Motion planning of upper-limb exoskeleton robots : a review

ABSTRACT: 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. 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 “. 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. 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

Motion planning for self-reconfiguring robotic systems

Robots that can actively change morphology offer many advantages over fixed shape, or monolithic, robots: flexibility, increased maneuverability and modularity. So called self-reconfiguring systems (SRS) are endowed with a shape changing ability enabled by an active connection mechanism. This mechanism allows a mechanical link to be engaged or disengaged between two neighboring robotic subunits. Through utilization of embedded joints to change the geometry plus the connection mechanism to change the topology of the kinematics, a collection of robotic subunits can drastically alter the overall kinematics. Thus, an SRS is a large robot comprised of many small cooperating robots that is able to change its morphology on demand. By design, such a system has many and variable degrees of freedom (DOF). To gain the benefits of self-reconfiguration, the process of morphological change needs to be controlled in response to the environment. This is a motion planning problem in a high dimensional configuration space. This problem is complex because each subunit only has a few internal DOFs, and each subunit's range of motion depends on the state of its connected neighbors. Together with the high dimensionality, the problem may initially appear to be intractable, because as the number of subunits grow, the state space expands combinatorially. However, there is hope. If individual robotic subunits are identical, then there will exist some form of regularity in the resulting state space of the conglomerate. If this regularity can be exploited, then there may exist tractable motion planning algorithms for self-reconfiguring system. Existing approaches in the literature have been successful in developing algorithms for specific SRSs. However, it is not possible to transfer one motion planning algorithm onto another system. SRSs share a similar form of regularity, so one might hope that a tool from mathematical literature would identify the common properties that are exploitable for motion planning. So, while there exists a number of algorithms for certain subsets of possible SRS instantiations, there is no general motion planning methodology applicable to all SRSs. In this thesis, firstly, the best existing general motion planning techniques were evaluated to the SRS motion planning problem. Greedy search, simulated annealing, rapidly exploring random trees and probabilistic roadmap planning were found not to scale well, requiring exponential computation time, as the number of subunits in the SRS increased. The planners performance was limited by the availability of a good general purpose heuristic. There does not currently exist a heuristic which can accurately guide a path through the search space toward a far away goal configuration. Secondly, it is shown that a computationally efficient reconfiguration algorithms do exist by development of an efficient motion planning algorithm for an exemplary SRS, the Claytronics formulation of the Hexagonal Metamorphic Robot (HMR). The developed algorithm was able to solve a randomly generated shape-to-shape planning task for the SRS in near linear time as the number of units in the configuration grew. Configurations containing 20,000 units were solvable in under ten seconds on modest computational hardware. The key to the success of the approach was discovering a subspace of the motion planning space that corresponded with configurations with high mobility. Plans could be discovered in this sub-space much more readily because the risk of the search entering a blind alley was greatly reduced. Thirdly, in order to extract general conclusions, the efficient subspace, and other efficient subspaces utilized in other works, are analyzed using graph theoretic methods. The high mobility is observable as an increase in the state space's Cheeger constant, which can be estimated with a local sampling procedure. Furthermore, state spaces associated with an efficient motion planning algorithm are well ordered by the graph minor relation. These qualitative observations are discoverable by machine without human intervention, and could be useful components in development of a general purpose SRS motion planner compiler

The Ariadne's Clew Algorithm

We present a new approach to path planning, called the "Ariadne's clew algorithm". It is designed to find paths in high-dimensional continuous spaces and applies to robots with many degrees of freedom in static, as well as dynamic environments - ones where obstacles may move. The Ariadne's clew algorithm comprises two sub-algorithms, called Search and Explore, applied in an interleaved manner. Explore builds a representation of the accessible space while Search looks for the target. Both are posed as optimization problems. We describe a real implementation of the algorithm to plan paths for a six degrees of freedom arm in a dynamic environment where another six degrees of freedom arm is used as a moving obstacle. Experimental results show that a path is found in about one second without any pre-processing

A hyper-redundant manipulator

“Hyper-redundant” manipulators have a very large number of actuatable degrees of freedom. The benefits of hyper-redundant robots include the ability to avoid obstacles, increased robustness with respect to mechanical failure, and the ability to perform new forms of robot locomotion and grasping. The authors examine hyper-redundant manipulator design criteria and the physical implementation of one particular design: a variable geometry truss

A geometrical approach to the motion planning problem for a submerged rigid body

The main focus of this paper is the motion planning problem for a deeply submerged rigid body. The equations of motion are formulated and presented by use of the framework of differential geometry and these equations incorporate external dissipative and restoring forces. We consider a kinematic reduction of the affine connection control system for the rigid body submerged in an ideal fluid, and present an extension of this reduction to the forced affine connection control system for the rigid body submerged in a viscous fluid. The motion planning strategy is based on kinematic motions; the integral curves of rank one kinematic reductions. This method is of particular interest to autonomous underwater vehicles which can not directly control all six degrees of freedom (such as torpedo shaped AUVs) or in case of actuator failure (i.e., under-actuated scenario). A practical example is included to illustrate our technique

Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening