Michiel Plooij

Lock Your Robot: A Review of Locking Devices in Robotics

Locking devices are widely used in robotics, for instance to lock springs and joints or to reconfigure robots. This review article classifies the locking devices currently described in the literature and performs a comparative study. Designers can therefore better determine which locking device best matches the needs of their application. The locking devices are divided into three main categories based on different locking principles: 1) mechanical locking, 2) friction-based locking, and 3) singularity locking. Different locking devices in each category can be passive or active. Based on an extensive literature survey, this article summarizes the findings by comparing different locking devices on a set of properties of an ideal locking device.

A novel spring mechanism to reduce energy consumption of robotic arms

Most conventional robotic arms use motors to accelerate the manipulator. This leads to an unnecessary high energy consumption when performing repetitive tasks. This paper presents an approach to reduce energy consumption in robotic arms by performing its repetitive tasks with the help of a parallel spring mechanism. A special non-linear spring characteristic has been achieved by attaching a spring to two connected pulleys. This parallel spring mechanism provides for the accelerations of the manipulator without compromising its ability to vary the task parameters (the time per stroke, the displacement per stroke the grasping time and the payload). The energy consumption of the arm with the spring mechanism is compared to that of the same arm without the spring mechanism. Optimal control studies show that the robotic arm uses 22% less energy due to the spring mechanism. On the 2 DOF prototype, we achieved an energy reduction of 20%. The difference was due to model simplifications. With a spring mechanism, there is an extra energetic cost, because potential energy has to be stored into the spring during startup. This cost is equal to the total energy savings of the 2 DOF arm during 8 strokes. Next, there could have been an energetic cost to position the manipulator outside the equilibrium position. We have designed the spring mechanism in such a way that this holding cost is negligible for a range of start- and end positions. The performed experiments showed that the implementation of the proposed spring mechanism results in a reduction of the energy consumption while the arm is still able to handle varying task parameters.

Optimization of feedforward controllers to minimize sensitivity to model inaccuracies

The common view on feedforward control is that it needs an accurate model in order to accurately predict a future state of the system. However, in this paper we show that there are model inaccuracies that do not affect the final position of a motion, when using the right feedforward controller. Having an accurate final position is the main requirement in the task we consider: a pick-and-place task. We optimized the feedforward controllers such that the effect of model inaccuracies on the final position was minimized. The system we studied is a one DOF robotic arm in the horizontal plane, of which we show simulation and hardware results. The results show that the errors in the final position can be reduced to approximately zero for an inaccurate Coulomb, viscous or torque dependent friction. Furthermore, errors in the final position can be reduced, but not to zero, for an inaccurate inertia or motor constant. In conclusion, we show that for certain model inaccuracies, no feedback is required to eliminate the effect of an inaccurate model on the final position of a motion.

Design and evaluation of the Bi-directional Clutched Parallel Elastic Actuator (BIC-PEA)

Parallel elastic actuators (PEAs) have shown the ability to reduce the energy consumption of robots. The problem with regular PEAs is that it is not possible to freely choose at which instant or configuration to store or release energy. This paper introduces the concept and the design of the Bi-directional Clutched Parallel Elastic Actuator (BIC-PEA*), which reduces the energy consumption of robots by loading and unloading the parallel spring in a controlled manner. The concept of the BIC-PEA consists of a spring that is mounted between the two outgoing axes of a differential mechanism. Those axes can also be locked to the ground by two locking mechanisms. At any position, the BIC-PEA can store the kinetic energy of a joint in the spring such that the joint is decelerated to zero velocity. The spring energy can then be released, accelerating the joint in any desired direction. In our prototype of 202 g, the energy that can be stored in the spring is 0.77 J. When disengaged, the friction that the mechanism adds is negligible. The current maximum over-all efficiency is 62 %, which is about 55% more than what generally can be achieved by recapturing the energy electrically. Its relatively high efficiency and controllability make the BIC-PEA a promising concept for reducing the energy consumption of robots.

Open loop stable control in repetitive manipulation tasks

Most conventional robotic arms depend on sensory feedback to perform their tasks. When feedback is inaccurate, slow or otherwise unreliable, robots should behave more like humans: rely on feedforward instead. This paper presents an approach to perform repetitive tasks with robotic arms, without the need for feedback (i.e. the control is open loop). The cyclic motions of the repetitive tasks are analyzed using an approach similar to limit cycle theory. We optimize open loop control signals that result in open loop stable motions. This approach to manipulator control was implemented on a two DOF arm in the horizontal plane with a spring on the first DOF, of which we show simulation and hardware results. The results show that both in simulation and in hardware experiments, it is possible to create open loop stable cycles. However, the two resulting cycles are different due to model inaccuracies. We also show simulation and hardware results for an inverted pendulum, of which we have a more accurate model. These results show stable cycles that are the same in simulation and hardware experiments.

Robust feedforward control of robotic arms with friction model uncertainty

To design feedforward controllers for robots, a model that includes friction is important. However, friction is hard to identify, which causes uncertainty in the model. In this paper we consider rest-to-rest motions of robotic arms that use only feedforward control. We show that it is possible to design feedforward controllers such that the final position of the motion is robust to uncertainty in the friction model. We studied a one DOF robotic arm in the horizontal plane, of which we show analytical, simulation and hardware results and we also show simulation results of a planar two DOF arm. Our friction model includes three types of friction: viscous, Coulomb and torque dependent friction. The results show that it is possible to eliminate the sensitivity of the final state to uncertainty in the three types of friction. We consider feedforward controlled rest-to-rest motions of robotic arms.The final positions of the motions are robust to uncertainty in the friction model.We study one and two DOF arms and perform analytical, numerical and hardware studies.Robust motions first move away from the goal position before moving towards it.

Learning while preventing mechanical failure due to random motions

Learning can be used to optimize robot motions to new situations. Learning motions can cause high frequency random motions in the exploration phase and can cause failure before the motion is learned. The mean time between failures (MTBF) of a robot can be predicted while it is performing these motions. The predicted MTBF in the exploration phase can be increased by filtering actions or possible actions of the algorithm. We investigated five algorithms that apply this filtering in various ways and compared them to SARSA(λ) learning. In general, increasing the MTBF decreases the learning performance. Three of the investigated algorithms are unable to increase the MTBF while keeping their learning performance approximately equal to SARSA(λ). Two algorithms are able to do this: the PADA algorithm and the low-pass filter algorithm. In case of LEO, a bipedal walking robot that tries to optimize a walking motion, the MTBF can be increased by a factor of 108 compared to SARSA(λ). This indicates that, in some cases, failures due to high frequency random motions can be prevented without decreasing the performance.

Clutched Elastic Actuators

This paper identifies the class of actuators called clutched elastic actuators (CEAs). CEAs use clutches to control the energy flow into springs. CEAs in exoskeletons, prostheses, legged robots, and robotic arms have shown the ability to reduce the energy consumption and motor requirements, such as peak torque and peak power. Because of those abilities, they are increasingly used in robotics. In this paper, we categorize existing CEA designs, identify trends in those designs, and provide a method to analyze their functionality. Based on a literature survey, current CEA designs are placed in nine categories, depending on their morphology. The main trend is that CEA designs are becoming more complex, meaning that the number of clutches and springs increases. We show with the introduced mathematical analysis that the functionality can be analyzed with a constraint matrix, a stiffness matrix, and multiplication of a clutch-dependent diagonal matrix with an oriented incidence matrix. This method eases the analysis of the functionality of CEAs. Furthermore, it can lead to new CEA designs in which the number of resulting stiffnesses grows exponentially with the number of springs and clutches.

Reducing the Energy Consumption of Robots Using the Bidirectional Clutched Parallel Elastic Actuator

Parallel elastic actuators (PEAs) have shown the abilto reduce the energy consumption of robots. However, regular PEAs do not allow us to freely choose at which instant or configuration to store or release energy. This paper introduces the concept and design of the bidirectional clutched parallel elastic actuator (BIC-PEA), which reduces the energy consumption of robots by loading and unloading a parallel spring with controlled timing and direction. The concept of the BIC-PEA consists of a spring that mounted between the two outgoing axes of a differential mechanism. Those axes can also be locked to the ground by two locking mechanisms. At any position, the BIC-PEA can store the kinetic energy of a joint in the spring such that the joint is decelerated zero velocity. The spring energy can then be released, accelerating joint in any desired direction. Such functionality is suitable for robots that perform rest-to-rest motions, such as pick-and-place robots or intermittently moving belts. The main body of our prototype weighs 202 g and fits in a cylinder with a length of 51 mm and a diameter of 45 mm. This excludes the size and weight nonoptimized clutches, which would approximately triple the total volume and weight. In the results, we also omit the energy consumption of the clutches. The BIC-PEA can store 0.77 J and a peak torque of 1.5 N·m. Simulations show that the energy consumption of our one-degree-of-freedom setup can be reduced 73%. In hardware experiments, we reached peak reductions 65% and a reduction of 53% in a realistic task, which is larger than all other concepts with the same functionality.

Influence of body weight unloading on human gait characteristics: a systematic review

BackgroundBody weight support (BWS) systems have shown promise as rehabilitation tools for neurologically impaired individuals. This paper reviews the experiment-based research on BWS systems with the aim: (1) To investigate the influence of body weight unloading (BWU) on gait characteristics; (2) To study whether the effects of BWS differ between treadmill and overground walking and (3) To investigate if modulated BWU influences gait characteristics less than unmodulated BWU.MethodA systematic literature search was conducted in the following search engines: Pubmed, Scopus, Web of Science and Google Scholar. Statistical analysis was used to quantify the effects of BWU on gait parameters.Results54 studies of experiments with healthy and neurologically impaired individuals walking in a BWS system were included and 32 of these were used for the statistical analysis. Literature was classified using three distinctions: (1) treadmill or overground walking; (2) the type of subjects and (3) the nature of unloading force. Only 27% studies were based on neurologically impaired subjects; a low number considering that they are the primary user group for BWS systems. The studies included BWU from 5% to 100% and the 30% and 50% BWU conditions were the most widely studied. The number of participants varied from 1 to 28, with an average of 12. It was seen that due to the increase in BWU level, joint moments, muscle activity, energy cost of walking and ground reaction forces (GRF) showed higher reduction compared to gait spatio-temporal and joint kinematic parameters. The influence of BWU on kinematic and spatio-temporal gait parameters appeared to be limited up to 30% unloading. 5 gait characteristics presented different behavior in response to BWU for overground and treadmill walking. Remaining 21 gait characteristics showed similar behavior but different magnitude of change for overground and treadmill walking. Modulated unloading force generally led to less difference from the 0% condition than unmodulated unloading.ConclusionThis review has shown that BWU influences all gait characteristics, albeit with important differences between the kinematic, spatio-temporal and kinetic characteristics. BWU showed stronger influence on the kinetic characteristics of gait than on the spatio-temporal parameters and the kinematic characteristics. It was ascertained that treadmill and overground walking can alter the effects of BWU in a different manner. Our results indicate that task-specific gait training is likely to be achievable at a BWU level of 30% and below.