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was proposed by Li et al. [47] for generating the globally shortest path. A modified pulse-coupled neural net-
work was proposed by Qu et al. [48,49] for real-time collision-free path planning. The computational complexity
of the algorithm was only related to the length of the shortest path. In addition, an improved Hopfield-type
neural network model was proposed by Zhong etal. [50] for easily responding the real-time changes in dynamic
environments. A padding mean neurodynamics model was proposed by Chen et al. [51] for the reasonable path
generation in both static and dynamic varying environments.

3.1.2. Cooperation of Multi-robotic Systems
A team of robots would work together to accomplish an assigned task rapidly and efficiently. In many chal-
lenging applications such as search and rescue operations, security surveillance and safety monitoring, a multi-
roboticsystemhasobviousadvantagesthanasingleroboticsystem. Thekeychallengeofmulti-roboticsystems
in dynamic environments is to infuse these robots with biologically inspired intelligence that will enable effi-
cient cooperation among the autonomous robots, and successful completion of designated tasks in changing
environments.

For the multiple targets path planning, an online solution based on the bio-inspired neural network was pro-
posed by Bueckert et al. [52] in static and dynamic environments. However, the task assignment approach was
very simple, as the robot visited the target, this target was removed from the visit list. Thus, the robot was hard
to find the optimal visit sequence of targets. A novel hybrid agent framework was proposed by Li et al. [53]
for real-time path planning to multi-robotic systems considering many moving obstacles. In this work, an
improved shunting equation was proposed by setting safety margins for the robots and the moving obstacles.
The robots are able to predict the movement of obstacles and avoid any collision. Nanoassembly planning
creates enormous potential in a vast range of new applications. An integrated method based on the shunt-
ing model was proposed to generate collision-free paths of multi-robotic nanoassembly [54] . The tasks of the
multi-robotic nanoassembly planning were a continuous process considering the environmental uncertainty.

If the robotic systems need to track the moving targets, an important influence of the algorithms is the relative
moving speed between the target and robot [55] . If the speed of robotic systems is much lower than the target,
the robotic systems need to corporately track the target, otherwise the robot will never catch the target. A real-
timecooperativehuntingalgorithmwasproposedbyNiandYangbaseonshuntingneurodynamicsmodels [56] .
In this hunting task study, the robots had no previous knowledge about the environment and locations of
evaders. It is important to note that the difference between tracking a moving target and the hunting algorithm
is that the evader in hunting problems has some intelligence to escape from the hunt of pursuer robots. Figure
6A shows the hunting process considering many evader robots. Compared with other hunting algorithms,
the hunting algorithm based on bio-inspired neurodynamics still works efficiently when some hunting robots
are broken. Figure 6B shows the hunting process that some robots are broken.

3.2. Cleaning robots
Thecleaningtasksrequiretherobotto passthrough every area in the workenvironment. The task requirement
is the same as the complete coverage path planning (CCPP), which is a special type of path planning in 2-D
environments. The CCPP can be also applied to many other robotic applications, such as painter robots, dem-
ining robots, lawnmowers, automated harvesters, agricultural crop harvesting equipment, windows cleaners,
and autonomous underwater covering vehicles [57,58] . In the bio-inspired neural network, the unclean areas are
set as targets, which globally attract the robot. The obstacles have only local effects, which avoids robot colli-
sions [59–61] . As the cleaning robot works, the unclean areas become clean and the excitatory input of the clean
area becomes zero. Thus, the landscape of neural activity dynamically changes with the change of the unclean
areas, obstacles, and other robot position. For any current position of the robot, the next robot position is
obtained by

= max { + , = 1, 2, · · · , } (11)
⇐