Dexterous Grasping by Manipulability Selection for Mobile Manipulator With Visual Guidance

Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning

“…In [4,20], the variable stiffness profile in task space is computed partially based on the measured forces through an external high-accuracy force sensor mounted on the robot end-effector, thus increasing the cost of the HRI system. Compared with these methods, the sEMG-based human-torobot impedance transfer has the following advantages: i) the cost is no longer a problem since the human limb sEMG signals can usually be collected via some cheap devices (e.g., MYO Armhand); ii) the human limb stiffness profiles can be extracted in a real time manner, guaranteeing the efficiency of the realization of the variable impedance control [23]; and iii) in this way, most importantly, the human factors (e.g., flexibility and adaptability) are taken into account within the process of the human-to-robot skill transfer, which can facilitate the interactions between humans and robots [24] and robotic dexterous manipulations [25].…”

Quadrupedal Robots Whole-Body Motion Control Based on Centroidal Momentum Dynamics

“…An over-constrained situation is the result of a short four contact points support phase, which provides compliant motions and stable walking over rough terrain. When the robot is in over-constrained situation, there are many ways to adapt the robot motion behavior to track the motion task [37]. In this paper, with all kinds of constraints are satisfied, the highest priority was assigned to track the desired rate of change of centroidal momentum.…”

Internet of Robotic Things in Smart Domains: Applications and Challenges

“…As the ability to perform self-localization and navigating are of crucial importance for several industrial robots, path planning is currently a research challenge in the manufacturing domain [ 51 , 55 , 56 ], and different algorithms are being studied to resolve this issue, such as Bellman–Ford [ 63 ], Dijkstra [ 64 ], and Floyd Warshall [ 65 ] algorithms. Furthermore, industrial manipulators provided with visual guidance can further enhance path planning systems, as a vision-based approach gives more awareness to detect and avoid obstacles [ 50 , 58 ].…”