Haut PDF Trajectory planning and control for robot manipulations

Trajectory planning and control for robot manipulations

Trajectory planning and control for robot manipulations

2.3.4.3 Inverse Optimal Control Reinforcement learning or planning in general (e.g. within a MDP framework) tries to generate motions maximizing some reward. Learning (e.g. with Value Iteration) requires constant feedback in the form of rewards for the states and action of the agent. However, in many tasks the reward is not analytically defined and there is no way to access it from the environment accurately. It is then up to the human expert to design a reward leading the robot to the desired behavior. There is an algorithm that can effectively learn the desired behavior and a policy for it just by observing example motions. Inverse Optimal Control (IOC), also known as Inverse Reinforcement Learning (IRL), aims to limit the reward feed- back requirement and human expertise required to design behavior for a task. IOC learns a proper reward function only on the basis of data, see [ Ratliff 06 ]. Let’s assume that policies π give rise to expected feature counts ϖ(π) of feature vectors φ : i.e. what feature we will see if this policy is executed. A weight vector ω such that the behavior demonstrated by the expert π ∗ has higher expected reward (negative costs) than any other policy is learned by minimizing a loss:
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Real-Time Distributed Receding Horizon Motion Planning and Control for Mobile Multi-Robot Dynamic Systems

Real-Time Distributed Receding Horizon Motion Planning and Control for Mobile Multi-Robot Dynamic Systems

Small errors and real-time capability in addition to the distributed and near-optimal aspects of this approach motivate further research about this take on the mobile multi-robot navigation problem. Besides, some issues remain to be addressed. In particular, fail-safe measures when no solution trajectory can be found by Step 1 or Step 2 must be implemented. The obvious next step will be to perform experiments with real mobile robots and analyze the robustness of this method with regard to perception, communication and non-synchronization among vehicles. Another interesting subject is the integration of this motion planning and control to task planners. That integration could enable a higher level of autonomy of the system making it possible to operate in a more complex environment.
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Mission Planning and Execution Control for Intervention Robots

Mission Planning and Execution Control for Intervention Robots

5 Conclusion We presented in this paper a generic architecture for intervention robots. Mission planning and telepro- gramming takes place on a Ground station, and au- tonomous execution, including mission renement, is carried out by the remote robot. The robot control system is composed of a a decisional level and a func- tional level based on a distinction between decision based on global and abstract representations and com- putations on low level numerical representations. The decisional level is composed of a planner-supervisor pair to ensure a deliberative and reactive behavior. The supervisor makes use of procedural representa- tions for plan execution and goal renement, and pur- sues goal-directed tasks while being responsive to chang- ing patterns of events in bounded time. The use of PRS to implement the supervisory part was detailed and its critical features presented. Work on rening this architecture and improving some features in PRS is on-going to better suit control and supervision of autonomous intervention mobile robots.
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convex segmentation and mixed-integer footstep planning for a walking robot

convex segmentation and mixed-integer footstep planning for a walking robot

the distinction between planning target footstep locations and planning the full trajectory of the robot’s state remains. It is not at all obvious that this division of the problem is likely to result in acceptable locomotion plans, but it has proven to be effective in practice, as seen in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. The primary issue with this division is defining and evaluating safety as mentioned in Def. 1.1. We can construct some necessary conditions for the safety of a set of footsteps: (1) adjacent foot placements must be kinematically reachable without self-collision and (2) foot placements must avoid unsafe terrain, e.g. terrain which is too rough, too slippery, too weak, too steep, etc. These conditions on their own are certainly not sufficient to ensure that there is a dynamically feasible trajectory through the given footsteps, or that such a trajectory can be executed by our controller. Existing approaches have typically focused on making conservative footstep plans with relatively short strides, which are likely to admit feasible dynamic trajectories. Even neglecting dynamics, it has still proven to be difficult to design a footstep planner which can produce walking motions which avoid obstacles and can handle complex terrain environments. We are motivated to develop such a planner.
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Motion planning for quadrupedal locomotion: coupled planning, terrain mapping and whole-body control

Motion planning for quadrupedal locomotion: coupled planning, terrain mapping and whole-body control

Abstract—Legged robots promise an advantage over tradi- tional wheeled systems, however, most legged robots are still confined to structured and flat environments. One of the main reasons for this is the difficulty in planning complex whole-body motions while taking into account the terrain conditions. This problem is very high-dimensional as it considers the robot’s dynamics together with the terrain model in a suitable prob- lem formulation. In this work, we propose a novel trajectory and foothold optimization method that plans dynamically both foothold locations and motions (coupled planning). It jointly optimizes body motion, step duration and foothold selection, considering the terrain topology. We show that it can be easily generalized to various terrain conditions (i.e. through the terrain costmap), thanks to a parametrized dynamic model, and an online terrain mapping that is used in our real-time whole- body controller. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. For the sake of analysis, we compare our coupled planner with our previous decoupled planner. With this novel locomotion framework we can cross a wider range of terrain conditions. We report thorough experimental results and comparative evaluations over a set of terrains of progressively increasing difficulty.
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Mobile Robot Navigation and Obstacles Avoidance based on Planning and Re-Planning Algorithm

Mobile Robot Navigation and Obstacles Avoidance based on Planning and Re-Planning Algorithm

avoidance. It presents an adaptive and flexible algorithm of control which guarantees the stability and the smoothness of mobile robot navigation dealing with unexpected events. Moreover, the proposed Planning and Re-Planning (PRP) algorithm combine the two schools of thought, the one based on the path planning to avoid obstacles and reach the target, described as cognitive, and the second using the reactive algorithms. In fact the mix of these two approaches allows us to develop a very reliable algorithm. It provides us a scalable mobile robot navigation and obstacle avoidance, with less processing. It is accomplished by making an initial path planning, then to resolve the problem of unexpected static or dynamic obstacles while tracking the trajectory. A system of hierarchical action selection allows us to switch to a reactive avoidance, then to re-plan a new and safe trajectory to reach the target. A large number of simulations in different environments are performed to show the efficiency of the proposed PRP algorithm.
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Dynamic trajectory planning and synthesis for fully-actuated cable-suspended parallel robots

Dynamic trajectory planning and synthesis for fully-actuated cable-suspended parallel robots

(a) Schematic diagram (b) Prototype Figure 1.12 – A three-DOF CSPR with a point-mass end-effector Dynamic Point-to-Point Trajectory Planning Periodic trajectories provide insight into the fundamental properties of CSPRs and it is found that these properties can be applied when robots are required to move between prescribed points in sequence. The planning of trajectories with the segment connecting consecutive points that have a zero instantaneous velocity at its end points as well as to ensure continuity of the accelerations were investigated. These trajectories can go beyond the static workspace. In ( Gosselin and Foucault , 2014 ), the dynamic trajectory planning of a planar two-DOF point- mass CSPR was addressed. Polynomial trajectories and trajectories based on trigonometric functions were proposed. The main advantage of the latter is that the feasibility can be verified by using simple algebraic expressions and does not require a discretization of the trajectory. However, in order to ensure the continuity of the acceleration, the frequency associated with the motion corresponding to an interval has to be calculated based on the preceding interval. This greatly reduces the chances of obtaining feasible target points. Although this limitation is not a critical issue for the two-DOF robot studied in ( Gosselin and Foucault , 2014 ), it may be more significant for spatial systems with three DOFs or even six DOFs. In spatial systems, target points are no longer located on any of the vertical planes defined by the cable attachment points and thus it is not intuitive to find proper target points. Predicting a region for the next target points then becomes a very useful tool. Moreover, ( Gosselin and Foucault , 2014 ) does not address the case where target points are not feasible and the possible introduction of intermediate points.
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A simple dynamic model for aggressive, near-limits trajectory planning

A simple dynamic model for aggressive, near-limits trajectory planning

V. N UMERICAL RESULTS The previous section provides a set of conditions for the dynamic feasibility of a trajectory, in the form of bounds on vehicle acceleration. These constraints can be used to design a trajectory planner for the vehicle, for instance using model predictive control (MPC). The specifics of our MPC implementation are out of the scope of this paper, and are presented in details in [19]. In this section, we simply present figures extracted from [19] to illustrate the good performance of our model when compared to a more classical kinematic bicycle one [20]. For both models, the planner is used to drive a vehicle along a circuit at high speed; the planning horizon is chosen as T = 3 s, and the time step of the MPC solver (based on the ACADO Toolkit [21]) is 0.2 s.
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Functional architecture for automated vehicles trajectory planning in complex environments

Functional architecture for automated vehicles trajectory planning in complex environments

Social acceptance of these motion planning techniques is increased when comfort is part of the design constraints of the systems. However technological gaps will force a transition period from mere ADAS to fully automated driving. One of the ways to make this transition is to share the control with the driver [Van Schijndel-de Nooij et al., 2011]. This keeps the driver (and his/her driving skills) in the control loop while automation vigilance can be added, increasing comfort (reduce of the workload) and security (warning and emergency actions performed by the embedded system). However, it remains unclear how this shared control system will effectively exchange the control with the driver. Some efforts aim to develop systems where the control sharing is "on-off" by determining stability envelopes as in [Erlien et al., 2016]. Others aim to share control with different levels of authority and control for each of the decision makers, smoothly shifting authority in the control action with respect to lane keeping applications [Saleh et al., 2013] and also introducing human machine cooperation examples or metaphors as in [Flemisch et al., 2014] and [Da Lio et al., 2015]. Another related issue is how the action suggestion is going to be provided [Li et al., 2012]. To address this matter, recent research points in the direction of applying the aid "as needed" rather than "all the time" [Flemisch et al., 2008a], [Petermeijer et al., 2015], thus avoiding drivers’ over-reliance on automation. Planning algorithms can come to aid this kind of systems by defining the limit regions of where warnings or further active actions should be taken according to collision avoidance [Hardy and Campbell, 2013] and human behavior [Bosetti et al., 2015].
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Directional redundancy for robot control

Directional redundancy for robot control

V. A PPLICATION TO V ISUAL S ERVOING All the work presented above has been realized under the only hypothesis that the main task is a task function as described in [21]. The method is thus fully general and can be applied for numerous sensor-based closed-loop control problems. For this article, the method has been applied to visual servoing. In the experiments described in the next section, the robot has to move with respect to a visual target, and simultaneously to take into account a secondary control law. For the simulations, this secondary term was simply an arbitrary velocity. For the exper- iments on the real robot, the joint limits and possible occlusions were considered. In this section, the classical visual servoing formalism is first quickly recalled (Section V-A). Two avoid- ance laws are then presented for joint-limit and visual-occlu- sion avoidance, based on the gradient of a cost function [13], [15], [17]. We have chosen to use a solution proposed from path planning [18] which ensures an optimal computation of the gra- dient by the use of a pseudo inverse. This general method is presented in Section V-B and the two cost functions are given in Section V-C.
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Kinematic Analysis and Trajectory Planning of the Orthoglide 5-Axis

Kinematic Analysis and Trajectory Planning of the Orthoglide 5-Axis

ABSTRACT The subject of this paper is about the kinematic analysis and the trajectory planning of the Orthoglide 5-axis. The Orthoglide 5-axis a five degrees of freedom parallel kinematic machine de- veloped at IRCCyN and is made up of a hybrid architecture, namely, a three degrees of freedom translational parallel manip- ulator mounted in series with a two degrees of freedom parallel spherical wrist. The simpler the kinematic modeling of the Or- thoglide 5-axis, the higher the maximum frequency of its control loop. Indeed, the control loop of a parallel kinematic machine should be computed with a high frequency, i.e., higher than 1.5 MHz, in order the manipulator to be able to reach high speed motions with a good accuracy. Accordingly, the direct and in- verse kinematic models of the Orthoglide 5-axis, its inverse kine- matic Jacobian matrix and the first derivative of the latter with respect to time are expressed in this paper. It appears that the kinematic model of the manipulator under study can be written in a quadratic form due to the hybrid architecture of the Orthoglide 5-axis. As illustrative examples, the profiles of the actuated joint angles (lengths), velocities and accelerations that are used in the control loop of the robot are traced for two test trajectories.
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Planning Human Centered Robot Activities

Planning Human Centered Robot Activities

Fig. 7. Plans stock during planning time for this and it will be done as soon as all its features will be implemented. Future work on HATP will be on three aspects: (1) introduction of heuristics in the hierarchical decomposition system to explore the most promising parts of the solution space first, (2) the ability for the user to define a set of various constraints associated to the problem to influence choices made by HATP and (3) the introduction of monitoring in the plan to anticipate task performance control that will be used during execution. The aspect (1) is very important. Relevance of a heuristics is shown by results on Fig. 7, we can see on it that best plans are found at the end of exploration, so we can expect than with a heuristics we would be able to search in that part of the solution space first reducing thus time needed to plan. The aspect (2) will be very useful to increase richness of plan negotiation. The aspect (3) will allow the robot to anticipate task realization and so, to insert task performance monitors. Thus, the robot will be able to check at specific moments that its human partner is still involved in task realization and so that the task is still relevant.
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Hybrid and Safe Control Architecture for Mobile Robot Navigation

Hybrid and Safe Control Architecture for Mobile Robot Navigation

I. I NTRODUCTION The control of mobile robot navigation in cluttered envi- ronment is a fundamental problem that has been receiving a large amount of attention. The main issues in this field is how to obtain accurate, flexible and reliable navigation? One part of the literature in this domain considers that the robot is fully actuated with no control bound and focuses the attention on path planning. Voronoï diagrams and visibility graphs [1] or navigation functions [2] are among these roadmap-based methods. However, the other part of the literature considers that to control a robot with safety, flexibility and reliability, it is essential to accurately take into account: robot’s structural con- straints (e.g., nonholonomy); avoid command discontinuities and set-point jerk, etc. Nevertheless, even in this method, there are two schools of thought, one uses the notion of planning and re-planning to reach the target, e.g., [3] and [4] and the other more reactive (without planning) like in [5], [6] or [7]. Our proposed control architecture is linked to this last approach. Therefore, where the stability of robot control is rigourously demonstrated and the overall robot behavior is constructed with modular and bottom-up approach [8].
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Nonlinear control with integral sliding properties for circular aerial robot trajectory tracking: Real‐time validation

Nonlinear control with integral sliding properties for circular aerial robot trajectory tracking: Real‐time validation

| INTRODUCTION New civil applications for aerial robots are being developed every day. In recent years, novel implementations of these vehicles have been increasing in complexity, demanding a higher control performance to accomplish their missions. A recurrent task for Unmanned Aerial Vehicles (UAVs) is target inspection and surveillance, which requires that the aerial vehicle continuously tracks its objective with high precision and robustness. Quadrotors are a kind of UAV that has been widely studied in recent years due to their mechanical simplicity and broad availability, typically, these vehicles are capable of performing rotation and translation movements over three axes (six degrees of freedom). This configuration consists on four rotating blades whose aerodynamic effects result on three total torque components and one total thrust (four control inputs).
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Motion Planning and Control for Hilare Pulling a Trailer

Motion Planning and Control for Hilare Pulling a Trailer

lability means that the set of con gurations reachable after any given time always contains a neighborhood of the start- ing con guration. As a consequence any collision-free path can be approximated by a sequence of collision-free feasi- ble paths. Then nonholonomic path planning can be ad- dressed by dealing separately with the physical constraints due to the obstacles and the kinematic constraints due to the wheels. This approach was rst proposed in [26] for a car-like robot. At this level our contribution is to propose a new open-loop steering method accounting for the small- time controllability of the systems. We will show that this property is crucial for the convergence of the algorithm.
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Online Via-Points Trajectory Generation for Reactive Manipulations*

Online Via-Points Trajectory Generation for Reactive Manipulations*

Online Via-Points Trajectory Generation for Reactive Manipulations* Ran Zhao 1,2 , Daniel Sidobre 1,2 and Wuwei He 1,3 Abstract— In various circumstances, such as human-robot interactions and industrial processes, planning at trajectory level is very useful to produce better movement. In this paper we present a near time optimal approach to plan a trajectory joining via points in real time for robot manipulators. To limit the speed variation, the path is smoothed around via-points in a limited area. The concept of online trajectory generation enables systems to react instantaneously at control level to unforeseen events. Simulation and real-world experimental results carried out on a KUKA Light-Weight Robot arm are presented.
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Reactive Planning on a Collaborative Robot for Industrial Applications

Reactive Planning on a Collaborative Robot for Industrial Applications

Yang and Brock (Yang and Brock, 2010) pre- sented an original solution, though lacking convinc- ing experimental validation and generic obstacle de- tection capabilities. It is based on a novel motion gen- eration technique, called elastic roadmap, proposed to generate robust and globally task-consistent motion in dynamic environments. Building and following a re- active path in real time, the motion must satisfy dif- ferent classes of constraints for a redundant mobile manipulator for which the end effector follows a path. Considering our use cases about collaborative robotics, these two approaches have important draw- backs. Flacco et al reacts only at control level by try- ing to increase distance to obstacles; Yang and Brock provide a solution taking advantage of a mobile arm, but they do not take into account human constraints.
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Trajectory planning and control of collaborative systems : Application to trirotor UAVS.

Trajectory planning and control of collaborative systems : Application to trirotor UAVS.

configuration) or three (hexrotor configuration) pairs of rotors in tandem. If the two main rotors share the same rotation axis, the rotors are said to be coaxial, such as for the Kamov Ka-27 of figure 5.6a . Coaxial rotors have a complex mechanical design in comparison to tandem helicopters and complex aerodynamics due to the downwash of the upper rotor on the lower rotor. However, they are rather compact compared to their lift capacities. Some models, such as the Kaman K-MAX depicted in figure 5.6d , have intermeshing rotors. Their rotors are on two different but very close axes. That way, the mechanical design of the rotors is almost as simple as for a tandem helicopter and the design is as compact as for a coaxial helicopter. However, since the two rotors are intermeshed, the helicopter needs an accurate synchronisation mechanism of the two rotors. This unusual configuration led to giving these aircrafts the name of “synchropter”. Another implemented solution is to use tip-jets. Indeed, the transmission of power by a shaft between the engine – located in the helicopter’s fuselage – and the rotor places torque on the main body. In tip-jets helicopters, the rotor is self-powered. While other designs have been studied or used in prototypes, the only tip-jet helicopter to have been put in production, the French SNCASO Djinn, used a compressor to inject compressed air in the hollow blades. The air stream puts the rotor in movement when ejected at the tip nozzles.
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Algorithms for rough terrain trajectory planning

Algorithms for rough terrain trajectory planning

On rough terrain, the planned trajectory must verify several validity constraints : stability of the robot, mechanical limits and collision avoidance with the ground.. Our approach relie[r]

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Efficient and Precise Trajectory Planning for Nonholonomic Mobile Robots

Efficient and Precise Trajectory Planning for Nonholonomic Mobile Robots

The localization of the robot in the playground is estimated by using odometry (as seen in Section 2.2.1 ) and an original global positioning system [ Pie13 ] based on a rotating turret and a set of three beacons that the Eurobot rules allow to place around the play field. Data are mixed together by an extended Kalman filter in order to obtain an accurate and reliable position of the robot at any time. The position of game elements and opponents are obtained with a certain degree of precision through, for example, the use of 3D cameras or ultrasound sensors. These positions are regularly updated at the rate of several tens of Hz. In summary, for this contest, since the game situation changes quickly, it is essential to plan trajectories in real-time, employing a method with very low computational cost, given the limited amount of computing power available on the robot. Information about future locations that will be visited by the robot and their associated timestamps is essential to efficiently implement catching objects and building operations while moving (in order to coordinate the different actuators). Finally, the generated trajectories need to reach quickly their target while taking into account the obstacles and the physical constraints (limited speed or acceleration) of the robot.
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