Dinesh Thakur

A Computationally Efficient Approach to Trajectory Management for Coordinated Aerial Surveillance

Time optimal path planning and trajectory management algorithms for air vehicles with limited on-board computing resources require an efficient approach to satisfy flight dynamic constraints needed to guarantee paths are feasible. B-spline curves enable compact definition of feasible airplane trajectories that are suited for on-board real-time computation. The design of a trajectory definition and management algorithm suited for a multi-agent persistent surveillance application is described. The proposed solution post-processes the output of a point-by-point path planner and converts it into a minimal representation. Key design requirements include minimization of mission execution time, ability to seamlessly redirect agents based on information acquired by sensor feedback, and robust adherence to mission and vehicle motion constraints. A simple coordinated aerial surveillance scenario is described and demonstrated using the algorithms presented.

Planning for opportunistic surveillance with multiple robots

We are interested in the multiple robot surveillance problem where robots must allocate waypoints to be visited among themselves and plan paths through different waypoints while avoiding obstacles. Furthermore, the robots are allocated specific times to reach their respective goal locations and as a result they have to decide which robots have to visit which waypoints. Such a problem has the challenge of computing the allocation of waypoints across robots, ordering for these waypoints and dynamical feasibility of the paths between waypoints. We present an algorithm that runs a series of graph searches to solve the problem and provide theoretical analysis that our approach yields an optimal solution. We present simulated results as well as experiments on two UAVs that validate the capability of our algorithm. For a single robot, we can solve instances having 10-15 waypoints and for multiple robots, instances having five robots and 10 waypoints can be solved.

Coordinated Path Planning for Fixed-Wing UAS Conducting Persistent Surveillance Missions

Algorithms for fixed-wing unmanned aerial systems (UAS) must integrate on-board sensor capabilities and vehicle maneuver constraints to reliably satisfy the objectives of persistent surveillance, path planning, and trajectory management. In many cases, the characteristic dimensions of sensor fields of view are comparable with the turning radius of the UAS platform. Consequently, when persistent, full area, time-critical coverage is required and the number of assets is limited, the complexity of path planning is increased, as the turn radius becomes comparable with or exceeds the sensor footprint. A technique is developed to integrate persistent surveillance mission requirements with sensor resolution and field-of-view to facilitate efficient path planning. Graph search techniques and spline-based methods are combined to develop computationally simple algorithms that converge to feasible paths with

An automated system for semantic object labeling with soft object recognition and dynamic programming segmentation

G^{2}

Playing Fetch with Your Robot: The Ability of Robots to Locate and Interact with Objects

continuity. (

Coordinated commencement of pre-planned routes for fixed-wing UAS starting from arbitrary locations - a near real-time solution

G^{2}

R-MASTIF: robotic mobile autonomous system for threat interrogation and object fetch

continuity in this context means the path, which is constructed from a concatenated series of

A column generation approach for optimized routing and coordination of a UAV fleet

C^{2}

Obstacle avoidance and path intersection validation for UAS: A B-spline approach

curve segments, and its derivatives are continuous through two derivatives, but the magnitudes of second derivatives may differ at the boundaries, where these curve segments are joined to form the entire path. Curvature is continuous throughout.) Two example cases are provided. In the first, the turning radius is small in comparison with the sensor footprint, while in the second, the minimum turn radius is a critical parameter in the determination of a feasible path.

The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception

This paper presents an automated system for generating a semantic map of inventory in a retail environment. Developing this map involves assigning a department label to each discrete section of shelving. We use a priori information to boost data from laser and camera sensors for object recognition and semantic labeling. We introduce a soft object map and a dynamic programming algorithm for point cloud segmentation. The primary contribution of this work is the integration of multiple systems including an automated path planning and navigation subsystem and a semantic mapping object recognition system. This work also represents an important contribution to robots working reliably in human environments. To our knowledge this is the first actual implementation of a fully automated robot inventory labeling system for a retail environment. The framework presented in this paper is easily scalable to other retail environments and is also relevant in any indoor environment with organized shelves, such as business storage facilities and hospital pharmacies.