Table 9.

Selected examples of MEMS-based sensors for object griping and recognition on various robotic platforms.

MEMS Elements	Operation Principle	Robotic Platform	Touch/Grip Object Type	Sensitivity	Reference
Optical fiber	Bragg’s grating	Four finger gripper	Metal, rubber, plastic	139 nm/N	[186]
Beam deformation strain gauge	Wheatstone bridge	Manufacturing robotic arm	None/Torque sensor	1–3 mV/Nm	[187]
Optical/magnetic	Retroreflective markers/Electromagnetic field	Two finger gripper	3D printed plastics	0.01/0.6 mm/deg.	[188]
Resistive sensors	Conductivity changes	Master-slave robotic hand system	Plastics and metals	0.1 N	[189]
Graphene/Nanosilver electrodes	Nanoparticle/elastomer composite resistance change	Humanoid robotic hand	Ceramics, plastics	1.32–3.40% kPa−1	[123]
Capacitive/pneumatic	pneumatic deformation sensing	Two finger gripper	3D printed soft plastics	0.03 N	[190]
Resistive/magnetic	Capacitance/magnet displacement	Two finger gripper	Metal, wood plastic	n/a	[191]
Resistive	Nanoparticle/elastomer composite resistance change	YuMi robot	Skin-like soft rubbers	18.83% N−1	[192]
Magnetic/barometric	Liquid metal sensing/electrical resistance	Two finger gripper	Plastic objects	85% accuracy	[193]
Resistive	Conductive foam compression	Two finger gripper	Metals, rubber, wood	1.196%/°C and 13.29%/kPa	[194]
Resistive	Resistance change under pressure	anthropomorphic artificial hand	Rigid objects	0.47, 0.45, 0.16 mV/mN for the x-, y- and z-directions	[195]
Tribolectric nanogenerator	Electrostatic induction	Three finger gripper	Plastic, fruits, aluminum, paper	98.1% accuracy	[196]
Resistive	Resistance variation upon compression	Soft robotic hand	100 objects of all sorts	94% basic grasping, 50–80% identification-grasping	[197]