In this paper, we present a recent survey on robotic grippers. In many cases, modern grippers outperform their older counterparts which are now stronger, more repeatable, and faster. Technological advancements have also attributed to the development of gripping various objects. This includes soft fabrics, microelectromechanical systems, and synthetic sheets. In addition, newer materials are being used to improve functionality of grippers, which include piezoelectric, shape memory alloys, smart fluids, carbon fiber, and many more. This paper covers the very first robotic gripper to the newest developments in grasping methods. Unlike other survey papers, we focus on the applications of robotic grippers in industrial, medical, for fragile objects and soft fabrics grippers. We report on new advancements on grasping mechanisms and discuss their behavior for different purposes. Finally, we present the future trends of grippers in terms of flexibility and performance and their vital applications in emerging areas of robotic surgery, industrial assembly, space exploration, and micromanipulation. These advancements will provide a future outlook on the new trends in robotic grippers.

This paper presents a new conflict resolution methodology for multiple mobile robots while ensuring their motion-liveness, especially for cluttered and dynamic environments. Our method constructs a mathematical formulation in a form of an optimization problem by minimizing the overall travel times of the robots subject to resolving all the conflicts in their motion. This optimization problem can be easily solved through coordinating only the robots' speeds. To overcome the computational cost in executing the algorithm for very cluttered environments, we develop an innovative method through clustering the environment into independent subproblems that can be solved using parallel programming techniques. We demonstrate the scalability of our approach through performing extensive simulations. Simulation results showed that our proposed method is capable of resolving the conflicts of 100 robots in less than 1.23 s in a cluttered environment that has 4357 intersections in the paths of the robots. We also developed an experimental testbed and demonstrated that our approach can be implemented in real time. We finally compared our approach with other existing methods in the literature both quantitatively and qualitatively. This comparison shows while our approach is mathematically sound, it is more computationally efficient, scalable for very large number of robots, and guarantees the live and smooth motion of robots.

For safe and efficient navigation of heterogeneous multiple mobile robots (HMRs), it is essential to incorporate dynamics (mass and inertia) in motion control algorithms. Many methods rely only on kinematics or point-mass models, resulting in conservative results or occasionally failure. This is especially true for robots with different masses. In this article, we develop a novel navigation methodology for a distributed scheme by incorporating the robots’ dynamics through calculating the time to collision (TTC) and designing a new controller accordingly that avoids collisions. We first propose a new predictive collision term by TTC that will be used to quantify imminent collisions among HMRs. Subsequently, using this term, we develop a novel nonlinear controller that explicitly incorporates TTC in the design and guarantees collision-free motion. Simulations and experiments were performed to demonstrate the effectiveness of the developed methods. We first compared the results of our proposed approach with controllers that only consider the robots’ kinematics. It was shown that the proposed control strategy (a TTC-based controller) proves to be less conservative when determining safe motions. Specifically, for environments with limited space, it was demonstrated that using robots’ kinematics may result in a collision, while our strategy results in safe motion. We also performed experiments that proved collision-free navigation of HMRs with this approach. The outcomes of this work provide more reliable motion control for HMRs, especially when the robots’ masses or inertias are significantly different, for example, warehouses. The developments in this work are also applicable to vehicles and can therefore be beneficial in automated collision avoidance in autonomous driving and intelligent transportation.

In this article, we propose a novel predictive strategy for collision avoidance of multiple mobile robots by precisely quantifying the robot’s impending collisions. Our proposed approach is based on formulating the collisions by predicting the robots’ states and provides generalized safety conditions that are more realistic measures for collisions. This is a novel safety condition that extends the previous methods that use distance or velocity for safety conditions into a more generalized format to effectively avoid underestimating or overestimating the collisions by explicitly considering surrounding objects’ motions. We formulate the safety condition based on the robots’ motion into a vector format and construct a predictive safety matrix. We then consolidate the safety condition based on the proposed safety matrix’s eigenvalues. To effectively incorporate the safety condition into a control design considering uncertainties in the input sensor measurements, we perform uncertainty analysis on our proposed safety condition, the results of which are further used in this article to design a robust collision avoidance and tracking controller. This tracking controller avoids collisions effectively by incorporating the proposed safety condition into a robust tracking control design that is mathematically proven to be stable and guaranteeing safe motion. We show that the proposed safety condition, by effectively quantifying the conflicts among the robots, avoids collisions while achieving improved tracking performance compared to other existing reactive methods. This method is also verified experimentally and is shown to be effective in real-time control with superior tracking performance to the existing methods.

In this paper, we develop a centralized and automated conflict resolution methodology for vehicles that guarantees collision-free motion by coordinating the vehicles' speeds on predetermined paths. A new computationally efficient formulation for collision-free motion constraints of vehicles is proposed. We develop a methodology to maximize the speed and safety distance of vehicles subjected to the collision-free motion constraints. Our method instead of constantly checking Euclidean distances among the vehicles, which is computationally expensive, first, finds the potential collision zones in the environment and then determines collision-free constraints based on a parameter called velocity rate. We also develop a new traffic flow measurement quantity, described as motion density, to study the performance of different conflict resolution scenarios. We studied how safety and speed objectives of vehicles will affect the motion density. Using simulation results the effectiveness of our approach is verified for coordinated transportation of multiple vehicles.

Free gait becomes necessary in walking robots when they come to walk over discontinuous terrain or face some difficulties in walking. A basic gait generation strategy is presented here using reinforcement learning and fuzzy reward approach. A six-legged (hexapod) robot is implemented using Q-learning algorithm. The learning ability of walking in a hexapod robot is explored considering only the ability of moving its legs and using a fuzzy rewarding system telling whether and how it is moving forward. Results show that the hexapod robot learns to walk using the presented approach properly.