In this article, a new method is proposed to help the mobile robot to avoid many kinds of collisions effectively, which combined past experience with modified artificial potential field method. In the process of the actual global obstacle avoidance, system will invoke case-based reasoning algorithm using its past experience to achieve obstacle avoidance when obstacles are recognized as known type; otherwise, it will invoke the modified artificial potential field method to solve the current problem and the new case will also be retained into the case base. In case-based reasoning, we innovatively consider that all the complex obstacles are retrieved by two kinds of basic build-in obstacle models (linear obstacle and angle-type obstacle). Our proposed experience mixing with modified artificial potential field method algorithm has been simulated in MATLAB and implemented on actual mobile robot platform successfully. The result shows that the proposed method is applicable to the dynamic real-time obstacle avoidance under unknown and unstructured environment and greatly improved the performances of robot path planning not only to reduce the time consumption but also to shorten the moving distance.
In this paper, we design an object sorting robot system, which is based on robot operating system distributed processing framework. This system can communicate with human beings; can percept the 3-D environment by Kinect sensor; has the ability of reasoning; can transfer the natural language intention to machine instruction to control the movement of manipulator. In particular, in order to improve the intelligence and usability of our robot, we propose a human-robot-environment interactive reasoning mechanism. In our method, a "dialogue and 3-D scene interaction module" is added into the traditional case-based reasoning-belief-desireintention mechanism. Our proposed mechanism not only realizes the traditional function of map matching but also achieves the function of desire analysis and guidance. When the user's desire is incomplete and/or mismatched with the actual scene, our robot will take the initiative to guide users through dialogue, and the user's input information will be used to replenish the user's desire. Experimental results prove the advantages of our mechanism.Index Terms-Belief-desire-intention (BDI) mechanism, casebased reasoning (CBR) mechanism, human-robot-environment interaction, object sorting robot, reasoning mechanism.
I. INTRODUCTIONM ANY new kinds of intelligent service robot systems are emerging in these days. These systems have a common characteristic that the robot could collaborate with humans to accomplish tasks in an unstructured environment with a flexible manipulator. Such a system needs to be capable of environmental perception, human-robot interaction (HRI), target recognition, knowledge reasoning, trajectory planning and grabbing, etc. Among the requirements, the reasoning mechanism is the core of the entire system, the brain of an intelligent robot that determines the degree of that intelligence.The classic reasoning mechanisms include rule-based reasoning (RBR), procedural reasoning system, knowledge-based reasoning, and case-based reasoning (CBR). Among them,
It is a necessary mean to realize the accurate motion control of the manipulator which uses end-effector pose correction method and compensation method. In this article, first, we established the kinematic model and error model of the modular manipulator (WUST-ARM), and then we discussed the measurement methods and precision of the inertial measurement unit sensor. The inertial measurement unit sensor is mounted on the end-effector of modular manipulator, to get the real-time pose of the end-effector. At last, a new inertial measurement unit-based iterative pose compensation algorithm is proposed. By applying this algorithm in the pose compensation experiment of modular manipulator which is composed of low-cost rotation joints, the results show that the inertial measurement unit can obtain a higher precision when in static state; it will accurately feedback to the control system with an accurate error compensation angle after a brief delay when the end-effector moves to the target point, and after compensation, the precision errors of roll angle, pitch angle, and yaw angle are reached at 0.05°, 0.01°, and 0.27°respectively. It proves that this low-cost method provides a new solution to improve the end-effector pose of low-cost modular manipulator.
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