in a more efficient way than existing methodologies and technologies. For instance, swarm intelligence and
               collective behaviors of living organisms have inspired the design of many robotics algorithms based on their
               biological strategies [3,4] . The process of natural selection has inspired many computational models to opti-
                                                                                     [7]
               mize robot performances, such as genetic algorithm [5,6]  and differential evolution . The neural network
               algorithm, derived from neural science, has gained rising popularity among researchers around the world [8,9] .
               Biologically inspired intelligence algorithms were also integrated with various conventional algorithms to de-
               velop more efficient algorithms. For example, a knowledge based genetic algorithm, which incorporated the
               domain knowledge into its specialized operators, was proposed to efficiently generate collision-free path of
               robots [10] . A neural network was used to convert the improved central pattern generator output to the foot
               trajectories of quadruped robots [11] . However, most bio-inspired studies are limited to conceptual or labo-
               ratory investigations or do not have much biological inspiration. Thus, the development of new intelligent
               strategies, algorithms and technologies are still highly needed, such as real-time collision-free navigation algo-
               rithms of individual robots or communication, coordination, and cooperation algorithms for multiple robotic
               systems, to accomplish multi-objective tasks in dynamic environments.


               Bio-inspired neurodynamics models have been studied for real-time path planning and control of various
                                                 [2]
               robotic systems during the past decades . The shunting neurodynamics model was derived from Hodgkin
               and Huxley’s membrane models for dynamic ion exchanges [12] . Based on the shunting neurodynamics model
               and its model variants, several new algorithms have been successfully developed for real-time path planning
               and control of various autonomous robots [13,14] . The definition of real-time is in the sense that the robot path
               planner and controller respond immediately to the dynamic environment, including the robots, targets, ob-
               stacles, sensor noise and disturbances. Many other model variants have been also developed for robot path
               planning and control. The additive model is computationally simpler and can generate real-time collision-
               free paths under most conditions [13,15] . The gated dipole model shows excellent performance in multi-robotic
               path planning and tracking control [16] . Beyond the application of autonomous robots, bio-inspired neuro-
               dynamics models have been also widely applied to many other research fields, such as odor dispersion with
               electronic nose [17]  and dynamic ginseng drying [18] . These researches on agriculture have also been extended
               to biomedical and other industrial applications.

               This paper focuses a comprehensive survey of the state-of-the-art research on bio-inspired neurodynamics
               models with their applications to path planning and control of autonomous robots. A detailed introduction
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