Using a delta-manipulator for stabilisation of an end-effector to perform precise spatial positioning is a current area of interest in aerial manipulation. High speed precision movements of a manipulator can cause disturbances to the aerial platform, which hinders trajectory tracking and in some cases could be sufficient to cause a loss of control of the vehicle. In this paper, a statically balanced delta aerial manipulator is developed and evaluated. The system is balanced using three counter-masses to reduce the force imparted onto the base and thus reduce perturbations to the movement of the drone. The system is thoroughly tested following trajectories while mounted to a force sensor and while on-board an aerial vehicle. Results show that the forces transmitted to the base in all axes are reduced considerably, however improvements in overall flight accuracy are not observed in aerial settings. Design lessons to make a balanced delta-manipulator viable for practical implementation on an aerial vehicle are discussed in depth. A video summarising the flight testing results is available at https://youtu.be/fXKnosnVKCk.

Mechanical intelligence is the use of mechanical and other physical properties to create robotic systems adaptable to new external situations using simple control schemes. Designs of robot hands have successfully been developed and optimised following this principle to produce self-adaptive and versatile power grasps via implementations based on underactuated fingers, elastic components, and open-loop motor control. However, these characteristics, and mechanical-intelligent strategies in general, have been seldom leveraged for precision grasping. This paper proposes a mechanical-intelligent technique to facilitate not only spiral caging power grasp, but also self-adaptive precision grasp with error tolerance. This approach is exemplified by the rigorous analysis, development, and testing of a novel three-fingered, two-actuator, underactuated robot hand, called the helical hand, which is capable of self-adaptive precision grasping, and of generating spiral helical power grasps of unknown objects by simply setting two actuators at a constant speed.

The Jacobian matrix is a highly popular tool for the control and performance analysis of closed-loop robots. Its usefulness in parallel mechanisms is certainly apparent, and its application to solve motion planning problems, or other higher level questions, has been seldom queried, or limited to nonredundant systems. In this article, we discuss the shortcomings of the use of the Jacobian matrix under redundancy, in particular when applied to kinematically redundant parallel architectures with non-serially connected actuators. These architectures have become fairly popular recently as they allow the end-effector to achieve full rotations, which is an impossible task with traditional topologies.The problems with the Jacobian matrix in these novel systems arise from the need to eliminate redundant variables forming it, resulting in both situations where the Jacobian incorrectly identifies singularities (false positive), and where it fails to identify singularities (false negative). These issues have, thus far, remained unaddressed in the literature. We highlight these limitations herein by demonstrating several cases using numerical examples of both planar and spatial architectures.