Search-based Motion Planning for Aggressive Flight in SE(3)

Quadrotors with large thrust-to-weight ratios are able to track aggressive trajectories with sharp turns and high accelerations. In this work, we develop a search-based trajectory planning approach that exploits the quadrotor maneuverability to generate sequences of motion primitives in cluttered environments. We model the quadrotor body as an ellipsoid and compute its flight attitude along trajectories in order to check for collisions against obstacles. The ellipsoid model allows the quadrotor to pass through gaps that are smaller than its diameter with non-zero pitch or roll angles. Without any prior information about the location of gaps and associated attitude constraints, our algorithm is able to find a safe and optimal trajectory that guides the robot to its goal as fast as possible. To accelerate planning, we first perform a lower dimensional search and use it as a heuristic to guide the generation of a final dynamically feasible trajectory. We analyze critical discretization parameters of motion primitive planning and demonstrate the feasibility of the generated trajectories in various simulations and real-world experiments.Comment: 8 pages, submitted to RAL and ICRA 201

Dynamic Multi-Heuristic A*

Abstract—Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the configuration space has several ‘narrow passages’. Search-based planners on the other hand typically provide good solution qualities and are not affected by narrow passages. However, in the absence of a good heuristic or when there are deep local minima in the heuristic, they suffer from the curse of dimensionality. In this work, our primary contribution is a method for dynamically generating heuristics, in addition to the original heuristic(s) used, to guide the search out of local minima. With the ability to escape local minima easily, the effect of dimensionality becomes less pronounced. On the theoretical side, we provide guarantees on completeness and bounds on suboptimality of the solution found. We compare our proposed method with the recently published Multi-Heuristic A * search, and the popular RRT-Connect in a full-body mobile manipulation domain for the PR2 robot, and show its benefits over these approaches. I

BCI Control of Heuristic Search Algorithms

The ability to develop Brain-Computer Interfaces (BCI) to Intelligent Systems would offer new perspectives in terms of human supervision of complex Artificial Intelligence (AI) systems, as well as supporting new types of applications. In this article, we introduce a basic mechanism for the control of heuristic search through fNIRS-based BCI. The rationale is that heuristic search is not only a basic AI mechanism but also one still at the heart of many different AI systems. We investigate how users’ mental disposition can be harnessed to influence the performance of heuristic search algorithm through a mechanism of precision-complexity exchange. From a system perspective, we use weighted variants of the A? algorithm which have an ability to provide faster, albeit suboptimal solutions. We use recent results in affective BCI to capture a BCI signal, which is indicative of a compatible mental disposition in the user. It has been established that Prefrontal Cortex (PFC) asymmetry is strongly correlated to motivational dispositions and results anticipation, such as approach or even risk-taking, and that this asymmetry is amenable to Neurofeedback (NF) control. Since PFC asymmetry is accessible through fNIRS, we designed a BCI paradigm in which users vary their PFC asymmetry through NF during heuristic search tasks, resulting in faster solutions. This is achieved through mapping the PFC asymmetry value onto the dynamic weighting parameter of the weighted A* (WA*) algorithm. We illustrate this approach through two different experiments, one based on solving 8-puzzle configurations, and the other on path planning. In both experiments, subjects were able to speed up the computation of a solution through a reduction of search space in WA?. Our results establish the ability of subjects to intervene in heuristic search progression, with effects which are commensurate to their control of PFC asymmetry: this opens the way to new mechanisms for the implementation of hybrid cognitive systems

Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal

In imitation learning for planning, parameters of heuristic functions are optimized against a set of solved problem instances. This work revisits the necessary and sufficient conditions of strictly optimally efficient heuristics for forward search algorithms, mainly A* and greedy best-first search, which expand only states on the returned optimal path. It then proposes a family of loss functions based on ranking tailored for a given variant of the forward search algorithm. Furthermore, from a learning theory point of view, it discusses why optimizing cost-to-goal \hstar\ is unnecessarily difficult. The experimental comparison on a diverse set of problems unequivocally supports the derived theory.Comment: 10 page

A motivational model of BCI-controlled heuristic search

Several researchers have proposed a new application for human augmentation, which is to provide human supervision to autonomous artificial intelligence (AI) systems. In this paper, we introduce a framework to implement this proposal, which consists of using Brain–Computer Interfaces (BCI) to influence AI computation via some of their core algorithmic components, such as heuristic search. Our framework is based on a joint analysis of philosophical proposals characterising the behaviour of autonomous AI systems and recent research in cognitive neuroscience that support the design of appropriate BCI. Our framework is defined as a motivational approach, which, on the AI side, influences the shape of the solution produced by heuristic search using a BCI motivational signal reflecting the user’s disposition towards the anticipated result. The actual mapping is based on a measure of prefrontal asymmetry, which is translated into a non-admissible variant of the heuristic function. Finally, we discuss results from a proof-of-concept experiment using functional near-infrared spectroscopy (fNIRS) to capture prefrontal asymmetry and control the progression of AI computation of traditional heuristic search problems

Analisa Penggunaan Nilai Bobot Heuristik yang Berbeda pada Algoritma Weighted A*

Path planning is a sequence of states to move objects from the initial state to the final state and avoid impassable areas. Objects here can be robots, autonomous cars, and others. The A* algorithm is a path search algorithm that uses distance estimation by using the closest path search to reach the destination. Weighted A* is an algorithm used to solve the pathfinding problem by changing the weight value in the heuristic function. The purpose of this study is to analyze the comparison of the Weighted A* algorithm with the A* algorithm and analyze the effect of the heuristic weight value on the Weighted A* algorithm. The tests carried out are using maze, narrow, trap, clutter environments. The results obtained in the comparison of the Weighted A* and A* algorithms, from the test results, the Weighted A* algorithm produces a better search time of 0.33 seconds, while the A* algorithm produces a time of 1.40 seconds. But the A* algorithm can produce a more optimal path of 163.69 than the Weighted A* algorithm which produces a path of 164.52. With a strategy that emphasizes choosing nodes that are closer to the goal node, Weighted A* can produce a path with a faster computation time. While the A* algorithm because it chooses the node with the smallest heuristic value, it can produce a more optimal path. Weighted A* is suitable to be implemented on systems that require shorter path-finding times but do not have to be optimal. The A* algorithm is suitable to be implemented in systems that require optimal paths even though the search time is not too fastPath planning merupakan urutan keadaan untuk memindahkan objek dari keadaan awal ke keadaan akhir, serta menghindari daerah yang tidak dapat dilalui. Objek disini dapat berupa robot, mobil otonom dan yang lainnya. Algoritma A* merupakan algoritma pencarian jalur yang menggunakan estimasi jarak dengan menggunakan pencarian jalur terdekat untuk mencapai tujuan. Weighted A* adalah algoritma yang digunakan untuk memecahkan masalah pencarian jalur dengan mengubah nilai bobot pada fungsi heuristiknya. Tujuan dari penelitian ini yaitu menganalisa perbandingan algoritma Weighted A* dengan algoritma A*, serta menganalisa pengaruh nilai bobot heuristik pada algoritma Weighted A*. Pengujian yang dilakukan yaitu menggunakan lingkungan maze, narrow, trap, clutter. Hasil yang didapat pada perbandingan algoritma Weighted A* dan A*, dari hasil pengujian diperoleh algoritma Weighted A* menghasilkan waktu pencarian yang lebih baik yaitu sebesar 0,33 detik, sedangkan algoritma A* menghasilkan waktu 1,40 detik. Tetapi algoritma A* dapat menghasilkan jalur yang lebih optimal yaitu 163,69 dibandingkan algoritma Weighted A* yang menghasilkan jalur sebesar 164,52. Dengan strategi yang lebih menekankan pemilihan node yang lebih dekat dengan node goal, maka Weighted A* dapat menghasilkan jalur dengan waktu komputasi yang lebih cepat. Sedangkan algoritma A* karena memilih node dengan nilai heuristik terkecil, maka dapat menghasilkan jalur yang lebih optimal. Weighted A* cocok di implementasikan pada sistem yang membutuhkan waktu pencarian jalur yang lebih singkat tapi tidak harus optimal. Algoritma A* cocok di implementasikan pada sistem yang membutuhkan jalur optimal walaupun waktu pencariannya tidak terlalu cepa

Motion Planning For Micro Aerial Vehicles

A Micro Aerial Vehicle (MAV) is capable of agile motion in 3D making it an ideal platform for developments of planning and control algorithms. For fully autonomous MAV systems, it is essential to plan motions that are both dynamically feasible and collision-free in cluttered environments. Recent work demonstrates precise control of MAVs using time-parameterized trajectories that satisfy feasibility and safety requirements. However, planning such trajectories is non-trivial, especially when considering constraints, such as optimality and completeness. For navigating in unknown environments, the capability for fast re-planning is also critical. Considering all of these requirements, motion planning for MAVs is a challenging problem. In this thesis, we examine trajectory planning algorithms for MAVs and present methodologies that solve a wide range of planning problems. We first introduce path planning and geometric control methods, which produce spatial paths that are inadequate for high speed flight, but can be used to guide trajectory optimization. We then describe optimization-based trajectory planning and demonstrate this method for solving navigation problems in complex 3D environments. When the initial state is not fixed, an optimization-based method is prone to generate sub-optimal trajectories. To address this challenge, we propose a search-based approach using motion primitives to plan resolution complete and sub-optimal trajectories. This algorithm can also be used to solve planning problems with constraints such as motion uncertainty, limited field-of-view and moving obstacles. The proposed methods can run in real time and are applicable for real-world autonomous navigation, even with limited on-board computational resources. This thesis includes a carefully analysis of the strengths and weaknesses of our planning paradigm and algorithms, and demonstration of their performance through simulation and experiments

Metareasoning for Planning and Execution in Autonomous Systems

Metareasoning is the process by which an autonomous system optimizes, specifically monitors and controls, its own planning and execution processes in order to operate more effectively in its environment. As autonomous systems rapidly grow in sophistication and autonomy, the need for metareasoning has become critical for efficient and reliable operation in noisy, stochastic, unstructured domains for long periods of time. This is due to the uncertainty over the limitations of their reasoning capabilities and the range of their potential circumstances. However, despite considerable progress in metareasoning as a whole over the last thirty years, work on metareasoning for planning relies on several assumptions that diminish its accuracy and practical utility in autonomous systems that operate in the real world while work on metareasoning for execution has not seen much attention yet. This dissertation therefore proposes more effective metareasoning for planning while expanding the scope of metareasoning to execution to improve the efficiency of planning and the reliability of execution in autonomous systems. In particular, we offer a two-pronged framework that introduces metareasoning for efficient planning and reliable execution in autonomous systems. We begin by proposing two forms of metareasoning for efficient planning: (1) a method that determines when to interrupt an anytime algorithm and act on the current solution by using online performance prediction and (2) a method that tunes the hyperparameters of the anytime algorithm at runtime by using deep reinforcement learning. We then propose two forms of metareasoning for reliable execution: (3) a method that recovers from exceptions that can be encountered during operation by using belief space planning and (4) a method that maintains and restores safety during operation by using probabilistic planning

Implementação de algoritmo de planejamento de trajetória com robô diferencial

Neste trabalho estão descritas a implementação e a análise de um algoritmo de plane- jamento de trajetória, utilizando como base o Algoritmo A-Star Search, e algumas de suas variações, para encontrar o caminho de menor custo, bem como evitar obstáculos, em um mapa previamente conhecido, representado por uma matriz, cujas células seriam interpretadas como nós de um grafo pelo algoritmo. A implementação foi realizada por meio de um um robô diferencial, com a movimentação em 4 direções, controlado por uma Raspberry PI 1 Model B Rev 2, utilizando Algoritmos de Inteligência Artificial