Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

Simplex Control Methods for Robust Convergence of Small Unmanned Aircraft Flight Trajectories in the Constrained Urban Environment

Constrained optimal control problems for Small Unmanned Aircraft Systems (SUAS) have long suffered from excessive computation times caused by a combination of constraint modeling techniques, the quality of the initial path solution provided to the optimal control solver, and improperly defining the bounds on system state variables, ultimately preventing implementation into real-time, on-board systems. In this research, a new hybrid approach is examined for real-time path planning of SUAS. During autonomous flight, a SUAS is tasked to traverse from one target region to a second target region while avoiding hard constraints consisting of building structures of an urban environment. Feasible path solutions are determined through highly constrained spaces, investigating narrow corridors, visiting multiple waypoints, and minimizing incursions to keep-out regions. These issues are addressed herein with a new approach by triangulating the search space in two-dimensions, or using a tetrahedron discretization in three-dimensions to define a polygonal search corridor free of constraints while alleviating the dependency of problem specific parameters by translating the problem to barycentric coordinates. Within this connected simplex construct, trajectories are solved using direct orthogonal collocation methods while leveraging navigation mesh techniques developed for fast geometric path planning solutions. To illustrate two-dimensional flight trajectories, sample results are applied to flight through downtown Chicago at an altitude of 600 feet above ground level. The three-dimensional problem is examined for feasibility by applying the methodology to a small scale problem. Computation and objective times are reported to illustrate the design implications for real-time optimal control systems, with results showing 86% reduction in computation time over traditional methods

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Vision-Aided Navigation using Tracked Lankmarks

This thesis presents vision-based state estimation algorithms for autonomous vehicles to navigate within GPS-denied environments. To accomplish this objective, an approach is developed that utilizes a priori information about the environment. In particular, the algorithm leverages recognizable ‘landmarks’ in the environment, the positions of which are known in advance, to stabilize the state estimate. Measurements of the position of one or more landmarks in the image plane of a monocular camera are then filtered using an extended Kalman filter (EKF) with data from a traditional inertial measurement unit (IMU) consisting of accelerometers and rate gyros to produce the state estimate. Additionally, the EKF algorithm is adapted to accommodate a stereo camera configuration to measure the distance to a landmark using parallax. The performances of the state estimation algorithms for both the monocular and stereo camera configurations are tested and compared using simulation studies with a quadcopter UAV model. State estimation results are then presented using flight data from a quadcopter UAV instrumented with an IMU and a GoPro camera. It is shown that the proposed landmark navigation method is capable of preventing IMU drift errors by providing a GPS-like measurement when landmarks can be identified. Additionally, the landmark method pairs well with non a priori measurements for interims when landmarks are not available

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios

Trade-Space Analysis of a Small Unmanned Vehicle System for Radiological Search Missions

Nuclear and radiological terrorism is a persistent threat to United States national security. The research and development of new technological capabilities is vital to bolstering emergency response and prevention capabilities in support of national security initiatives. This research characterized the applicable trade-space for a system of unmanned vehicles deployed for search, detection, and identification of radiological source material. Exploration included the development of a CONOPS, a functional decomposition and physical allocation, design considerations, and an analysis of feasibility and utility. The concept system comprises of a ground control station, ground vehicle, hybrid-electric multirotor, and fixed-wing vehicle with an open architecture permitting the exchange of payload components. Payload options include a Geiger-Müller detector or scintillator for large area search and a scintillator or high purity germanium semiconductor for radioisotope identification. Endurance estimates revealed that a hybrid-electric multirotor is capable of carrying a 6.8-kilogram payload for 58 minutes. Similar estimates indicated that a battery-powered fixed-wing vehicle can provide a minimum of 41 minutes of endurance with a payload mass fraction of 15% (1.36-kilogram payload), whereas a gasoline-powered vehicle with the same payload mass fraction (1.95-kilogram payload) can operate for 12 hours. Electric multirotors are limited to a maximum endurance of 20 minutes, which is insufficient for radiological search missions. The system concept proves effective to the radiological search mission and can be expanded to other mission areas through its open architecture