Sankalp Arora

Infrastructure-free shipdeck tracking for autonomous landing

Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking a shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in three different environments - field testing results on actual helicopter flights, in simulation with a moving shipdeck for lidar based tracking and in laboratory using an occluded, and, moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions.

Emergency maneuver library - ensuring safe navigation in partially known environments

Autonomous mobile robots are required to operate in partially known and unstructured environments. It is imperative to guarantee safety of such systems for their successful deployment. Current state of the art does not fully exploit the sensor and dynamic capabilities of a robot. Also, given the non-holonomic systems with non-linear dynamic constraints, it becomes computationally infeasible to find an optimal solution if the full dynamics are to be exploited online. In this paper we present an online algorithm to guarantee the safety of the robot through an emergency maneuver library. The maneuvers in the emergency maneuver library are optimized such that the probability of finding an emergency maneuver that lies in the known obstacle free space is maximized. We prove that the related trajectory set diversity problem is monotonic and sub-modular which enables one to develop an efficient trajectory set generation algorithm with bounded sub-optimality. We generate an off-line computed trajectory set that exploits the full dynamics of the robot and the known obstacle-free region. We test and validate the algorithm on a full-size autonomous helicopter flying up to speeds of 56m/s in partially-known environments. We present results from 4 months of flight testing where the helicopter has been avoiding trees, performing autonomous landing, avoiding mountains while being guaranteed safe.

Randomized algorithm for informative path planning with budget constraints

Maximizing information gathered within a budget is a relevant problem for information gathering tasks for robots with cost or operating time constraints. This problem is also known as the informative path planning (IPP) problem or correlated orienteering. It can be formalized as that of finding budgeted routes in a graph such that the reward collected by the route is maximized, where the reward at nodes can be dependent. Unfortunately, the problem is NP-Hard and the state of the art methods are too slow to even present an approximate solution online. Here we present Randomized Anytime Orienteering (RAOr) algorithm that provides near optimal solutions while demonstrably converging to an efficient solution in runtimes that allows the solver to be run online. The key idea of our approach is to pose orienteering as a combination of a Constraint Satisfaction Problem and a Traveling Salesman Problem. This formulation allows us to restrict the search space to routes that incur minimum distance to visit a set of selected nodes, and rapidly search this space using random sampling. The paper provides the analysis of asymptotic near-optimality, convergence rates for RAOr algorithms, and present strategies to improve anytime performance of the algorithm. Our experimental results suggest an improvement by an order of magnitude over the state of the art methods in relevant simulation and in real world scenarios.

PASP: Policy based approach for sensor planning

Capabilities of mobile autonomous systems is often limited by the sensory constraints. Range sensors moving in a fixed pattern are commonly used as sensing modalities on mobile robots. The performance of these sensors can be augmented by actively controlling their configuration for minimizing the expected cost of the mission. The related information gain problem in NP hard. Current methodologies are either computationally too expensive to run online or make simplifying assumptions that fail in complex environments. We present a method to create and learn a policy that maps features calculated online to sensory actions. The policy developed in this work actively controls a nodding lidar to keep the vehicle safe at high velocities and focuses the sensor bandwidth on gaining information relevant for the mission once safety is ensured. It is validated and evaluated on an autonomous full-scale helicopter (Boeing Unmanned Little Bird) equipped with an actively controlled nodding laser. It is able to keep the vehicle safe at its maximum operating velocity, 56 m=s, and reduce the landing zone evaluation time by 500% as compared to passive nodding. The structure of the policy and efficient learning algorithm should generalize to provide a solution for actively controlling a sensor for keeping a mobile robots safe while exploring regions of interest to the robot.

Data-driven planning via imitation learning:

Robot planning is the process of selecting a sequence of actions that optimize for a task=specific objective. For instance, the objective for a navigation task would be to find collision-free paths, whereas the objective for an exploration task would be to map unknown areas. The optimal solutions to such tasks are heavily influenced by the implicit structure in the environment, i.e. the configuration of objects in the world. State-of-the-art planning approaches, however, do not exploit this structure, thereby expending valuable effort searching the action space instead of focusing on potentially good actions. In this paper, we address the problem of enabling planners to adapt their search strategies by inferring such good actions in an efficient manner using only the information uncovered by the search up until that time. We formulate this as a problem of sequential decision making under uncertainty where at a given iteration a planning policy must map the state of the search to a planning action. Unfortunately, the training process for such partial-information-based policies is slow to converge and susceptible to poor local minima. Our key insight is that if we could fully observe the underlying world map, we would easily be able to disambiguate between good and bad actions. We hence present a novel data-driven imitation learning framework to efficiently train planning policies by imitating a clairvoyant oracle: an oracle that at train time has full knowledge about the world map and can compute optimal decisions. We leverage the fact that for planning problems, such oracles can be efficiently computed and derive performance guarantees for the learnt policy. We examine two important domains that rely on partial-information-based policies: informative path planning and search-based motion planning. We validate the approach on a spectrum of environments for both problem domains, including experiments on a real UAV, and show that the learnt policy consistently outperforms state-of-the-art algorithms. Our framework is able to train policies that achieve up to 39 % more reward than state-of-the art information-gathering heuristics and a 70 × speedup as compared with A* on search-based planning problems. Our approach paves the way forward for applying data-driven techniques to other such problem domains under the umbrella of robot planning.

Anytime exploration planning algorithm for UAVs

D gathering in the physical world is tedious and often risky. Robots are ideally suited for such applications. The objective of a data gathering robot is to travel between two points maximizing the information it gains while not exceeding energy or cost constraints. This problem can be formalized as that of finding budgeted routes in a graph such that the reward collected by the route is maximized, where the reward at nodes can be related. This problem is known as correlated orienteering. Unfortunately, state of the art solutions are too slow to even present an approximate solution while running online. We describe, RRO, a sampling based anytime orienteering algorithm that provides approximate solutions to the orienteering problem with computational costs that enable it to run online. This enables the UAV’s to reason about intelligently planning non-myopic paths to explore an environment at large scale. We prove the algorithm is asymptotically optimal, and converges in polynomial time, whilst analyzing the effects of various heuristics on run times. We demonstrate that the state of the art methods take 10-12 minutes to solve a 400 node problem, whereas the method presented here takes 7-8 seconds.T paper proposes two way model to secure data during transmission and sharing over the Internet, wherein cryptographic and steganographic techniques are combined. It also proposes compressing the cipher-text file using the Lempel-Ziv algorithm to reduce its size so that it uses less space in the cover image after data embedding and also to increase the speed (rate) at which the message is delivered to the destination. The use of Discrete Cosine Transform and Inverse Discrete Cosine Transform for image processing is explained, since they will be used in the process of image compression and decompression respectively.T paper proposes two way model to secure data during transmission and sharing over the Internet, wherein cryptographic and steganographic techniques are combined. It also proposes compressing the cipher-text file using the Lempel-Ziv algorithm to reduce its size so that it uses less space in the cover image after data embedding and also to increase the speed (rate) at which the message is delivered to the destination. The use of Discrete Cosine Transform and Inverse Discrete Cosine Transform for image processing is explained, since they will be used in the process of image compression and decompression respectively.C Physical Systems has become a major trend and with it a number of technologies that have been present for decades have been swallowed up into one single term. This presentation will sum up the current and past developments within CPS including the details of successful implementations of such systems in pre-industrial demonstrators. Industrially available embedded controllers, enabling the first re-configurable assembly systems were launched within the IDEAS FP7 project and included modular systems, multi-agent development platforms and an array of supporting software tools. This work then led to the openMOS Horizon 2020 project that is attempting to reach industrial standardization with some specified protocols and communication approaches. Nonetheless CPS is now broadening its spectrum and also encompasses cloud technologies that will open the way for remote monitoring, remote diagnostics and options we may not even have thought of today. All these potential opportunities, well detailed in the IIP Roadmap 2030, led the research team to propose the Cybernetic Production System approach. Spanning from self-configuration to self-organization and self-diagnostics, this proposed vision will hereby be detailed, including active European projects and other supporting technologies that are essential to its success: Adequate business models, human-robot collaboration and circular economy strategies.