64,711 research outputs found
Force and impedance control for hydraulically driven hexapod robot walking on uneven terrain
A variety approach of multi-legged robot designs, especially on a large scale design with hydraulically driven actuators exist, but most of it still unsolved and used primitive techniques on control solutions. This made this area of research still far from demonstrating the scientific solutions, which is more towards developing and optimizing the algorithm, control technique and software engineering for practical locomotion (flexibility and reliability). Therefore in this thesis,the study is done to propose two categories of solution for statically stable and hydraulically driven hexapod robot, named COMET-IV, which are dynamic walking trajectory generation and force/impedance control implementation (during body start patching), in order to solve the stability problems (horizontal) that encountered when walking on extremely uneven terrains.Only three sensors are used for control feedback; potentiometers (each leg joint), pressure
sensors (hydraulic cylinders) and attitude sensor (center of body). For dynamic walking trajectory generation, the fixed/determined of tripod walking trajectory is modified with force threshold-based, named as environment trailed trajectory (ETT),on each first step of foot during
support phase (preliminary sensing uneven terrain surfaces). Moreover,the proposed dynamic trajectory generation is then upgraded with capability of omni-directional walking with a
proposed center of body rotational-based method.
The instability of using the ETT module alone and with proposed hybrid force/position control in the previous progress, during body patching on walking session is then solved using the proposed pull-back position-based force control (PPF). PPF controller is derived from the ETT
module itself and supported by proposed compliant (switching) mechanism, logical attitude control and dynamic swing rising control. The limitation of PPF controller applied with ETT module for walking on uneven terrain contains extreme soft surface makes the study narrowed to
the impedance control approaches as a replacement of PPF controller. Three new adaptive impedance controller are designed and proposed: Optimal single leg impedance control based on body inertia, Optimal center of mass—based impedance control based on body inertia and Single
leg impedance control with self-tuning stiffness. To reduce the hard swinging/shaking of the robot's body in motion that arise after applying the proposed impedance controllers, fuzzy logic control via Takagaki-Sugeno-Kang (TSK) model is proposed to be cascaded on the input feedback of the controller.The study has verified the effectiveness of both categories of control unit (dynamic trajectory,force controller and impedance controllers) combination throughout several experiments of COMET-IV walking on uneven/unstructured terrains
Using artificial neural network for forward kinematic problem of under-constrained cable robots
Cable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lack of proper structure and non-positive cable tension prevent the spread of them. In this work, a neural network (NN) model of under constrained cable robots is presented with external forces applied to the end-effector (EE) for computing the position of it. As in under-constrained robot’s kinematics and statics are innately coupled together, and they contemporaneously should be considered the forward kinematic problem of the robot change to an optimization problem. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The main contribution of this paper has three goals. First, a method is used toward kinematic problem of the under constrained cable robot modeling using four bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. Second, in order to track the position of end-effector, an online PD controller is designed by the three error criteria methods such as IAE, ISE and ITSE. Finally, as the third contribution, NN control approach is applied in order to validate the model. A model is created based on the robot’s geometry and dynamic to solve the forward kinematics problem. So, the forward kinematic problem is solved offline and used online. Moreover, an analysis of workspace is performed which discovers that the solution of the forward kinematic problem of the under-constrained cable robots is unique in this case. In addition, a modified local linear model tree algorithm for nonlinear system modelling are proposed. The results show the effectiveness of the proposed approach in modeling the under constrained cable robot
Benchmarking Cerebellar Control
Cerebellar models have long been advocated as viable models
for robot dynamics control. Building on an increasing insight
in and knowledge of the biological cerebellum, many models have been
greatly refined, of which some computational models have emerged
with useful properties with respect to robot dynamics control.
Looking at the application side, however, there is a totally different
picture. Not only is there not one robot on the market which uses
anything remotely connected with cerebellar control, but even in
research labs most testbeds for cerebellar models are restricted to
toy problems. Such applications hardly ever exceed the complexity of
a 2 DoF simulated robot arm; a task which is hardly representative for
the field of robotics, or relates to realistic applications.
In order to bring the amalgamation of the two fields forwards, we
advocate the use of a set of robotics benchmarks, on which existing
and new computational cerebellar models can be comparatively tested.
It is clear that the traditional approach to solve robotics dynamics
loses ground with the advancing complexity of robotic structures;
there is a desire for adaptive methods which can compete as traditional
control methods do for traditional robots.
In this paper we try to lay down the successes and problems in the
fields of cerebellar modelling as well as robot dynamics control.
By analyzing the common ground, a set of benchmarks is suggested
which may serve as typical robot applications for cerebellar models
Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios
In this work, we consider the problem of decentralized multi-robot target
tracking and obstacle avoidance in dynamic environments. Each robot executes a
local motion planning algorithm which is based on model predictive control
(MPC). The planner is designed as a quadratic program, subject to constraints
on robot dynamics and obstacle avoidance. Repulsive potential field functions
are employed to avoid obstacles. The novelty of our approach lies in embedding
these non-linear potential field functions as constraints within a convex
optimization framework. Our method convexifies non-convex constraints and
dependencies, by replacing them as pre-computed external input forces in robot
dynamics. The proposed algorithm additionally incorporates different methods to
avoid field local minima problems associated with using potential field
functions in planning. The motion planner does not enforce predefined
trajectories or any formation geometry on the robots and is a comprehensive
solution for cooperative obstacle avoidance in the context of multi-robot
target tracking. We perform simulation studies in different environmental
scenarios to showcase the convergence and efficacy of the proposed algorithm.
Video of simulation studies: \url{https://youtu.be/umkdm82Tt0M
Dynamic whole-body motion generation under rigid contacts and other unilateral constraints
The most widely used technique for generating wholebody motions on a humanoid robot accounting for various tasks and constraints is inverse kinematics. Based on the task-function approach, this class of methods enables the coordination of robot movements to execute several tasks in parallel and account for the sensor feedback in real time, thanks to the low computation cost.
To some extent, it also enables us to deal with some of the robot constraints (e.g., joint limits or visibility) and manage the quasi-static balance of the robot. In order to fully use the whole range of possible motions, this paper proposes extending the task-function approach to handle the full dynamics of the robot multibody along with any constraint written as equality or inequality of the state and control variables. The definition of multiple objectives is made possible by ordering them inside a strict hierarchy. Several models of contact with the environment can be implemented in the framework. We propose a reduced formulation of the multiple rigid planar contact that keeps a low computation cost. The efficiency of this approach is illustrated by presenting several multicontact dynamic motions in simulation and on the real HRP-2 robot
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
- …