In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. The IK problem is addressed as the optimization of the end-effector position and orientation based on the forward kinematics model of the systems which is built using the Denavit–Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges such as singularities, which may simply harm the existence of an inverse expression. Experimental investigations included the capacity of the proposal to handle random single points in the workspace and also a circular path planning with a specific orientation. The comparative analysis showed that the mono-objective m-PSO is better than the classical PSO, the CSA, and SSA. The multi-objective variants returned accurate results, fair and better solutions compared to multi-objective variants of MO-PSO, MO-JAYA algorithm, and MO-CSA. Even if the proposed method were applied to solve the inverse kinematics of and educational robotics arms for a single point as well as for a geometric shape, it may be transposed to solve related industrial robotized arms withthe only condition of having their forward kinematics model.

The bat algorithm (BA) is a nature inspired algorithm which is mimicking the bio-sensing characteristics of bats, known as echolocation. This paper suggests a Bat-based meta-heuristic for the inverse kinematics problem of a robotic arm. An intrinsically modified BA is proposed to find an inverse kinematics (IK) solution, respecting a minimum variation of the joints’ elongation from the initial configuration of the robot manipulator to the proposed new pause position. The proposed method is called IK-BA, it stands for a specific bat algorithm dedicated to robotic-arms’ inverse geometric solution, and where the elongation control mechanism is embedded in bat agents update equations. Performances analysis and comparatives to related state of art meta-heuristics solvers showed the effectiveness of the proposed IK bat solver for single point IK planning as well as for geometric path planning, which may have several industrial applications. IK-BA was also applied to a real robotic arm with a spherical wrist as a proof of concept and pertinence of the proposed approach.

The geometry of soft actuators significantly impacts their performance, including force generation, range of motion, and adaptability. Optimizing actuator geometry and material properties under specific constraints is crucial for achieving desired performance. This paper presents an optimization workflow employing metaheuristic algorithms in synergy with SolidWorks and Sorotoki, a newly developed MATLAB toolkit for soft robotics. The workflow optimizes actuator geometry to maximize bending while minimizing actuating pressure. A metaheuristic algorithm iteratively modifies the actuator’s design in SolidWorks, according to finite element analysis conducted using Sorotoki. To ensure accurate simulations, a uniaxial tensile test is performed on Thermoplastic Polyurethane (TPU), with curve fitting based on metaheuristic algorithms for precise hyperelastic modeling. The Ogden and Yeoh models are compared, with results indicating the Ogden model best represents TPU behavior. Four metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm, Simulated Annealing, and Moth Flame Optimization (MFO)—are evaluated. PSO outperforms others in material modeling, while MFO yields the most effective actuator geometry. This workflow enables the design of more efficient and adaptable soft actuators for applications in robotics, prosthetics, and biomedical devices.