Multi-robot mission optimisation : an online approach for optimised, long range inspection and sampling missions

Mission execution optimisation is an essential aspect for the real world deployment of robotic systems. Execution optimisation can affect the outcome of a mission by allowing longer missions to be executed or by minimising the execution time of a mission. This work proposes methods for optimising inspection and sensing missions undertaken by a team of robots operating under communication and budget constraints. Regarding the inspection missions, it proposes the use of an information sharing architecture that is tolerant of communication errors combined with multirobot task allocation approaches that are inspired by the optimisation literature. Regarding the optimisation of sensing missions under budget constraints novel heuristic approaches are proposed that allow optimisation to be performed online. These methods are then combined to allow the online optimisation of long-range sensing missions performed by a team of robots communicating through a noisy channel and having budget constraints. All the proposed approaches have been evaluated using simulations and real-world robots. The gathered results are discussed in detail and show the benefits and the constraints of the proposed approaches, along with suggestions for further future directions

Long-term Informative Path Planning with Autonomous Soaring

The ability of UAVs to cover large areas efficiently is valuable for information gathering missions. For long-term information gathering, a UAV may extend its endurance by accessing energy sources present in the atmosphere. Thermals are a favourable source of wind energy and thermal soaring is adopted in this thesis to enable long-term information gathering. This thesis proposes energy-constrained path planning algorithms for a gliding UAV to maximise information gain given a mission time that greatly exceeds the UAV's endurance. This thesis is motivated by the problem of probabilistic target-search performed by an energy-constrained UAV, which is tasked to simultaneously search for a lost ground target and explore for thermals to regain energy. This problem is termed informative soaring (IFS) and combines informative path planning (IPP) with energy constraints. IFS is shown to be NP-hard by showing that it has a similar problem structure to the weight-constrained shortest path problem with replenishments. While an optimal solution may not exist in polynomial time, this thesis proposes path planning algorithms based on informed tree search to find high quality plans with low computational cost. This thesis addresses complex probabilistic belief maps and three primary contributions are presented: • First, IFS is formulated as a graph search problem by observing that any feasible long-term plan must alternate between 1) information gathering between thermals and 2) replenishing energy within thermals. This is a first step to reducing the large search state space. • The second contribution is observing that a complex belief map can be viewed as a collection of information clusters and using a divide and conquer approach, cluster tree search (CTS), to efficiently find high-quality plans in the large search state space. In CTS, near-greedy tree search is used to find locally optimal plans and two global planning versions are proposed to combine local plans into a full plan. Monte Carlo simulation studies show that CTS produces similar plans to variations of exhaustive search, but runs five to 20 times faster. The more computationally efficient version, CTSDP, uses dynamic programming (DP) to optimally combine local plans. CTSDP is executed in real time on board a UAV to demonstrate computational feasibility. • The third contribution is an extension of CTS to unknown drifting thermals. A thermal exploration map is created to detect new thermals that will eventually intercept clusters, and therefore be valuable to the mission. Time windows are computed for known thermals and an optimal cluster visit schedule is formed. A tree search algorithm called CTSDrift combines CTS and thermal exploration. Using 2400 Monte Carlo simulations, CTSDrift is evaluated against a Full Knowledge method that has full knowledge of the thermal field and a Greedy method. On average, CTSDrift outperforms Greedy in one-third of trials, and achieves similar performance to Full Knowledge when environmental conditions are favourable

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

An informative path planning framework for UAV-based terrain monitoring

© 2020, The Author(s). Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task

Dynamic mission planning for communication control in multiple unmanned aircraft teams

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 147-160).As autonomous technologies continue to progress, teams of multiple unmanned aerial vehicles will play an increasingly important role in civilian and military applications. A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This thesis presents a unique method to control communications through distributed mission planning to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. The distributed algorithm uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes the team is able to optimize the use of agents to address the needs of dynamic complex missions. The framework is designed to consider realistic network communication dynamics including path loss, stochastic fading, and information routing. The planning strategy is shown to ensure agents support both data-rate and interconnectivity bit-error- rate requirements during task execution. In addition, a method is provided for UAVs to estimate the network performance during times of uncertainty, adjust their plans to acceptable levels of risk, and adapt the planning behavior to changes in the communication environment. The system performance is verified through multiple experiments conducted in simulation. Finally, the work developed is implemented in outdoor flight testing with a team of up to four UAVs to demonstrate real-time capability and robustness to imperfections in the environment. The results validate the proposed framework, but highlight some of the challenges these systems face when operating in outdoor uncontrolled environments.by Andrew N. Kopeikin.S.M

Motion Planning in Artificial and Natural Vector Fields

This dissertation advances the field of autonomous vehicle motion planning in various challenging environments, ranging from flows and planetary atmospheres to cluttered real-world scenarios. By addressing the challenge of navigating environmental flows, this work introduces the Flow-Aware Fast Marching Tree algorithm (FlowFMT*). This algorithm optimizes motion planning for unmanned vehicles, such as UAVs and AUVs, navigating in tridimensional static flows. By considering reachability constraints caused by vehicle and flow dynamics, flow-aware neighborhood sets are found and used to reduce the number of calls to the cost function. The method computes feasible and optimal trajectories from start to goal in challenging environments that may contain obstacles or prohibited regions (e.g., no-fly zones). The method is extended to generate a vector field-based policy that optimally guides the vehicle to a given goal. Numerical comparisons with state-of-the-art control solvers demonstrate the method\u27s simplicity and accuracy. In this dissertation, the proposed sampling-based approach is used to compute trajectories for an autonomous semi-buoyant solar-powered airship in the challenging Venusian atmosphere, which is characterized by super-rotation winds. A cost function that incorporates the energetic balance of the airship is proposed to find energy-efficient trajectories. This cost function combines the main forces acting on the vehicle: weight, buoyancy, aerodynamic lift and drag, and thrust. The FlowFMT* method is also extended to consider the possibility of battery depletion due to thrust or battery charging due to solar energy and tested in this Venus atmosphere scenario. Simulations showcase how the airship selects high-altitude paths to minimize energy consumption and maximize battery recharge. They also show the airship sinking down and drifting with the wind at the altitudes where it is fully buoyant. For terrestrial applications, this dissertation finally introduces the Sensor-Space Lattice (SSLAT) motion planner, a real-time obstacle avoidance algorithm for autonomous vehicles and mobile robots equipped with planar range finders. This planner uses a lattice to tessellate the area covered by the sensor and to rapidly compute collision-free paths in the robot surroundings by optimizing a cost function. The cost function guides the vehicle to follow an artificial vector field that encodes the desired vehicle path. This planner is evaluated in challenging, cluttered static environments, such as warehouses and forests, and in the presence of moving obstacles, both in simulations and real experiments. Our results show that our algorithm performs collision checking and path planning faster than baseline methods. Since the method can have sequential or parallel implementations, we also compare the two versions of SSLAT and show that the run-time for its parallel implementation, which is independent of the number and shape of the obstacles found in the environment, provides a significant speedup due to the independent collision checks

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number