Neural Dynamics for Cooperative Control of Redundant Robot Manipulators

Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach

“…Similarly, Fig. 4e shows the minimum distance of any link of the manipulator from the obstacle as defined in (7). A high value is desirable because it reduces the risk of collision in case of uncertainty in obstacle position or error in the manipulator model.…”

“…Accurate tracking control, along with obstacle avoidance, is a critical requirement for the industrial manipulators [5], [6]. To fulfill those requirements, redundant manipulators [7] are particularly desirable because the extra degree of freedoms (DOFs) provided by redundant joints helps in achieving secondary design objectives, such as obstacle avoidance [8]- [10]. It is well-known in the literature that the tracking control and obstacle avoidance in itself are challenging problems [11].…”

“…Kinematic tracking control of a redundant robotic manipulators is a well-studied problem in robotic literature [7], [10], [19]. For example, consider an industrial manipulator, assigned to move an object from one point to another by following a specified trajectory in the cartesian task-space.…”

Blockchain Technology with Applications to Distributed Control and Cooperative Robotics: A Survey

“…They can help in gathering information, data or other services to its acquirer. Some other worth mentioning practical applications from our past research work includes; distributed task allocation of multiple robots: A control perspective [52], decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks [61], cooperative distributed source seeking by multiple robots [62], Formation control and tracking for co-operative robots with non-holonomic constraints [63], multi-robot cooperative control for monitoring and tracking dynamic plumes [64], distributed recurrent neural networks for cooperative control of manipulator [65], decentralized control of collaborative redundant manipulators with partial command coverage via locally connected recurrent neural networks [66], distributed source seeking by cooperative robots: All-to-all and limited communications [67], cooperative motion generation in a distributed network of redundant robot manipulators with noises [68], and neural dynamics for cooperative control of redundant robot manipulators [69].…”

A Fault-Tolerant Method for Motion Planning of Industrial Redundant Manipulator

“…To detect which joint transfer to fault state and simultaneously carry on the accurate motion planning and control may more applicable and useful for long-term heavy industrial process. As discussed previously, among the existing methods for dealing with motion planning and control of redundant manipulators [11], [13], [24], [25], [26], [14], [27], most of approaches deal with motion analysis and resolution of redundant manipulators without joint failures considered. However, currently there might be almost no related work on simultaneous fault-tolerant motion planning and faultdiagnose method on redundancy motion control of redundant manipulators.…”