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studies with nine seniors living at home. The study looks at
both practical and emotional aspects of the robot’s assistance,
and it shows that seniors found value in its health-related
help and formed emotional bonds with it. This research can
help elderly people, and it introduces a new method to assess
how well robots navigate homes. However, there is still work
needed to improve and test the robot’s reliability in different
home settings.
In the context of real-world applications, M. Gillham et
al. [27] highlight the importance of human-assistive devices,
especially in situations where collision avoidance is crucial.
The approach focuses on giving users control while providing
helpful assistance. It uses a new technique to quickly recog-
nize specific situations using basic sensor data, even when the
data is uncertain. This innovative method could be a valuable
tool for pattern recognition in human-assistive devices. How-
ever, it might face challenges in complex real-world settings
with diverse obstacles. In addition, relying solely on simple
sensor data might limit its ability to make precise decisions
in uncertain situations, potentially leading to suboptimal path
planning results.
Additionally, a recent study [28] introduces an innovative
telemedicine method that utilizes robots for certain medical
procedures. This pioneering approach involves the use of
automated robotic systems, reducing operation time and the
requirement for extensive robot training. With only a few ac-
tions, this automated method improves the quality of medical
procedures, offering exciting possibilities for telemedicine in
robot-assisted healthcare. This research has potential benefits
in telemedicine and robotic-assisted healthcare, but limitations to balance collision avoidance and smoothness [36]. Many
like adapting to different patients, concerns about accuracy,
and needing more real-world testing and refinement exist.
In the context of the COVID-19 pandemic, P. Manikandan
et al. [29] highlighted the crucial role of robotics in health-
care. This research emphasizes the importance of medical
robots in various medical tasks. Medical robots help reduce
human-to-human contact, improve cleaning and sterilization,
and provide support in quarantine areas, thereby reducing
risks for healthcare workers. The proposed system aims to
aid healthcare professionals in delivering essential supplies
to those who require them. The robot offers benefits like pa-
tient monitoring and precise medication delivery, but it has
drawbacks, including limited human interaction and potential
technical problems. The cost of implementing such robots in
healthcare settings is also a concern.
C. Gap in the Current Research on Path Planning for
Robots
There is ongoing research in this area to address the challenges planning in healthcare, it also underscores the complexity
discussed earlier and develop more robust and efficient path-
planning algorithms for dynamic environments. One approach
Sabeeh & Al-Furati
uses machine learning to improve path-planning in dynamic
environments [30]. Machine learning can be used to learn the
behavior of dynamic obstacles and to predict future changes
in the environment. This can help path-planning algorithms
to adapt to changes in the environment more quickly and ef-
fectively. Another approach uses distributed path-planning
algorithms [31]. Distributed path-planning algorithms have
the potential to break down the path-planning task into smaller,
independent subtasks. This approach enhances the scalability
of path-planning algorithms, making them better suited for
navigating extensive and intricate environments. A third ap-
proach uses hybrid path-planning algorithms [32] and [33].
Hybrid path-planning algorithms merge various path-planning
techniques to enhance the efficiency of path-planning in en-
vironments that are in continuous change. For instance, a
hybrid approach could involve employing a sampling-based
algorithm for swift exploration of the search area, comple-
mented by a local planner to fine-tune the path.
However, there are a number of issues with current studies
on path-planning approaches in terms of handling dynamic
obstacles. For instance, lack of real-time adaptability and
responsiveness [34]. Many path-planning algorithms are not
able to adapt to changes in the environment in real time. This
can be a problem in dynamic environments, where the envi-
ronment is constantly changing. A second issue is the inability
to deal with uncertainty and unpredictability [35]. Many path-
planning algorithms are not able to deal with uncertainty and
unpredictability in the environment. This can be a problem
in dynamic environments, where the environment is often
uncertain and unpredictable. A third issue is the inability
path-planning algorithms are not able to balance collision
avoidance and smoothness. This can lead to paths that are
either too safe (or slow) or too risky (and fast). A fourth issue
is the computational complexity [37]. The environment in
dynamic settings is often uncertain and unpredictable, which
means that the path-planning algorithm must be able to make
decisions based on incomplete information [36]. This can be a
difficult task, as it requires the algorithm to be able to estimate
the probability of different outcomes and to make decisions
that minimize the risk of collisions. Many path-planning al-
gorithms are computationally expensive. This can make it
difficult to find a path in real time or for large environments.
The most significant gap that proposed GA-PRM algo-
rithm is trying to address is the need for efficient and adaptable
path-planning algorithms in healthcare environments. This
gap is significant as it directly influences the practical applica-
tion of robotics in healthcare, which requires a unique set of
capabilities compared to other industries. While the literature
review highlights various advancements in robotics and path