Optimal robot motions for physical criteria

Newly Elaborated Hybrid Algorithm for Optimization of Robot Arm’s Trajectory in Order to Increase Efficiency and Provide Sustainability in Production

Nowadays, resources for production (raw materials, human, energy, etc.) are limited, while population, consumption and environmental damage are continuously increasing. Consequently, the current practices of resource usage are not sustainable. Therefore, manufacturing companies have to change to environmentally friendly and innovative technologies and tools, e.g., industrial robots. Robots are necessary in the production sector and also in terms of sustainability: (1) the application of robots is needed to compensate for the lack of human resources; (2) robots can increase productivity significantly; and (3) there are several hazardous (e.g., chemical, physical) industrial tasks for which robots are more adequate than human workforce. This article introduces a newly elaborated Hybrid Algorithm for optimization of a robot arm’s trajectory by the selection of that trajectory that has the smallest cycle time. This Hybrid Algorithm is based on the Tabu Search Algorithm and also uses two added methods—Point Insertion and Grid Refinement—simultaneously to find more precisely the optimal motion path of the robot arm in order to further reduce the cycle time and utilize the joints’ torque more efficiently. This Hybrid Algorithm is even more effective than applying the Tabu Search method alone and results in even higher efficiency improvement. The Hybrid Algorithm is executed using MATLAB software by creating a dynamic model of a 5 degree-of-freedom robot arm. The main contribution of the research is the elaboration of the new Hybrid Algorithm, which results in the minimization of robot arms’ motion cycle times, causing a significant increase in productivity and thus a reduction in specific production cost; furthermore, obstacles in the workspace can be avoided. The efficiency of the Hybrid Algorithm is validated by a case study showing that application of the new algorithm resulted in 32% shorter motion cycle time.

Optimal Robot Motion for Accelerated Life Testing of a 6-DoF Industrial Robot

In order to verify the reliability of drive components for industrial robots, component-level life tests must be accompanied by a system-level life test using actual robots in which predefined robot motions are repeated throughout the test. To properly verify the durability of drive components through a system-level life test, it is important to design test modes so that the required test time is the same for all joint drive components of the robot, and it is necessary to design test modes with a high acceleration factor so as to shorten the required test time as much as possible. To solve this problem, the present research proposes a method for designing robot motions that makes the accelerated life test time for all the drive components of the robot equal. In particular, we solve a dynamic based motion optimization problem for an industrial 6-DoF (degrees-of-freedom) robot that minimizes the AM-GM (arithmetic mean to geometric mean) ratio of the acceleration factors of each joint. The results show that C2-continuous test modes with the same acceleration factor, which is inversely proportional to the cycle time of the robot motion, can be derived.

Design and Implementation of a Graphic Simulator for Calculating the Inverse Kinematics of a Redundant Planar Manipulator Robot

In this paper, a graphics simulator that allows for characterizing the kinematic and dynamic behavior of redundant planar manipulator robots is presented. This graphics simulator is implemented using the Solidworks software and the SimMechanics Toolbox of MATLAB/Simulink. To calculate the inverse kinematics of this type of robot, an algorithm based on the probabilistic method called Simulated Annealing is proposed. By means of this method, it is possible to obtain, among many possibilities, the best solution for inverse kinematics. Without losing generality, the performance of metaheuristic algorithm is tested in a 6-DoF (Degrees of Freedom) virtual robot. The Cartesian coordinates (x,y) of the end effector of the robot under study can be accessed through a graphic interface, thereby automatically calculating its inverse kinematics, and yielding the solution set with the position adopted by each joint for each coordinate entered. Dynamic equations are obtained from the Lagrange–Euler formulation. To generate the joint trajectories, an interpolation method with a third order polynomial is used. The effectiveness of the developed methodologies is verified through computational simulations of a virtual robot.

Development of a Tele-Operated Rescue Robot for a Disaster Response

This paper proposes practical hardware design strategies and control methods for a rescue robot to save patients in disastrous environments. None of the existing humanoid robots have not shown the capability to efficiently execute rescue tasks for transferring a human to a safe place in a highly unstructured world. To resolve this problem a new form of powerful dual arm mechanism and hybrid tracked-legged mobile platform is developed and the motion is synthesized with dynamics based optimization and a modified hierarchical control scheme. These new design and control policies enable us to simultaneously enhance the manipulation performance and driving stability which have been verified through both in extensive numerical simulations and physical experiments where the rescue robot and whole-body control are indeed required.

Identification of the contribution of contact and aerial biomechanical parameters in acrobatic performance

INTRODUCTION

Teaching acrobatic skills with a minimal amount of repetition is a major challenge for coaches. Biomechanical, statistical or computer simulation tools can help them identify the most determinant factors of performance. Release parameters, change in moment of inertia and segmental momentum transfers were identified in the prediction of acrobatics success. The purpose of the present study was to evaluate the relative contribution of these parameters in performance throughout expertise or optimisation based improvements. The counter movement forward in flight (CMFIF) was chosen for its intrinsic dichotomy between the accessibility of its attempt and complexity of its mastery.

METHODS

Three repetitions of the CMFIF performed by eight novice and eight advanced female gymnasts were recorded using a motion capture system. Optimal aerial techniques that maximise rotation potential at regrasp were also computed. A 14-segment-multibody-model defined through the Rigid Body Dynamics Library was used to compute recorded and optimal kinematics, and biomechanical parameters. A stepwise multiple linear regression was used to determine the relative contribution of these parameters in novice recorded, novice optimised, advanced recorded and advanced optimised trials. Finally, fixed effects of expertise and optimisation were tested through a mixed-effects analysis.

RESULTS AND DISCUSSION

Variation in release state only contributed to performances in novice recorded trials. Moment of inertia contribution to performance increased from novice recorded, to novice optimised, advanced recorded, and advanced optimised trials. Contribution to performance of momentum transfer to the trunk during the flight prevailed in all recorded trials. Although optimisation decreased transfer contribution, momentum transfer to the arms appeared.

CONCLUSION

Findings suggest that novices should be coached on both contact and aerial technique. Inversely, mainly improved aerial technique helped advanced gymnasts increase their performance. For both, reduction of the moment of inertia should be focused on. The method proposed in this article could be generalized to any aerial skill learning investigation.

Task-space separation principle: a force-field approach to motion planning for redundant manipulators

In this work, we address human-like motor planning in redundant manipulators. Specifically, we want to capture postural synergies such as Donders' law, experimentally observed in humans during kinematically redundant tasks, and infer a minimal set of parameters to implement similar postural synergies in a kinematic model. For the model itself, although the focus of this paper is to solve redundancy by implementing postural strategies derived from experimental data, we also want to ensure that such postural control strategies do not interfere with other possible forms of motion control (in the task-space), i.e. solving the posture/movement problem. The redundancy problem is framed as a constrained optimization problem, traditionally solved via the method of Lagrange multipliers. The posture/movement problem can be tackled via the separation principle which, derived from experimental evidence, posits that the brain processes static torques (i.e. posture-dependent, such as gravitational torques) separately from dynamic torques (i.e. velocity-dependent). The separation principle has traditionally been applied at a joint torque level. Our main contribution is to apply the separation principle to Lagrange multipliers, which act as task-space force fields, leading to a task-space separation principle. In this way, we can separate postural control (implementing Donders' law) from various types of tasks-space movement planners. As an example, the proposed framework is applied to the (redundant) task of pointing with the human wrist. Nonlinear inverse optimization (NIO) is used to fit the model parameters and to capture motor strategies displayed by six human subjects during pointing tasks. The novelty of our NIO approach is that (i) the fitted motor strategy, rather than raw data, is used to filter and down-sample human behaviours; (ii) our framework is used to efficiently simulate model behaviour iteratively, until it converges towards the experimental human strategies.

Quantifying control effort of biological and technical movements: an information-entropy-based approach

In biomechanics and biorobotics, muscles are often associated with reduced movement control effort and simplified control compared to technical actuators. This is based on evidence that the nonlinear muscle properties positively influence movement control. It is, however, open how to quantify the simplicity aspect of control effort and compare it between systems. Physical measures, such as energy consumption, stability, or jerk, have already been applied to compare biological and technical systems. Here a physical measure of control effort based on information entropy is presented. The idea is that control is simpler if a specific movement is generated with less processed sensor information, depending on the control scheme and the physical properties of the systems being compared. By calculating the Shannon information entropy of all sensor signals required for control, an information cost function can be formulated allowing the comparison of models of biological and technical control systems. Exemplarily applied to (bio-)mechanical models of hopping, the method reveals that the required information for generating hopping with a muscle driven by a simple reflex control scheme is only I=32 bits versus I=660 bits with a DC motor and a proportional differential controller. This approach to quantifying control effort captures the simplicity of a control scheme and can be used to compare completely different actuators and control approaches.

Inertia Matching Manipulability and Load Matching Optimization for Humanoid Jumping Robot

Human jumping motion includes stance phase, flight phase and landing impact phase. Jumping robot belongs to a variable constraints system because every phase has different constraint conditions. An unified dynamics equation during stance phase and flight phase is established based on floated-basis space. Inertia matching is used to analyze actuator/gear systems and select the optimum gear ratio based on the transmission performance between the torque produced at the actuator and the torque applied to the load. Load matching is an important index which affects jumping performance and reflects the capability of supporting a weight or mass. It also affects the distributing of the center of gravity (COG). Regarding jumping robot as a redundant manipulator with a load at end-effector, inertia matching can be applied to optimize load matching for jumping robot. Inertia matching manipulability and directional manipulability are easy to analyze and optimize the load matching parameters. A 5th order polynomial function is defined to plan COG trajectory of jumping motion, taking into account the constraint conditions of both velocity and acceleration. Finally, the numerical simulation of vertical jumping and experimental results show inertia matching is in direct proportion to jumping height, and inertia matching manipulability is a valid method to load matching optimization and conceptual design of robot.

An impact dynamics model and sequential optimization to generate impact motions for a humanoid robot

When a human needs to generate a large force, they will try to apply an impulsive force with dynamic cooperation of the whole body. In this paper we first discuss impact dynamics of humanoid robots and then propose a way to generate impact motions for a humanoid robot to exert a large force while keeping a balance. In the impact motion generation, Sequential Quadratic Programming (SQP) is used to solve a non-linear programming problem in which an objective function and constraints may be non-linear functions of the motion parameters. Impact motions are generated using SQP so that the impact force is maximized while the angular momentum is minimized. Breaking wooden boards with a Karate chop is taken as a case study because it is a typical example of tasks that utilize impulsive force. A humanoid robot motion for the Karate chop is generated by the proposed method. In order to validate the designed motion, experiments are carried out using a small humanoid robot Fujitsu HOAP-2. The Karate-chop motion generated by the proposed method is compared with the motion designed by a human. The results of breaking the wooden boards experiments clearly show the effectiveness of the proposed method.

Optimization and learning for rough terrain legged locomotion

We present a novel approach to legged locomotion over rough terrain that is thoroughly rooted in optimization. This approach relies on a hierarchy of fast, anytime algorithms to plan a set of footholds, along with the dynamic body motions required to execute them. Components within the planning framework coordinate to exchange plans, cost-to-go estimates, and ‘certificates’ that ensure the output of an abstract high-level planner can be realized by lower layers of the hierarchy. The burden of careful engineering of cost functions to achieve desired performance is substantially mitigated by a simple inverse optimal control technique. Robustness is achieved by real-time re-planning of the full trajectory, augmented by reflexes and feedback control. We demonstrate the successful application of our approach in guiding the LittleDog quadruped robot over a variety of types of rough terrain. Other novel aspects of our past research efforts include a variety of pioneering inverse optimal control techniques as well as a system for planning using arbitrary pre-recorded robot behavior.

Motion Planning for Legged Robots on Varied Terrain

In this paper we study the quasi-static motion of large legged robots that have many degrees of freedom. While gaited walking may suffice on easy ground, rough and steep terrain requires unique sequences of footsteps and postural adjustments specifically adapted to the terrain's local geometric and physical properties. In this paper we present a planner that computes these motions by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls. To improve motion quality, the probabilistic planner derives its sampling strategy from a small set of motion primitives that have been generated offline. The viability of this approach is demonstrated in simulation for the six-legged Lunar vehicle ATHLETE and the humanoid HRP-2 on several example terrains, including one that requires both hand and foot contacts and another that requires rappelling.