_Applied_  
_sciences_  
_Motion Planning of Upper- Limb Exoskeleton Robots: A Review_  
**[PERSON] 1,2,3,*, [PERSON] 1,2 and [PERSON] 1**  
1 Department of Mechanical Engineering, Ecole Polytechnique de Montreal, Montreal, QC H3T 1J4, Canada; [EMAIL_ADDRESS] (M.R.); [EMAIL_ADDRESS] (S.A.)  
2 Technopole in Pediatric Rehabilitation of Ste-Justine UHC, Montreal, QC H3T 1C5, Canada  
3 CRME--Research Center, Office GR-123, 5200, East Belanger Street, Montreal, QC H1T 1C9, Canada  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +1-(514)-374-1710-8189  
**Abstract:** (1) Background: Motion planning is an important part of exoskeleton control that improves the wearer's safety and comfort. However, its usage introduces the problem of trajectory planning. The objective of trajectory planning is to generate the reference input for the motion-control system. This review explores the methods of trajectory planning for exoskeleton control. In order to reduce the number of surveyed papers, this review focuses on the upper limbs, which require refined three-dimensional motion planning. (2) Methods: A systematic search covering the last 20 years was conducted in Ei Compendex, Inspect-IET, Web of Science, PubMed, ProQuest, and Science-Direct. The search strategy was to use and combine terms \"trajectory planning\", \"upper limb\", and \"exoskeleton\" as high-level keywords. \"Trajectory planning\" and \"motion planning\" were also combined with the following keywords: \"rehabilitation\", \"humanlike motion\", \"upper extremity\", \"inverse kinematic\", and \"learning machine \". (3) Results: A total of 67 relevant papers were discovered. Results were then classified into two main categories of methods to plan trajectory: (i) Approaches based on Cartesian motion planning, and inverse kinematics using polynomial-interpolation or optimization-based methods such as minimum-jerk, minimum-torque-change, and inertia-like models; and (ii) approaches based on \"learning by demonstration\" using machine-learning techniques such as supervised learning based on neural networks, and learning methods based on hidden Markov models, Gaussian mixture models, and dynamic motion primitives. (4) Conclusions: Various methods have been proposed to plan the trajectories for upper-limb exoskeleton robots, but most of them plan the trajectory offline. The review approach is general and could be extended to lower limbs. Trajectory planning has the advantage of extending the applicability of therapy robots to home usage (assistive exoskeletons); it also makes it possible to mitigate the shortages of medical caregivers and therapists, and therapy costs. In this paper, we also discuss challenges associated with trajectory planning: kinematic redundancy and incompatibility, and the trajectory-optimization problem. Commonly, methods based on the computation of swivel angles and other methods rely on the relationship (e.g., coordinated or synergistic) between the degrees of freedom used to resolve kinematic redundancy for exoskeletons. Moreover, two general solutions, namely, the self-tracing configuration of the joint axis and the alignment-free configuration of the joint axis, which add the appropriate number of extra degrees of freedom to the mechanism, were employed to improve the kinematic incompatibility between human and exoskeleton. Future work will focus on online trajectory planning and optimal control. This will be done because very few online methods were found in the scope of this study.  
**Keywords:** automatic systems; trajectory planning; exoskeleton; imitation learning; inverse kinematics; machine learning