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Both
human and machine bring to the partnership varying strengths and opportunities
for action, and during collaboration, each must be able to perceive and access
these opportunities in order for them to be effectively leveraged. These
affordances define the interaction possibilities of the team, and determine the
degree to which each party’s skills can be utilized during collaborative
problem solving. The set of problems warranting a collaborative technique is
equivalent to the set problems where there is an opportunity to effectively
leverage affordances on both sides of the partnership in pursuit of the
solution. Instead of deciding who gets which task, we can begin to reason about
which party can best contribute to the collective goal at each stage. The answer
may not be only the human, or only the machine, but could in fact be both. By
designing such that both human and machine are aware of the affordances made
available to them by their collaborators, we encourage the development of more
flexible procedures for collective problem solving.

 

The success of human-computer collaborative systems hinges on
leveraging the skills of both the human and the computer. That said, in order
to address the problem of balancing and allocating workload in a human-computer
collaborative system, it is first necessary to explore the space of problem
difficulty relative to human and machine. While problem difficulty for a
machine can be defined as space and time complexity, for the human we propose
that problem difficulty is attributed to two main sources: knowledge necessary
to solve the problem and time investment required to solve a problem. The level
of difficulty for one party may not necessarily transfer to the other. For
instance, some problems such as character recognition are inherently easy for a
human and can sometimes be performed in constant time but can be
computationally expensive or unsolvable for the machine. The inverse can also
be true. We can think about the problem space as having two orthogonal
dimensions: human difficulty and machine difficulty.

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 Benefits of Collaborative Robots in
Assembly

 

 Robots also still have problems handling
complex and soft components. These problems hinder robot implementation for
many applications. However, advancements in several fields such as programming,
robot sensor and control technology, force sensing, environment recognition,
human–machine-interfaces and safety system technology are about to change this.
These advancements could make it possible for operators to guide or collaborate
with robots that assist operators at close range, without compromising safety,
often referred to as cobot installations. The author has shown that this mode
of robot operation promises several potential benefits as it takes full
advantage of robot as well as human strengths, while at the same time avoiding
automation drawbacks. The technological developments and this mode of operation
could make robot installations competitive for many more applications than today.
This refers to a study by Stanley Automation where they estimate that the use
of intelligent automation devices in assembly tasks can reduce injury costs,
management costs and labour costs with 58%, leading to a payback time of less
than 1 month. If cobot installations turn out to be as cost efficient as the
Stanley Automation study indicates, this will make this type of robot
installations very competitive and the range of operations that can be
automated will increase significantly. Implementing this type of robot could
hence add substantial value for producing companies and more detailed
information on possible economic benefit could be useful.

 

Human-Robot Interaction
in Collaborative Task

 

According
to this Review, robots are placed not only in production line but also as
service providers. These robots are called as professional service robots. This
newer class of robots, are specifically designed to assist workers in
accomplishing their goals. These robots differ from industrial robots and many
other technologies found in the work environment because they are mobile, they
do things without being commanded, and they are interactive. These differences
suggest that professional service robots may affect the work environment in
socially important ways. Because of their ability to move with apparent
intentionality in physical space, they are likely to be perceived as animate,
triggering social responses. Their ability to travel between different
departments also may allow the unplanned movement of information between
distant co-workers. If professional service robots are to share the workplace
with people, we need to understand what the interaction between them is likely
to be like. people trust robots to perform operations that the robots are capable
of, without oversight. If things go wrong, will people take appropriate
responsibility to correct the problem, or will they abdicate responsibility to
the robot? In the face of uncertainty, will people ask for and accept the
guidance of expert robots. The aspects of the design of the robot will affect
the way people and robots work together. The better we understand these
questions, the better we can design robots to be effective work partners.

The
findings in this review shows that people’s responses to human coworkers versus
robot coworkers; and to more humanoid versus less humanoid robots. It is clear
that there are significant differences in the extent to which people will rely
on robots as compared with human work partners. When working with a person
instead of a robot, participants relied more on the partner’s advice and were
less likely to ignore their counsel. However, only marginal support for the
idea that people would feel less burden of responsibility when interacting with
another person as compared with a robot. It appears from these data that
participants collaborated more with the human partners than with the robot
partners but still did not necessarily surrender responsibility to them. Robots
can work with human but it cannot alternate all the things which human work on.

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