Robotics Research Centre

We present an algorithm for multi robotic exploration of an unknown terrain where the robots are also required to serve the role of hops or nodes in a communication link maintained between a fixed base station and the last robot (end effector robot) in the chain. A baseline algorithm is presented as a tree traversal mechanism akin to a depth first strategy, further embellished by an adaptive rule that decides the number of children based on the local obstacle configuration at a node and avoidance of redundancy in traversal through a look-ahead method that decides the utility of spreading the tree from the current robotic hop node. This system finds immense utility in arenas such as planetary exploration, search and rescue scenarios and scenarios where the robots have limited on-board computing capabilities and need to continuously preserve the link with a fixed base station for receiving instructions or transfer of data.

A multi-robotic exploration with the requirement of communication link to a fixed base station is presented in this paper. The robots organize themselves into roles of maintainers of communication (hinged robots or robot nodes) or explorers of the environment ensuring that every robot is in contact with the base station directly or through the hinged robots. A two phased strategy for the same is presented. The first phase is characterized by a recursive growth of trees that starts from the root node or the base station and then repeated from other nodes of the hitherto grown tree in a depth first fashion. The second phase constitutes the recursive tree growth invoked repeatedly from the frontier nodes. While the first phase rapidly explores areas around the base station in a concentric fashion, the second phase extends the depth of the explored area to increase the limits of coverage. The strategy is consistent in that none of the robots loose contact with the base station. Extensive simulations confirm the efficacy of the method and comparisons portray performance gain in terms of exploration time and absence of deadlocks vis-a-vis the few methods previously reported in the literature.

In this paper we address the problem of detecting road intersections. We present two approaches to solve the problem of intersection detection in an unstructured outdoor setting. The first is a natural extension of the popular VFH* obstacle avoidance algorithm. It detects intersections and tracks, over a period of time, the angles at which gaps in the robot's certainty grid (CG) are first observed. The second approach uses techniques from image processing and computational geometry on the certainty grid image, to extract a skeleton of the navigable region, thus providing the intersections. We show experimental results portraying intersection detection due to both methods and show the results. On the whole, we found that the robot was able to detect all possible intersections.

We present an autonomous system for outdoor terrain (road) mapping using a robot equipped with viable low cost sensors (2D laser scanner, cameras, odometry, gyroscope and commercial GPS). The research work on outdoor navigation with known maps has matured well in past few years, but navigation and exploration of roads in an unknown area is still emerging. The presented system is an amalgamation of various computation modules running in parallel and interacting asynchronously with each other through message queues. The goal of the system is for a vehicle to explore and build a road network graph of a given area by autonomously navigating through all the connected roads in the area. The graph nodes contain topographic and semantic properties to allow reconstruction of the road network at a later stage for easier navigation.

This paper presents a model predictive control (MPC) based algorithm for tracking multiple targets using a swarm of unmanned aerial vehicles (UAVs). All the UAVs belong to fixed-wing category with constraints on flight velocity, climb rate and turn rate. Each UAV carries a camera to detect and track the target. Two cases are considered where for the first case, the number of the UAVs is equal to the number of targets. For the second case, the number of UAVs is lesser than the number of targets leading to a conservative solution where the objective is to maximize the average time duration for which the targets are in the field-of-view (FOV) of any one of the UAV's camera. A data driven Gaussian process (GP) based model is developed to relate the hyperparameters used in MPC to the mission efficiency. Bayesian optimization is performed to obtain the hyperparameters of the MPC that maximize the mission efficiency. Numerical simulations are performed for both cases using algorithm based on distributed MPC formulation. A performance comparison is provided with the centralized MPC formulation.

In this paper, we explore Probabilistic Inverse Velocity Obstacle (PIVO) as an alternative to probabilistic versions of Velocity Obstacles (PVO) for free flying quadrotor systems. Inverse Velocity Obstacles compute effective controls from a sequence of observations on other agents without the need to access ego state information. As a direct consequence of this the ego state noise is not entailed in probabilistic formulations bringing in verifiable advantages in the form of reduced path lengths, less conservative maneuvers, reduced occurrences of stopping/hovering to let others pass. These advantages are vividly tabulated in this paper, showcasing the efficacy of PIVO as an alternative to probabilistic versions of Velocity Obstacles. In particular we show the benefits of PIVO over PVO in relation to sample complexity as well as overall trajectory lengths. We also show the efficacy of our probabilistic formulation in handling non-parametric and often multimodal noise distributions.