Robotics

An extended operational space kinematics and dynamics formulation is presented for the control of redundant non-serial compound robotic manipulators. A broad spectrum of high-load-capacity non-serial manipulators used in earth moving, material handling, and construction applications is addressed. Departing from conventional approaches that rely on Jacobian pseudoinverses and local null-space projections, a globally valid, differential-geometry-based, multi-valued inverse kinematic mapping is defined at the configuration level, with the explicit self-motion parameterization of manipulator redundancy. The formulation yields coupled second-order ordinary differential equations of manipulator dynamics on the product space of task variables and self-motion coordinates. This enables the direct integration of system dynamics with control strategies, such as model predictive control or feedback design, while maintaining task constraint compliance. The methods presented are validated through the simulation and control of a non-serial compound material loader manipulator with multiple degrees of redundancy, demonstrating advantages in generality, numerical accuracy, and trajectory smoothness.

This paper presents the development and experimental evaluation of the Athena parallel robot, a novel system designed for robot-assisted pancreatic surgery. The development of the experimental model based on the kinematic scheme, including the command and control system (hardware and software), the calibration procedure, and the performance measurements of the experimental model based on finite element analyses of the 3D model, are also detailed in this paper. Based on these finite element analyses, a region of the robot that introduces clearance during the operation of the experimental model is found. The paper also presents the methodology used for mapping the robot’s workspace with an optical system, which enabled improvements to ensure coverage of the entire pancreas area. The results obtained before and after the mechanical improvements are presented, demonstrating a reduction in clearance by up to 4.1 times following part replacement, as well as a workspace extension that enables the active instrument to reach the entire pancreatic region.

This paper presents an optimization framework for Multi-Robot Task Allocation (MRTA) for a heterogeneous robot fleet operating in dynamic, failure-prone environments. In contrast to traditional MRTA approaches that handle only the initial allocation, our system extends functionality by integrating real-time crisis response and intelligent task recovery from failure points. The framework combines island model genetic algorithm-based initial optimization with an event-driven architecture for handling robot failures during mission execution. Our key contribution is the integration of crisis-aware capabilities with the island model paradigm, enabling task resumption from failure points and dynamic reoptimization, while preserving the diversity benefits of multi-population evolution. When a robot fails, the system intelligently substitutes replacement robots and resumes interrupted tasks from their exact failure point, rather than restarting from the beginning. This significantly improves mission efficiency and resilience. We introduce a temporal scheduling mechanism that tracks actual task execution states and calculates remaining work upon failure, enabling true task continuation. Experimental validation across 57 diverse scenarios with 2340 independent runs demonstrates that the island model achieves higher fitness scores, maintains greater population diversity, exhibits more consistent performance, and recovers faster from crisis events compared to the standard single-population genetic algorithm.