Abstract
The adoption of robotic manipulators in industry has improved productivity, precision, and safety, yet their use in high-force tasks such as assembly and machining remains limited due to conservative, configuration-independent Cartesian force limits. Since the mapping between joint torques and Cartesian forces is highly non-linear and configuration dependent, this work proposes a joint-space framework for evaluating and optimising task execution regions within the robot workspace. Given prescribed forces and moments, a genetic algorithm is used to minimise joint torques while explicitly accounting for actuator limits, kinematic and dynamic constraints, and obstacle avoidance. Simulation results from representative use cases demonstrate the effectiveness of the proposed approach in identifying feasible task placements and enhancing the robot’s force and moment generation capabilities beyond nominal limits.