Trending Papers in robotics

Drones have a fairly short range due to their limited battery life. We propose an adaptive exploration techniques to extend the range of drones by taking advantage of physical structures such as tall buildings and trees in urban environments. Our goal is to extend the coverage of a drone delivery service by generating paths for a drone to reach its destination while learning about the energy consumption on each edge on its path in order to optimize its range for future missions. We evaluated the performance of our exploration strategy in finding the set of all reachable destinations in a city, and found that exploring locations near the boundary of the reachable sets according to the current energy model can speed up the learning process.

Behavior Trees (BTs) were invented as a tool to enable modular AI in computergames, but have received an increasing amount of attention in the roboticscommunity in the last decade. With rising demands on agent AI complexity, gameprogrammers found that the Finite State Machines (FSM) that they used scaledpoorly and were difficult to extend, adapt and reuse. In BTs, the statetransition logic is not dispersed across the individual states, but organizedin a hierarchical tree structure, with the states as leaves. This has asignificant effect on modularity, which in turn simplifies both synthesis andanalysis by humans and algorithms alike. These advantages are needed not onlyin game AI design, but also in robotics, as is evident from the research beingdone. In this paper we present a comprehensive survey of the topic of BTs inArtificial Intelligence and Robotic applications. The existing literature isdescribed and categorized based on methods, application areas andcontributions, and the paper is concluded with a list of open researchchallenges.

In this paper, in order to pursue high-efficiency robotic arc welding tasks,we propose a method based on point cloud acquired by an RGB-D sensor. Themethod consists of two parts: welding groove detection and 3D weldingtrajectory generation. The actual welding scene could be displayed in 3D pointcloud format. Focusing on the geometric feature of the welding groove, thedetection algorithm is capable of adapting well to different welding workpieceswith a V-type welding groove. Meanwhile, a 3D welding trajectory involving6-DOF poses of the welding groove for robotic manipulator motion is generated.With an acceptable error in trajectory generation, the robotic manipulatorcould drive the welding torch to follow the trajectory and execute weldingtasks. In this paper, details of the integrated robotic system are alsopresented. Experimental results prove application value of the presentedwelding robotic system.

It is very important to adhere strictly to ethical and social influences whendelivering most of our life to artificial intelligence systems. With industry4.0, the internet of things, data analysis and automation have begun to be ofgreat importance in our lives. With the Yapanese version of Industry 5.0, ithas come to our attention that machine-human interaction and human intelligenceare working in harmony with the cognitive computer. In this context, robotsworking on artificial intelligence algorithms co-ordinated with the developmentof technology have begun to enter our lives. But the consequences of the recentcomplaints of the Robots have been that important issues have arisen about howto be followed in terms of intellectual property and ethics. Although there areno laws regulating robots in our country at present, laws on robot ethics andrights abroad have entered into force. This means that it is important that weorganize the necessary arrangements in the way that robots and artificialintelligence are so important in the new world order. In this study, it wasaimed to examine the existing rules of machine and robot ethics and to set anexample for the arrangements to be made in our country, and various discussionswere given in this context.

Through many recent successes in simulation, model-free reinforcementlearning has emerged as a promising approach to solving continuous controlrobotic tasks. The research community is now able to reproduce, analyze andbuild quickly on these results due to open source implementations of learningalgorithms and simulated benchmark tasks. To carry forward these successes toreal-world applications, it is crucial to withhold utilizing the uniqueadvantages of simulations that do not transfer to the real world and experimentdirectly with physical robots. However, reinforcement learning research withphysical robots faces substantial resistance due to the lack of benchmark tasksand supporting source code. In this work, we introduce several reinforcementlearning tasks with multiple commercially available robots that present varyinglevels of learning difficulty, setup, and repeatability. On these tasks, wetest the learning performance of off-the-shelf implementations of fourreinforcement learning algorithms and analyze sensitivity to theirhyper-parameters to determine their readiness for applications in variousreal-world tasks. Our results show that with a careful setup of the taskinterface and computations, some of these implementations can be readilyapplicable to physical robots. We find that state-of-the-art learningalgorithms are highly sensitive to their hyper-parameters and their relativeordering does not transfer across tasks, indicating the necessity of re-tuningthem for each task for best performance. On the other hand, the besthyper-parameter configuration from one task may often result in effectivelearning on held-out tasks even with different robots, providing a reasonabledefault. We make the benchmark tasks publicly available to enhancereproducibility in real-world reinforcement learning.