Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Encoderless position estimation and error correction techniques for miniature mobile robots

This paper presents an encoderless position estimation technique for miniature-sized mobile robots. Odometry techniques, which are based on the hardware components, are commonly used for calculating the geometric location of mobile robots. Therefore, the robot must be equipped with an appropriate sensor to measure the motion. However, due to the hardware limitations of some robots, employing extra hardware is impossible. On the other hand, in swarm robotic research, which uses a large number of mobile robots, equipping the robots with motion sensors might be costly. In this study, the trajectory of the robot is divided into several small displacements over short spans of time. Therefore, the position of the robot is calculated within a short period, using the speed equations of the robot's wheel. In addition, an error correction function is proposed that estimates the errors of the motion using a current monitoring technique. The experiments illustrate the feasibility of the proposed position estimation and error correction techniques to be used in miniature-sized mobile robots without requiring an additional sensor

Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG-RL works in a two-level manner. At the first level, SG-RL uses a geometric path-planning method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG-RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG-RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.Comment: 20 page

Development of a tabletop guidance system for educational robots

The guidance of a vehicle in an outdoor setting is typically implemented using a Real Time Kinematic Global Positioning System (RTK-GPS) potentially enhanced by auxiliary sensors such as electronic compasses, rotation encoders, gyroscopes, and vision systems. Since GPS does not function in an indoor setting where educational competitions are often held, an alternative guidance system was developed. This article describes a guidance method that contains a laser-based localization system, which uses a robot-borne single laser transmitter spinning in a horizontal plane at an angular velocity up to 81 radians per second. Sensor arrays positioned in the corners of a flat rectangular table with dimensions of 1.22 m × 1.83 m detected the laser beam passages. The relative time differences among the detections of the laser passages gave an indication of the angles of the sensors with respect to the laser beam transmitter on the robot. These angles were translated into Cartesian coordinates. The guidance of the robot was implemented using a uni-directional wireless serial connection and position feedback from the localization system. Three experiments were conducted to test the system: 1) the accuracy of the static localization system was determined while the robot stood still. In this test the average error among valid measurements was smaller than 0.3 %. However, a maximum of 3.7 % of the measurements were invalid due to several causes. 2) The accuracy of the guidance system was assessed while the robot followed a straight line. The average deviation from this straight line was 3.6 mm while the robot followed a path with a length of approximately 0.9 m. 3) The overall performance of the guidance system was studied while the robot followed a complex path consisting of 33 sub-paths. The conclusion was that the system worked reasonably accurate, unless the robot came in close proximity