## 5 Conclusions  
Trajectory planning is an important part of the control of intelligent robotic systems such as exoskeleton robots. It has been proven in the literature that trajectory planning helps to better control the movement of exoskeleton joints and improve the ADLs of stroke patients, helping them return to society and their occupation. This review explores the methods of trajectory planning for exoskeleton control, especially at the upper limbs. From 67 relevant papers found in the literature, we categorized trajectory-planning methods into two main approaches:  
1. Approaches based on Cartesian motion planning and inverse kinematics using polynomial-interpolation or computational methods such as minimum-jerk, minimum-torque-change, and inertia-like models.
2. Approaches based on learning by demonstration (LbD) using learning models such as neural networks (NNs), HMMs, Gaussian mixture models, and DMP models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Studies Tested on** & **Studies Tested on** & **Untested on** & **Others** & **Effective** \\
**Healthy Subjects** & **Patients** & **Human Beings** & **Others** & **Effective** \\ \hline
7 & 2 & 10 & 3 & 22 \\
31.8\% & 9\% & 45.4\% & 13.6\% & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kind of tests performed by presented studies.  
As shown in this review, each method has its limitations. In the first approach, generated trajectories by methods based on polynomial trajectories are not natural. Moreover, optimization-based methods that rely on humanlike motions are only appropriate for simple point-to-point movements with a limited range of motion. They are not suitable when applied to complex tasks like those of ADLs. Second, imitation-learning methods have high computational costs.  
In this paper, we also discussed challenges associated with trajectory planning: kinematic redundancy and incompatibility, and the trajectory-optimization problem. For upper-limb exoskeletons, methods based on the computation of swivel angles and other approaches commonly rely on the relationship (e.g., coordinated or synergistic) between degrees of freedom to resolve kinematic redundancy for exoskeletons. Moreover, two general solutions, namely, the self-tracing and alignment-free configurations of the joint axis, which add the appropriate number of extra degrees of freedom to the mechanism, are employed to improve kinematic incompatibility between humans and exoskeletons.  
Although online trajectory planning can be adapted to manage the variability of the environment, very few of the proposed methods for planning trajectories in upper-limb exoskeletons generate offline trajectories. Future work will focus on online trajectory planning and optimal control because very few online methods were found during this study. In future works, tests should be completed on more patients. In any study on exoskeleton robots, we recommend considering the clinical needs presented in this review.  
Conceptualization, C.N. and S.A.; research and original draft preparation, C.N.; review, editing and revised draft preparation, C.N. and M.R.; supervision M.R. and S.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. The authors declare no conflict of interest.