Continuum manipulators are a rapidly emerging class of robots. However, due to the complexity of their mathematical models and modeling inaccuracies, the development of effective control systems is a particularly challenging task. This paper presents the first attempt on kinematic control of continuum manipulators using a fuzzy-model-based approach. A fuzzy controller is proposed for autonomous execution of end-effector trajectory tracking tasks for a continuum manipulator. Particularly, membership functions are employed to combine the linearized state-space models, to achieve, overall, a fuzzy model. The fuzzy model can help design the fuzzy controller; in our approach, this process is supported by a thorough stability analysis. This control methodology enables a solution with low computational requirements to this motion control problem - there is no need to continuously update the Jacobian of the continuum manipulator. The superior performance of this controller is validated in MATLAB simulations and compared with those of classical controllers found in the literature. The experiments on a rapid-prototyped continuum manipulator further verify the feasibility and the advantages of this fuzzy controller in the presence of modeling discrepancies and hardware inaccuracies.

Plant-inspired inflatable eversion robots with their tip growing behaviour have recently emerged. Because they extend from the tip, eversion robots are particularly suitable for applications that require reaching into remote places through narrow openings. Besides, they can vary their structural stiffness. Despite these essential properties which make the eversion robot a promising candidate for applications involving cluttered environments and tight spaces, controlling their motion especially laterally has not been investigated in depth. In this paper, we present a new approach based on model-based kinematics to control the eversion robot's tip position and orientation. Our control approach is based on Euler-Bernoulli beam theory which takes into account the effect of the internal inflation pressure to model each robot bending segment for various conditions of structural stiffness. We determined the parameters of our bending model by performing a least-square technique based on the pressure-bending data acquired from an experimental study. The model is then used to develop a pose controller for the tip of our eversion robot. Experimental results show that the proposed control strategy is capable of guiding the tip of the eversion robot to reach a desired position and orientation whilst varying its structural stiffness.

Various methods based on hyperelastic assumptions have been developed to address the mathematical complexities of modeling motion and deformation of continuum manipulators. In this study, we propose a quasistatic approach for 3D modeling and real-time simulation of a pneumatically actuated soft continuum robotic appendage to estimate the contact force and overall pose. Our model can incorporate external load at any arbitrary point on the body and deliver positional and force propagation information along the entire backbone. In line with the proposed model, the effectiveness of elasticity versus hyperelasticity assumptions (neo-Hookean and Gent) is investigated and compared. Experiments are carried out with and without external load, and simulations are validated across a range of Young's moduli. Results show best conformity with Hooke's model for limited strains with about 6% average normalized error of position; and a mean absolute error of less than 0.08 N for force applied at the tip and on the body, demonstrating high accuracy in estimating the position and the contact force.

In this paper, a potential-field-based real-time path planning algorithm for a multi-segment continuum manipulator is proposed. This planner is employed to enable a continuum-style manipulator to move autonomously in dynamic environments in real-time. The classic potential field method is modified to make it applicable for a kinematics model based on the constant-curvature assumption. The contribution of this paper lies in the design of a novel potential field in the actuator space satisfying the mechanical constraints of the manipulator. The planning algorithm is tested and validated in real-time simulation for a 3 segments continuum manipulator. Preliminary tests for a tendon-driven single-segment continuum manipulator prototype confirm the performance of the proposed planner.

In this paper, we present a novel pose estimation and obstacle avoidance approach for tendon-driven multi-segment continuum manipulators moving in dynamic environments. A novel multi-stage implementation of an Extended Kalman Filter is used to estimate the pose of every point along the manipulator's body using only the position information of each segment tip. Combined with a potential field, the overall algorithm will guide the manipulator tip to a desired target location and, at the same time, keep the manipulator body safe from collisions with obstacles. The results show that the approach works well in a real-time simulation environment that contains moving obstacles in the vicinity of the manipulator.

Potential-function-based control strategy for path planning and formation control of Quadrotors is proposed in this work. The potential function is used to attract the Quadrotor to the goal location as well as avoiding the obstacle. The algorithm to solve the so called local minima problem by utilizing the wall-following behavior is also explained. The resulted path planning via potential function strategy is then used to design formation control algorithm. Using the hybrid virtual leader and behavioral approach schema, the formation control strategy by means of potential function is proposed. The overall strategy has been successfully applied to the Quadrotor's model of Parrot AR Drone 2.0 in Gazebo simulator programmed using Robot Operating System.