The 3-CRS manipulator is an original parallel mechanism having 6 degrees of freedom (DOFs) with only 3 limbs. This mechanism uses a motorized cylindrical joint per limb. This new paradigm of actuation opens research fields on new families of robots that should particularly interest the parallel robotics community. According to its dimensional synthesis, this mechanism can have remarkable kinematic properties such as a large orientation workspace or reconfiguration capabilities. In this paper, we introduce this mechanism and we study its singularities by using a geometric approach. This approach simplifies considerably singularity analysis problem by considering the relative geometric configurations of three planes defined by the distal links of the limbs. Thanks to that, a reconfiguration mode of the 3-CRS, that doubles its reachable workspace, is highlighted. This property is illustrated on a physical prototype of the robot.

Finding the current pose of the end-effector of a parallel robot is a problem, since its forward geometric model generally has several solutions. Current methods to address this problem operate mainly under the assumption that the robot never changes its assembly mode nor gets close to Type 2 singularities. Nonetheless, recent works proved that a parallel robot can change its assembly mode, thanks to dedicated trajectory generation and control. Such a feature allows increasing the operational workspace of such manipulators. Hence, correctly tracking the end-effector pose while crossing Type 2 singularities is mandatory for a practical usage of this workspace enhancement method. However, on Type 2 singularities, several solutions of the forward geometric model merge, making current tracking methods ineffective. To fill this gap, we propose a two-step pose tracking methodology. First, a differential inclusion based on kinematics and dynamics is solved. Second, joint measurements are used to tighten resulting enclosures. The effectiveness of this method is discussed, thanks to experimental data gathered on a planar parallel robot.

Increasing the size of operationnal workspace is one of the main problems parallel robots are faced with. Among all the proposed solutions to that, crossing Type 2 singularities using dedicated trajectory generation and multi-model controller has a great potential. Yet, this approach is not sufficient for the robot to operate autonomously, as assembly mode detection during the motion currently requires additional redundant information. To tackle this problem, we propose an algorithm based on Interval Analysis (IA) that is able to track the end-effector of the robot even under assembly mode change. IA-based solvers for the forward kinematic problem of parallel robots are well known, but they cannot be used under assembly mode change. Compared to those classical approaches, the major modification introduced is the tracking of end-effector velocity in addition to its pose. Using this new information of velocity, the algorithm is capable to monitor the assembly mode change of the robot happening when the singularities are crossed. The behavior and the reliability of this algorithm are analyzed experimentally on a five-bar planar parallel mechanism.

In order to increase the workspace size of parallel robots (largely reduced by the presence of singularities) several solutions have been proposed. One promising solution consists in the definition of optimal trajectories that ensure the non degeneracy of the dynamic model in the singularity and therefore are able to cross the Type 2 singularities. Those works are based on the computation of the optimal trajectories and assume that the robot can perfectly track the desired trajectory. Nevertheless, this assumption cannot be verified in reality due to modelling errors which largely impact the control law used to follow the desired trajectory. Therefore, if the optimal trajectory is not perfectly tracked, the dynamic model can degenerate near the Type 2 singularities and the robot might stay blocked. In order to solve that problem, this paper proposes a multimodel approach that allows parallel robots to cross the Type 2 singularities without any torque discontinuity. The main idea is to shift near singularities from the full robot dynamic model to another simplified one that can never degenerate. The proposed control law is then coupled with an optimal trajectory planning methodology that makes the singularity crossing more robust to modelling errors. The proposed approach is validated experimentally on a prototype of Five-bar planar parallel mechanism.

As the end-effector pose is an external property of a parallel robot, it is natural to use exteroceptive sensors to measure it in order to suppress inaccuracies coming from modelling errors. Cameras offer this possibility. So, it is possible to obtain higher accuracy than in the case of classic control schemes (based on geometrical model). In some cases, it is impossible to directly observe the end-effector, but the leg directions can instead be used. In this case, however, unusual results were recorded, namely: (i) the possibility of controlling the robot by observing a number of legs less than the total number of legs, and that (ii) in some cases, the robot does not converge to the desired end-effector pose, even if the observed leg directions did. These results can be explained through the use of the hidden robot concept, which is a tangible visualisation of the mapping between the observed leg direction space (internal property) and Cartesian space (external property). This hidden robot has different assembly modes and singular configurations from the real robot, and it is a powerful tool to simplify the analysis of the aforementioned mapping. In this paper, the concept of hidden robot model is generalised for any type of parallel robot controlled through visual servoing based on observation of the leg directions. Validation has been accomplished through experiments on a Quattro robot with 4 dof.

This paper discusses variable selection for the efficient dynamic control of the Quattro parallel robot through an inverse dynamic model expressed by means of leg orientations. A selection is made within a group of variables where each can imply the state of the robot. Besides, in this work, steering a parallel robot dynamically using its self-projection onto the image plane (where the edges of the lower-legs are exploited in control) is proposed and validated for the first time. In the light of the realistic control simulation, the formative points of better control of the Quattro robot are figured out.

Automation in outdoor applications (farming, surveillance, etc.) requires highly accurate control of mobile robots, at high speed, accounting for natural ground specificities (mainly sliding effects). In previous work, predictive control algorithms dedicated to All-Terrain Vehicle lateral stability was investigated. Satisfactory advanced simulation results have been reported but no experimental ones were presented. In this paper, the prevention of a real off-road mobile robot rollover is addressed. First, both rollover dynamic modeling and previous work on a Mixed observer designed to estimate on-line sliding phenomena for path tracking control are recalled. Then, this observer is here used to compute a rollover indicator accounting for sliding phenomena, from a low-cost perception system. Next, the maximum vehicle velocity, compatible with a safe motion over some horizon of prediction, is computed via Predictive Functional Control (PFC), and can then be applied, if needed, to the vehicle actuator to prevent from rollover. The capabilities of the proposed device are demonstrated and discussed thanks to real experimentation.

The active devices dedicated to on-road vehicle stability cannot be applied satisfactorily in an off-road context, since the variability and the non-linear features of grip conditions can no longer be neglected. Specific solutions have then to be investigated. In this paper, the prevention of light all-terrain vehicle (ATV) rollover is addressed. First, a backstepping observer is designed in order to estimate online a rollover indicator accounting for sliding phenomena, from a low-cost perception system. Next, the maximum vehicle velocity, compatible with a safe motion over some horizon of prediction, is computed via predictive functional control (PFC), and can then be applied, if needed, to the vehicle actuator to prevent from rollover. The capabilities of the proposed device are demonstrated and discussed thanks to an advanced simulation testbed that has proved to supply results very close to experimental ones.

Lateral rollover is the leading cause of fatal accidents in light all-terrain vehicles (e.g. quad bikes), especially in the agricultural area. The estimation and prediction of hazardous situations are preliminary steps in the design of active security devices. If numerous metrics have already been defined for on-road vehicles, few approaches are suitable for fast motions in a natural environment (mainly due to tire/ground contact specificity and variability). This paper proposes an algorithm dedicated to the estimation and prediction of one metric, namely lateral load transfer (LLT), in order to anticipate rollover situations on an irregular and natural ground. It is based on a vehicle dynamic model, used jointly with a backstepping observer. It allows to take into account tire/ground contact nonlinearities and variability, which impact the rollover tendency. The efficiency of the metric is investigated through advanced simulations and full scale experiments on a Kymco quad bike.

The lateral rollover of quad bikes represents a significant part of severe accidents in the field of agricultural work. The specificities of such vehicles (small wheelbase, track and weight, as well as high speed), together with the terrain configuration (off-road environment) prevent from describing rollover occurrence as it is proposed for car-like vehicles. In particular, sliding effects significantly affects the evaluation of the rollover risk. This paper proposes a rollover risk indicator dedicated to off-road vehicles, taking into account the environment properties and more particularly the grip condition and its variation. It is based on the prediction of the lateral load transfer relying on vehicles models including sliding effects. This indicator can be run on-line when the vehicle is moving. It allows anticipating a potential danger, and could then be used to design security systems. Performances of this indicator are demonstrated using the multibody dynamic simulation software Adams.

Most of active devices focused on vehicle stability concerns on-road cars and cannot be applied satisfactorily in an off-road context, since the variability and the non-linearities of the tire/ground contact are often neglected. In previous work, a rollover indicator devoted to light ATVs, accounting for these phenomena has been proposed. It is based on the prediction of the lateral load transfer. Such an indicator requires the online knowledge of the tire cornering stiffness, initially selected from a ground classes network. In this paper, an adapted backstepping observer, making only use of yaw rate measurement, is designed to improve specifically tire cornering stiffness estimation. Capabilities of such an observer are demonstrated and discussed through both advanced simulations and actual experiments.