Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Connectionist robot motion planning: a neurally-inspired approach to visually-guided reaching
Connectionist robot motion planning: a neurally-inspired approach to visually-guided reaching
Integrable solutions of kinematic redundancy via impedance control
International Journal of Robotics Research
A random sampling scheme for path planning
International Journal of Robotics Research
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Spikes: exploring the neural code
Spikes: exploring the neural code
A Learning Approach to Fixating on 3D Targets with Active Cameras
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Neural network models of motor timing and coordination
Neural network models of motor timing and coordination
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
A self-organizing neural model of motor equivalent reaching and tool use by a multijoint arm
Journal of Cognitive Neuroscience
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
IEEE Transactions on Robotics
IEEE Transactions on Neural Networks
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This paper describes a redundant robot arm that is capable of learning to reach for targets in space in a self-organized fashion while avoiding obstacles. Self-generated movement commands that activate correlated visual, spatial and motor information are used to learn forward and inverse kinematic control models while moving in obstacle-free space using the Direction-to-Rotation Transform (DIRECT). Unlike prior DIRECT models, the learning process in this work was realized using an online Fuzzy ARTMAP learning algorithm. The DIRECT-based kinematic controller is fault tolerant and can handle a wide range of perturbations such as joint locking and the use of tools despite not having experienced them during learning. The DIRECT model was extended based on a novel reactive obstacle avoidance direction (DIRECT-ROAD) model to enable redundant robots to avoid obstacles in environments with simple obstacle configurations. However, certain configurations of obstacles in the environment prevented the robot from reaching the target with purely reactive obstacle avoidance. To address this complexity, a self-organized process of mental rehearsals of movements was modeled, inspired by human and animal experiments on reaching, to generate plans for movement execution using DIRECT-ROAD in complex environments. These mental rehearsals or plans are self-generated by using the Fuzzy ARTMAP algorithm to retrieve multiple solutions for reaching each target while accounting for all the obstacles in its environment. The key aspects of the proposed novel controller were illustrated first using simple examples. Experiments were then performed on real robot platforms to demonstrate successful obstacle avoidance during reaching tasks in real-world environments.