Purpose: To conduct a survey on the research and development of cable-driven rehabilitation devices (CDRDs). Method: This review searches in the databases of PubMed, IEEE Xplore Digital Library, Science Direct, and Google Scholar using various combinations of the following keywords: cable, wire, rehabilitation, assistance, therapy, training, robot, elastic, and pneumatic. Searches in the above databases for references cited by the above-searched references are also conducted to include a larger context of CDRDs. Results: CDRDs are classified into four categories, namely, serial exoskeleton-based, parallel exoskeleton-based, serial end-effector-based, and parallel end-effector-based CDRDs. Each category of CDRDs are further grouped based on the part of the human body to be rehabilitated. All four categories of CDRDs are examined and compared and their advantages and shortcomings are discussed based on popular rehabilitation requirements on weight, adaptability, versatility, misalignment, and safety. Open issues of CDRDs are also discussed. Conclusions: Each category of CDRDs has its own advantages and shortcomings. The selection of a CDRD highly depends on the specific application. Regarding the convenience of setting up a CDRD for rehabilitation, parallel CDRDs usually have better adaptability than serial ones. However, uncertainties come with parallel CDRDs, which makes the control of parallel CDRDs more challenging. Moreover, the strategy of inherent safety has a great potential to further improve the safety of CDRDs.

The movable anchor points make reconfigurable cable-driven parallel robots (RCDPRs) advantageous over conventional cable-driven parallel robots with fixed anchor points, but the movable anchor points also introduce an inherent problem—reconfiguration planning. Scholars have proposed reconfiguration planning approaches for RCDPRs, taking into account the statics and kinematics of RCDPRs. However, a real-time reconfiguration planning approach that considers the dynamics of an RCDPR and is computationally efficient enough to be integrated into the RCDPR's dynamic controller is still not available in the literature yet. This paper develops a real-time reconfiguration planning approach for RCDPRs. A novel reconfiguration value function is defined to reflect the “value” of an RCDPR configuration and provide a reference index for the reconfiguration planning of an RCDPR. And then, the developed approach conducts reconfiguration planning based on the value of RCDPR configurations. The developed approach is computationally efficient, reducing the reconfiguration planning time by more than 93%, compared to single iteration of a box-constrained optimization-based reconfiguration planning approach. Such a high efficiency allows the developed approach to be integrated into an RCDPR's dynamic controller that usually runs with a high frequency. Integrating reconfiguration planning and dynamic control enhances the control performance of the RCDPR. To verify the effectiveness of the developed approach and the integration of reconfiguration planning and dynamic control for RCDPRs, a case study of an RCDPR with seven cables and four movable anchor points is conducted.

Quadrotors have been used in many areas such as cargo transportation, agriculture, and search and rescue. The low energy density of power sources and the low energy efficiency of quadrotors have prevented quadrotors from a wider range of applications where a large payload has to be carried or long flight time is required. This paper optimizes the energy efficiency of a quadrotor via rotating its arms to proper positions calculated based on the dynamics model of the quadrotor and the power–thrust curve of rotors. The conditions that a quadrotor in steady-state can achieve the optimal energy efficiency are mathematically derived and the energy efficiency of a quadrotor in various scenarios is analyzed. Based on the analysis, an arm-rotation approach is proposed to optimize the energy efficiency of a quadrotor with a center-of-gravity offset in steady hovering. It is shown with simulation that an example quadrotor with rotatable arms can save up to 13% of energy. Experiments show that the same example quadrotor can save even more energy in practice, owing to the byproduct of the arm-rotation approach.