SAFETY-GUARANTEED TASK PLANNING FOR BIPEDAL NAVIGATION IN PARTIALLY OBSERVABLE ENVIRONMENTS

Bipedal robots are becoming more capable as basic hardware and control challenges are being overcome, however reasoning about safety at the task and motion planning levels has been largely underexplored. I would like to make key steps towards guaranteeing safe locomotion in cluttered environments in the presence of humans or other dynamic obstacles by designing a hierarchical task planning framework that incorporates safety guarantees at each level. This layered planning framework is composed of a coarse high-level symbolic navigation planner and a lower-level local action planner. A belief abstraction at the global navigation planning level enables belief estimation of non-visible dynamic obstacle states and guarantees navigation safety with collision avoidance. Both planning layers employ linear temporal logic for a reactive game synthesis between the robot and its environment while incorporating lower level safe locomotion keyframe policies into formal task specification design. The high-level symbolic navigation planner has been extended to leverage the capabilities of a heterogeneous multi-agent team to resolve environment assumption violations that appear at runtime. Modifications in the navigation planner in conjunction with a coordination layer allow each agent to guarantee immediate safety and eventual task completion in the presence of an assumption violation if another agent exists that can resolve said violation, e.g. a door is closed that another dexterous agent can open. The planning framework leverages the expressive nature and formal guarantees of LTL to generate provably correct controllers for complex robotic systems. The use of belief space planning for dynamic obstacle belief tracking and heterogeneous robot capabilities to assist one another when environment assumptions are violated allows the planning framework to reduce the conservativeness traditionally associated with using formal methods for robot planning.M.S

Path-Tree Optimization in Partially Observable Environments using Rapidly-Exploring Belief-Space Graphs

Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a path-tree in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm has three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime.Comment: Also submitted to IROS 2022 and RA-

Belief Space-Guided Navigation for Robots and Autonomous Vehicles

Navigating through the environment is a fundamental capability for mobile robots, which is still very challenging today. Most robotic applications these days, such as mining, disaster response, and agriculture, require the robots to move and perform tasks in a variety of environments which are stochastic and sometimes even unpredictable. A robot often cannot directly observe its current state but instead estimates a distribution over the set of possible states based on sensor measurements that are both noisy and partial. The actual robot position differs from its prediction after applying a motion command, due to actuation noise. Classic algorithms for navigation should adapt themselves to where the behavior of the environment is stochastic, and the execution of the motions has great uncertainty. To solve such challenging problems, we propose to guide the robot's navigation in the belief space. Belief space-guided navigation differs fundamentally from planning without uncertainty where the state of the robot is always assumed to be known precisely. The robot senses its environment, estimates its current state due to perception uncertainty, and decides whether a new (or priori) action is appropriate. Based on that determination, it actuates its sensors to move with motion uncertainty in the environment. This inspires us to connect robot perception and motion planning, and reason about the uncertainty to improve the quality of plan so that the robot can follow a collision-free, feasible kinodynamic, and task-optimal trajectory. In this dissertation, we explore the belief space-guided robotic navigation problems, which include belief space-based scene understanding for autonomous vehicles and introduce belief space guided robotic planning. We first investigate how belief space can facilitate scene understanding under the context of lane marking quality assessment in the application of autonomous driving. We propose a new problem by measuring the quality of roads and ensuring they are ready for autonomous driving. We focus on developing three quality metrics for lane markings (LMs), correctness metric, shape metric, and visibility metric, and algorithms to assess LM qualities to facilitate scene understanding. As another example of using belief space for better scene understanding, we utilize crowdsourced images from multiple vehicles to help verify LMs for high-definition (HD) map maintenance. An LM is consistent if belief functions from the map and the image satisfy statistical hypothesis testing. We further extend the Bayesian belief model into a sequential belief update using crowdsourced images. LMs with a higher probability of existence are kept in the HD map whereas those with a lower probability of existence are removed from the HD map. Belief space can also help us to tightly connect perception and motion planning. As an example, we develop a motion planning strategy for autonomous vehicles. Named as virtual lane boundary approach, this framework considers obstacle avoidance, trajectory smoothness (to satisfy vehicle kinodynamic constraints), trajectory continuity (to avoid sudden movements), global positioning system (GPS) following quality (to execute the global plan), and lane following or partial direction following (to meet human expectation). Consequently, vehicle motion is more human-compatible than existing approaches. As another example of how belief space can help guide robots for different tasks, we propose to use it for the probabilistic boundary coverage of unknown target fields (UTFs). We employ Gaussian processes as a local belief function to approximate a field boundary distribution in an ellipse-shaped local region. The local belief function allows us to predict UTF boundary trends and establish an adjacent ellipse for further exploration. The process is governed by a depth-first search process until UTF is approximately enclosed by connected ellipses when the boundary coverage process ends. We formally prove that our boundary coverage process guarantees the enclosure above a given coverage ratio with a preset probability threshold

Multimodal Planning under Uncertainty: Task-Motion Planning and Collision Avoidance

openIn this thesis we investigate the problem of motion planning under environment uncertainty. Specifically, we focus on Task-Motion Planning (TMP) and probabilistic collision avoidance which are presented as two parts in this thesis. Though the two parts are largely self-contained, collision avoidance is an integral part of TMP or any robot motion planning problem in general. The problem of TMP which is the subject of Part I is by itself challenging and hence in Part I, collision computation is not the main focus and is addressed with a deterministic approach. Moreover, motion planning is performed offline since we assume static obstacles in the environment. Online TMP, incorporating dynamic obstacles or other environment changes is rather difficult due to the computational challenges associated with updating the changing task domain. As such, we devote Part II entirely to the field of online probabilistic collision avoidance motion planning. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In Part I of this thesis, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as Belief Space Planning (BSP). The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our method by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work. In Part II of this thesis, we present a BSP framework that accounts for the landmark uncertainties during robot localization. We further extend the state-of-the-art by computing an exact expression for the collision probability under Gaussian motion and perception uncertainties. Existing BSP approaches assume that the landmark locations are well known or are known with little uncertainty. However, this might not be true in practice. Noisy sensors and imperfect motions compound to the errors originating from the estimate of environment features. Moreover, possible occlusions and dynamic objects in the environment render imperfect landmark estimation. Consequently, not considering this uncertainty can result in wrongly localizing the robot, leading to inefficient plans. Our approach incorporates the landmark uncertainty within the Bayes filter framework. We also analyze the effect of considering this uncertainty and delineate the conditions under which it can be ignored. Furthermore, we also investigate the problem of safe motion planning under Gaussian motion and sensing uncertainties. Existing approaches approximate the collision probability using upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans. We formulate the collision probability process as a quadratic form in random variables. Under Gaussian distribution assumptions, an exact expression for collision probability is thus obtained which is computable in real-time. Further, we compute a tight upper bound for fast online computation of collision probability and also derive a collision avoidance constraint to be used in an optimization setting. We demonstrate and evaluate our approach using a theoretical example and simulations in single and multi-robot settings using mobile and aerial robots. A comparison of our approach to different state-of-the-art methods are also provided.openXXXIII CICLO - BIOINGEGNERIA E ROBOTICA - BIOENGINEERING AND ROBOTICSThomas, Anton

Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

Planning Algorithms Under Uncertainty for a Team of a UAV and a UGV for Underground Exploration

Robots’ autonomy has been studied for decades in different environments, but only recently, thanks to the advance in technology and interests, robots for underground exploration gained more attention. Due to the many challenges that any robot must face in such harsh environments, this remains an challenging and complex problem to solve. As technology became cheaper and more accessible, the use of robots for underground ex- ploration increased. One of the main challenges is concerned with robot localization, which is not easily provided by any Global Navigation Services System (GNSS). Many developments have been achieved for indoor mobile ground robots, making them the easiest fit for subterranean explo- ration. With the commercialization of small drones, the potentials and benefits of aerial exploration increased along with challenges connected to their dynamics. This dissertation presents two path planning algorithms for a team of robots composed of an Unmanned Ground Vehicle (UGV) and an Unmanned Aerial Vehicle (UAV) with the task of ex- ploring a subterranean environment. First, the UAV’s localization problem is addressed by fusing different sensors present on both robots in a centralized manner. Second, a path planning algo- rithm that minimizes the UAV’s localization error is proposed. The algorithm propagates the UAV motion model in the Belief Space, evaluating for potential exploration routes that optimize the sensors’ observations. Third, a new algorithm is presented, which results to be more robust to dif- ferent environmental conditions that could affect the sensor’s measurements. This last planning algorithm leverages the use of machine learning, in particular the Gaussian Process, to improve the algorithm’s knowledge of the surrounding environment pointing out when sensors provide poor observations. The algorithm utilizes real sensor measurements to learn and predict the UAV’s lo- calization error. Extensive results are presented for the first two parts regarding the UAV’s localization and the path planning algorithm in the belief space. The localization algorithm is supported with real-world scenario experimental results, while the belief space planning algorithm has been extensively tested in a simulated environment. Finally, the last approach has also been tested in a simulated environ- ment and showed its benefits compared to the first planning algorithm