SUMMARY: We introduce a novel probabilistic algorithm (CPRM) for real-time motion planning in the configuration space ${\EuScript C}$ . Our algorithm differs from a probabilistic road map (PRM) algorithm in the motion between a pair of anchoring points (local planner) which takes place on the boundary of the obstacle subspace ${\EuScript O}$ . We define a varying potential fieldfon ∂ ${\EuScript O}$ as a Morse function and follow $\vec{\nabla} f$ . We then exemplify our algorithm on a redundant worm climbing robot withndegrees of freedom and compare our algorithm running results with those of the PRM.

Autonomous Driving (AD) introduces dramatic changes to the way we travel. This emerging technology has the potential to impact the transportation sector across a wide array of categories including safety, efficiency, congestion, legislation, and travel behavior. In this survey, we review the main issues involved in AD as discussed in the literature, and shed light on topics that we consider requiring further development based on Google Scholar and the Web of Science (WoS) data base. The paper also provides the results of research trends related to Autonomous Driving based on analysis of the number of search results listed in google trends. According to our research, the fields of Vehicle-to-Vehicle (V2V) and Vehicle-to-Cloud (V2C) networking are of higher interest due to the technological gaps and standardization processes. In addition, cyber and security research is in acceleration due to its importance.

Many tasks performed by swarms of unmanned aerial vehicles require localization. In many cases, the sensors that take part in the localization process suffer from inherent measurement errors. This problem is amplified when disruptions are added, either endogenously through Byzantine failures of agents within the swarm, or exogenously by some external source, such as a GNSS jammer. In this paper, we first introduce an improved localization method based on distance observation. Then, we devise schemes for detecting Byzantine agents, in scenarios of endogenous disruptions, and for detecting a disrupted area, in case the source of the problem is exogenous. Finally, we apply pool testing techniques to reduce the communication traffic and the computation time of our schemes. The optimal pool size should be chosen carefully, as very small or very large pools may impair the ability to identify the source/s of disruption. A set of simulated experiments demonstrates the effectiveness of our proposed methods, which enable reliable error estimation even amid disruptions. This work is the first, to the best of our knowledge, that embeds identification of endogenous and exogenous disruptions into the localization process.

In robotics, knowing a mechanism's configuration is essential. Commonly, potentiometers or encoders are used in order to obtain the mechanism's configuration. Nevertheless, such sensors are useless for applications where a surface's spatial-shape is to be attained. For that end, we introduce a novel sensor mesh. We use an electrical-resistors grid as the shape sensor. The resistance in each grid segment changes with accordance to its length. So, by attaching the sensor mesh to an elastic grid, bending results with resistance change in the associated segments. For practical reasons, only the peripheral nodes are wired. We introduce an algorithm to extract the resistance of each of the segments. We then formulate the forward kinematics of elastic grid mechanisms which later on, we apply, in order to obtain an estimate for the surface shape. We exemplify our algorithm on a 40 resistors grid.

Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of N ≥ 6 agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model’s full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios.

Recent years show an increasing interest in flexible robots due to their adaptability merits. This paper introduces a novel set of hyper-redundant flexible robots which we call actuated flexible manifold (AFM). The AFM is a two-dimensional hyper-redundant grid surface embedded in $ℝ2$ or $ℝ3$. Theoretically, such a mechanism can be manipulated into any continuous smooth function. We introduce the mathematical framework for the kinematics of an AFM. We prove that for a nonsingular configuration, the number of degrees of freedom (DOF) of an AFM is simply the number of its grid segments. We also show that for a planar rectangular grid, every nonsingular configuration that is also energetically stable is isolated. We show how to calculate the forward and inverse kinematics for such a mechanism. Our analysis is also applicable for three-dimensional hyper-redundant structures as well. Finally, we demonstrate our solution on some actuated flexible grid-shaped surfaces.