Camera-Based Net Avoidance Controls of Underwater Robots

Fishing nets are dangerous obstacles for an underwater robot whose aim is to reach a goal in unknown underwater environments. This paper proposes how to make the robot reach its goal, while avoiding fishing nets that are detected using the robot's camera sensors. For the detection of underwater nets based on camera measurements of the robot, we can use deep neural networks. Passive camera sensors do not provide the distance information between the robot and a net. Camera sensors only provide the bearing angle of a net, with respect to the robot's camera pose. There may be trailing wires that extend from a net, and the wires can entangle the robot before the robot detects the net. Moreover, light, viewpoint, and sea floor condition can decrease the net detection probability in practice. Therefore, whenever a net is detected by the robot's camera, we make the robot avoid the detected net by moving away from the net abruptly. For moving away from the net, the robot uses the bounding box for the detected net in the camera image. After the robot moves backward for a certain distance, the robot makes a large circular turn to approach the goal, while avoiding the net. A large circular turn is used, since moving close to a net is too dangerous for the robot. As far as we know, our paper is unique in addressing reactive control laws for approaching the goal, while avoiding fishing nets detected using camera sensors. The effectiveness of the proposed net avoidance controls is verified using simulations.

Three-Dimensional Multi-Agent Foraging Strategy Based on Local Interaction

This paper considers a multi-agent foraging problem, where multiple autonomous agents find resources (called pucks) in a bounded workspace and carry the found resources to a designated location, called the base. This article considers the case where autonomous agents move in unknown 3-D workspace with many obstacles. This article describes 3-D multi-agent foraging based on local interaction, which does not rely on global localization of an agent. This paper proposes a 3-D foraging strategy which has the following two steps. The first step is to detect all pucks inside the 3-D cluttered unknown workspace, such that every puck in the workspace is detected in a provably complete manner. The next step is to generate a path from the base to every puck, followed by collecting every puck to the base. Since an agent cannot use global localization, each agent depends on local interaction to bring every puck to the base. In this article, every agent on a path to a puck is used for guiding an agent to reach the puck and to bring the puck to the base. To the best of our knowledge, this article is novel in letting multiple agents perform foraging and puck carrying in 3-D cluttered unknown workspace, while not relying on global localization of an agent. In addition, the proposed search strategy is provably complete in detecting all pucks in the 3-D cluttered bounded workspace. MATLAB simulations demonstrate the outperformance of the proposed multi-agent foraging strategy in 3-D cluttered workspace.

Simultaneous Localization and Guidance of Two Underwater Hexapod Robots under Underwater Currents

This paper addresses the simultaneous localization and guidance of two underwater hexapod robots under sea currents. This paper considers an underwater environment where there are no landmarks or features which can assist a robot's localization. This article uses two underwater hexapod robots that move together while using each other as landmarks in the environment. While one robot moves, another robot extends its legs into the seabed and acts as a static landmark. A moving robot measures the relative position of another static robot, in order to estimate its position while it moves. Due to underwater currents, a robot cannot maintain its desired course. Moreover, there may be obstacles, such as underwater nets, that a robot needs to avoid. We thus develop a guidance strategy for avoiding obstacles, while estimating the perturbation due to the sea currents. As far as we know, this paper is novel in tackling simultaneous localization and guidance of underwater hexapod robots in environments with various obstacles. MATLAB simulations demonstrate that the proposed methods are effective in harsh environments where the sea current magnitude can change irregularly.

Fast Path Planning of Autonomous Vehicles in 3D Environments

Three dimensional path planner is crucial for the safe navigation of autonomous vehicles (AV), such as unmanned aerial vehicles or unmanned underwater vehicles, which operate in three dimensions. In this paper, we develop a novel 3D path planner, which is fast in generating a near-optimal solution path. The planner generates the 3D path considering the size of an AV so that as the AV traverses the constructed path, it does not collide with an obstacle. This paper introduces a 3D path planner with novel concepts, such as a virtual agent and virtual sensors. In order to generate a 3D path to the goal as fast as possible, we let the virtual agent deploy virtual sensors iteratively, such that the connected sensor network can be formed. The constructed sensor network serves as a topological map for the AV, and we find a shortest path from the start to the goal utilizing the network. The virtual agent’s maneuver is biased towards the goal, in order to find a path to the goal as fast as possible. Moreover, the size of the agent is set considering the safety margin of the generated path. Through MATLAB simulations, we demonstrate the outperformance (low computational load and short path length) of our 3D path planner by comparing it with the 3D RRT-star algorithm.

Time-efficient path planning using two virtual robots

This article introduces time-efficient path planning algorithms handling both path length and safety within a reasonable computational time. The path is planned considering the robot’s size so that as the robot traverses the constructed path, it doesn’t collide with an obstacle boundary. This article introduces two virtual robots deploying virtual nodes which discretize the obstacle-free space into a topological map. Using the topological map, the planner generates a safe and near-optimal path within a reasonable computational time. It is proved that our planner finds a safe path to the goal in finite time. Using MATLAB simulations, we verify the effectiveness of our path planning algorithms by comparing it with the rapidly-exploring random tree (RRT)-star algorithm in three-dimensional environments.