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de Silva. Intell Robot 2021;1(1):3-17 https://dx.doi.org/10.20517/ir.2021.01 Page 12

1. Some networked agents (e.g., robots, unmanned aerial vehicles or UAVs, and sensor nodes containing
sensors, actuators, effectors, controllers, etc.) may be dynamic or mobile.
2. The operating system environment may be dynamic, unstructured, and unknown.
3. The system should be self-adaptive to optimize its performance, particularly by utilizing the dynamic
components in addition to parameter adjustment or tuning and structural reorganization.
4. The system may be further optimized by sharing resources among the applications.
5. Dynamic or mobile sensors may receive “feedback” from themselves to improve their sensing
effectiveness (e.g., data/information quality, the relevance of their data, speed, and confidence).
6. The networked agents should possess “intelligence” to facilitate autonomous and desired performance.
7. The system should be able to predict, detect, and diagnose malfunctions and faults in it and accommodate
or self-repair.

The underlying activities of system development and implementation will pertain to sensor/data fusion and
adaptive sensing; multi-agent cooperation; multi-objective and parameter/structure optimization; fault
prediction, detection, diagnosis, and resolution; self-organization/adaptation; and distributed/networked
intelligent control. Suitable system architecture and an application platform of this type are schematically
shown in Figure 6. In this system development process, one may have to determine and quantify the design
constraints, performance limits, trade-offs, and development/operation guidelines and benchmarks for the
pertinent applications. That will lead to significant improvements in performance, developmental and
operational costs, productivity, resource requirements, energy efficiency, safety, fault tolerance, reliability,
autonomy, and sustainability of the robotic system.

It is clear that both individual and network control are relevant in the present context, and furthermore,
both conventional control and “intelligent” control are also relevant. The present paper has devoted much
focus to the aspect of robotic intelligence. Hence, in the present section, particular attention is given to the
conventional control of robots.

5.1. Conventional control
The development and application of conventional control have been extended wide effort by many. The
relevant techniques include the following.

Feedback control and particularly servo-control of robotic joints had been the main focus in the early
developments of robotic control. Here, the robot motions are measured (sensed) and used by the controller
in feedback to move the robot in the desired manner. Thus, a robot is “servoed” along a specified motion
trajectory through feedback control of the motion error using servo control. Notably, the subject of design
and compensation or tuning of proportional-integral-derivative control has received adequate attention.

An image of an object is indeed a valuable source of information about that object. In this context, the
imaging device is the sensor, and the image is the sensed data. Depending on the imaging device, an image
can be many varieties such as optical, thermal or infrared, X-ray, ultraviolet, acoustic, ultrasound, etc. The
image processing methods are rather similar among these imaging devices. For example, the digital camera
is a very popular optical imaging device used in various engineering applications such as vision-guided
robotics. Such operations as object recognition, pattern recognition and classification, abstraction, and
knowledge-based decision making can be carried out using the information extracted through image
processing. Visual servoing [12,13] , in particular, has received much attention and is commonly implemented
in robotics. Here, the robot motion, including the actual position of the end effector (gripper, hand, tool,
etc.) and the relative position of the targeted object, is measured using camera images and compared with