Yanjiang Huang

Selection of manipulator system for multiple-goal task by evaluating task completion time and cost with computational time constraints

The focus of this study is on the problem of manipulator system selection for a multiple-goal task by evaluating task completion time and cost with computational time constraints. An approach integrating system selection, structural configuration design, layout design, motion planning, and relative cost calculation is proposed to solve this problem within a reasonable computational time. In the proposed approach, multiple-objective particle swarm optimization (MOPSO) is utilized to search for the appropriate manipulator system with appropriate structural configuration from a set of candidate systems. Particle swarm optimization (PSO) and the nearest neighborhood algorithm are employed in layout design and motion planning due to their high convergence speed. Three methods involving a random search algorithm are compared to the proposed approach through a simulation. The simulation is done with a set of tasks and the result shows the effectiveness of the proposed approach.

Robust multi-robot coordination in pick-and-place tasks based on part-dispatching rules

This paper addresses the problem of realizing multi-robot coordination that is robust against pattern variation in a pick-and-place task. To improve productivity and reduce the number of parts remaining on the conveyor, a robust and appropriate part flow and multi-robot coordinate strategy are needed. We therefore propose combining part-dispatching rules to coordinate robots, by integrating a greedy randomized adaptive search procedure (GRASP) and a Monte Carlo strategy (MCS). GRASP is used to search for the appropriate combination of part-dispatching rules, and MCS is used to estimate the minimum-maximal part flow for one combination of part-dispatching rules. The part-dispatching rule of first-in-first-out is used to control the final robot in the multi-robot system to pick up parts left by other robots, and the part-dispatching rule of shortest processing time is used to make the other robots pick up as many parts as possible. By comparing it with non-cooperative game theory, we verify that the appropriate combination of part-dispatching rules is effective in improving the productivity of a multi-robot system. By comparing it with a genetic algorithm, we also verify that MCS is effective in estimating minimum-maximal part flow. The task-completion success rate derived via the proposed method reached 99.4% for 10,000 patterns. Propose combination of part-dispatching rules to coordinate multi-robot system.Pattern variation in a pick-and-place task is taken into account.Achieve an appropriate part flow and combination of part-dispatching rules.Integrate a greedy randomized adaptive search procedure with a Monte Carlo strategy.

Part dispatching rule-based multi-robot coordination in pick-and-place task

In this paper, the problem of multi-robot coordination for a robot-conveyor system in a pick-and-place task is addressed. To improve the productivity of a robot-conveyor system, the combination of part dispatching rules is utilized to coordinate the action of robots. The greedy randomized adaptive search procedure (GRASP) is utilized to search for the optimal combination of part dispatching rules from a set of candidate part dispatching rules. The proposed method is verified to be effective and practical through a set of tasks in a simulation.

Manipulator system selection based on evaluation of task completion time and cost

Task completion time and cost are two significant criteria for the selection of manipulator system. For a given task, several Pareto solutions of manipulator systems should be derived based on the evaluation of these two criteria. However, this process requires a large calculation time. In this paper, we propose a method that can select appropriate systems by evaluating task completion time and cost within the desired calculation time. In the proposed method, multiple objective particle swarm optimization (MOPSO) is employed to search for appropriate manipulator systems from a set of candidate systems. Location optimization and motion coordination are integrated to derive the task completion time and the relative cost is used to evaluate the cost of a manipulator system. We employ particle swarm optimization (PSO) for location optimization and use nearest-neighborhood algorithm (NNA) for motion coordination, since PSO and NNA have a high speed of convergence to a good solution. The proposed method is applied to a set of tasks and is proved to be effective and practical.

Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection

This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods.

Peg-in-Hole Assembly Based on Two-phase Scheme and F/T Sensor for Dual-arm Robot

This paper focuses on peg-in-hole assembly based on a two-phase scheme and force/torque sensor (F/T sensor) for a compliant dual-arm robot, the Baxter robot. The coordinated operations of human beings in assembly applications are applied to the behaviors of the robot. A two-phase assembly scheme is proposed to overcome the inaccurate positioning of the compliant dual-arm robot. The position and orientation of assembly pieces are adjusted respectively in an active compliant manner according to the forces and torques derived by a six degrees-of-freedom (6-DOF) F/T sensor. Experiments are conducted to verify the effectiveness and efficiency of the proposed assembly scheme. The performances of the dual-arm robot are consistent with those of human beings in the peg-in-hole assembly process. The peg and hole with 0.5 mm clearance for round pieces and square pieces can be assembled successfully.

Integrated design of multi-robot system for pick-and-place tasks

In the application of multi-robot system for a pick-and-place task, the appropriate robot arms and their base positions should be rapidly selected to improve the productivity, reduce the purchase cost, and apply the system for a task quickly. However, in current factories, a large amount of computational time is required to select the appropriate robot arms and their base positions because they are selected by the experienced engineers through evaluating the performance index in several trials. In this paper, we call the selection of robot arms and their base positions as integrated design of multi-robot system and propose a method to rapidly realize the integrated design of multi-robot system. We use the multi-objective particle swarm optimization (MOPSO) to select an appropriate multi-robot system, use the particle swarm optimization (PSO) to search for the base positions of the robot arms, and use the M/M/1 queueing model with impatient customer to estimate the performance index. A simulation proves that the proposed method is effective and efficient in comparison to a comparative method that uses simulation-based statistical inference to estimate the performance index. The computational time for the proposed method is 0.48 hour, which is less than 1/20 of the computational time for the comparative method.

Teaching-playback robot manipulator system in consideration of singularities

This paper studies the kinematic singularities problem faced by a 6-DOF manipulator controlled manually by using teaching pendant in real time. Two algorithms of singularities avoidance are discussed, which include non-redundancy and redundancy. Experimental case studies are developed to investigate manipulator performance when end-effector trajectories come near to shoulder and wrist singularities that are controlled manually by user. Proposed algorithms are verified to be effective and practical through experiment.

Vision-guided peg-in-hole assembly by Baxter robot:

Dual-arm robot Baxter has been utilized in many applications. To use the Baxter robot realize a peg-in-hole assembly task, we proposed a step-by-step grasping strategy and a vision-guidance method for the Baxter robot. The step-by-step grasping strategy was used to position and grasp a peg and a hole on the working platform. At the assembling process, the positioning error was derived by the camera attached on the robot end-effector, and the positioning error was compensated through proportional–integral–derivative controller. Experiments were conducted to evaluate the grasping strategy and the vision-guidance method. Experimental results show that the Baxter robot can grasp the peg and the hole from the working platform, and the peg in hole with 1 mm clearance can be assembled successfully.

Stance control model in consideration of feed-forward control by reticulospinal tract

This paper aims to investigate the function of constant feed-forward control from the reticulospinal tract (RST) on improving posture stability during standing from the viewpoint of ability to countering the disturbances. We presented a stance control model considering not only the balance control, a PD controller, from vestibular tract based on vestibular feedback but also the constant feed-forward control ur by reticulospinal tract. Parameters of PD controller, max muscle isometric force of back extensors and flexors and the constant strength of control from RST were optimized during a 3s forward dynamics simulation and the optimal ur was obtained. Then, we fixed the value of ur around the value of optimal one and only optimized other four parameters. After that, the abilities of countering the platform disturbance of the musculoskeletal (MSK) model under the control of different ur were investigated. As a result, we found that optimal ur would improve the posture stability and make MSK model more adaptive to the disturbance.