Sanjiban Choudhury

Sparse Tangential Network (SPARTAN): Motion planning for micro aerial vehicles

Micro aerial vehicles operating outdoors must be able to maneuver through both dense vegetation and across empty fields. Existing approaches do not exploit the nature of such an environment. We have designed an algorithm which plans rapidly through free space and is efficiently guided around obstacles. In this paper we present SPARTAN (Sparse Tangential Network) as an approach to create a sparsely connected graph across a tangential surface around obstacles. We find that SPARTAN can navigate a vehicle autonomously through an outdoor environment producing plans 172 times faster than the state of the art (RRT*). As a result SPARTAN can reliably deliver safe plans, with low latency, using the limited computational resources of a lightweight aerial vehicle.

Autonomous Exploration and Motion Planning for an Unmanned Aerial Vehicle Navigating Rivers

Mapping a rivers geometry provides valuable information to help understand the topology and health of an environment and deduce other attributes such as which types of surface vessels could traverse the river. While many rivers can be mapped from satellite imagery, smaller rivers that pass through dense vegetation are occluded. We develop a micro air vehicle MAV that operates beneath the tree line, detects and maps the river, and plans paths around three-dimensional 3D obstacles such as overhanging tree branches to navigate rivers purely with onboard sensing, with no GPS and no prior map. We present the two enabling algorithms for exploration and for 3D motion planning. We extract high-level goal-points using a novel exploration algorithm that uses multiple layers of information to maximize the length of the river that is explored during a mission. We also present an efficient modification to the SPARTAN Sparse Tangential Network algorithm called SPARTAN-lite, which exploits geodesic properties on smooth manifolds of a tangential surface around obstacles to plan rapidly through free space. Using limited onboard resources, the exploration and planning algorithms together compute trajectories through complex unstructured and unknown terrain, a capability rarely demonstrated by flying vehicles operating over rivers or over ground. We evaluate our approach against commonly employed algorithms and compare guidance decisions made by our system to those made by a human piloting a boat carrying our system over multiple kilometers. We also present fully autonomous flights on riverine environments generating 3D maps over several hundred-meter stretches of tight winding rivers.

Regionally accelerated batch informed trees (RABIT*): A framework to integrate local information into optimal path planning

Sampling-based optimal planners, such as RRT*, almost-surely converge asymptotically to the optimal solution, but have provably slow convergence rates in high dimensions. This is because their commitment to finding the global optimum compels them to prioritize exploration of the entire problem domain even as its size grows exponentially. Optimization techniques, such as CHOMP, have fast convergence on these problems but only to local optima. This is because they are exploitative, prioritizing the immediate improvement of a path even though this may not find the global optimum of nonconvex cost functions. In this paper, we present a hybrid technique that integrates the benefits of both methods into a single search. A key insight is that applying local optimization to a subset of edges likely to improve the solution avoids the prohibitive cost of optimizing every edge in a global search. This is made possible by Batch Informed Trees (BIT*), an informed global technique that orders its search by potential solution quality. In our algorithm, Regionally Accelerated BIT* (RABIT*), we extend BIT* by using optimization to exploit local domain information and find alternative connections for edges in collision and accelerate the search. This improves search performance in problems with difficult-to-sample homotopy classes (e.g., narrow passages) while maintaining almost-sure asymptotic convergence to the global optimum. Our experiments on simulated random worlds and real data from an autonomous helicopter show that on certain difficult problems, RABIT* converges 1.8 times faster than BIT*. Qualitatively, in problems with difficult-to-sample homotopy classes, we show that RABIT* is able to efficiently transform paths to avoid obstacles.

RRT*-AR: Sampling-based alternate routes planning with applications to autonomous emergency landing of a helicopter

Engine malfunctions during helicopter flight poses a large risk to pilot and crew. Without a quick and coordinated reaction, such situations lead to a complete loss of control. An autonomous landing system could react quicker to regain control, however current emergency landing methods only generate dynamically feasible trajectories without considering obstacles. We address the problem of autonomously landing a helicopter while considering a realistic context: multiple potential landing zones, geographical terrain, sensor limitations and pilot contextual knowledge. We designed a planning system to generate alternate routes (AR) that respect these factors till touchdown exploiting the human-in-loop to make a choice. This paper presents an algorithm, RRT*-AR, building upon the optimal sampling-based algorithm RRT* to generate AR in realtime and examines its performance for simulated failures occurring in mountainous terrain, while maintaining optimality guarantees. After over 4500 trials, RRT*-AR outperformed RRT* by providing the human 280% more options 67% faster on average. As a result, it provides a much wider safety margin for unaccounted disturbances, and a more secure environment for a pilot. Using AR, the focus can now shift on delivering safety guarantees and handling uncertainties in these situations.

Emergency maneuver library - ensuring safe navigation in partially known environments

Autonomous mobile robots are required to operate in partially known and unstructured environments. It is imperative to guarantee safety of such systems for their successful deployment. Current state of the art does not fully exploit the sensor and dynamic capabilities of a robot. Also, given the non-holonomic systems with non-linear dynamic constraints, it becomes computationally infeasible to find an optimal solution if the full dynamics are to be exploited online. In this paper we present an online algorithm to guarantee the safety of the robot through an emergency maneuver library. The maneuvers in the emergency maneuver library are optimized such that the probability of finding an emergency maneuver that lies in the known obstacle free space is maximized. We prove that the related trajectory set diversity problem is monotonic and sub-modular which enables one to develop an efficient trajectory set generation algorithm with bounded sub-optimality. We generate an off-line computed trajectory set that exploits the full dynamics of the robot and the known obstacle-free region. We test and validate the algorithm on a full-size autonomous helicopter flying up to speeds of 56m/s in partially-known environments. We present results from 4 months of flight testing where the helicopter has been avoiding trees, performing autonomous landing, avoiding mountains while being guaranteed safe.

Learning to gather information via imitation

The budgeted information gathering problem — where a robot with a fixed fuel budget is required to maximize the amount of information gathered from the world — appears in practice across a wide range of applications in autonomous exploration and inspection with mobile robots. Although there is an extensive amount of prior work investigating effective approximations of the problem, these methods do not address the fact that their performance is heavily dependent on distribution of objects in the world. In this paper, we attempt to address this issue by proposing a novel data-driven imitation learning framework. We present an efficient algorithm, EXPLORE, that trains a policy on the target distribution to imitate a clairvoyant oracle — an oracle that has full information about the world and computes non-myopic solutions to maximize information gathered. We validate the approach on a spectrum of results on a number of 2D and 3D exploration problems that demonstrates the ability of EXPLORE to adapt to different object distributions. Additionally, our analysis provides theoretical insight into the behavior of EXPLORE. Our approach paves the way forward for efficiently applying data-driven methods to the domain of information gathering.

The Dynamics Projection Filter (DPF) - real-time nonlinear trajectory optimization using projection operators

Robotic navigation applications often require on-line generation of trajectories that respect underactuated non-linear dynamics, while optimizing a cost function that depends only on a low-dimensional workspace (collision avoidance). Approaches to non-linear optimization, such as differential dynamic programming (DDP), suffer from the drawbacks of slow convergence by being limited to stay within the trust-region of the linearized dynamics and having to integrate the dynamics with fine granularity at each iteration. We address the problem of decoupling the workspace optimization from the enforcement of non-linear constraints. In this paper, we introduce the Dynamics Projection Filter, a nonlinear projection operator based approach that first optimizes a workspace trajectory with reduced constraints and then projects (filters) it to a feasible configuration space trajectory that has a bounded sub-optimality guarantee. We show simulation results for various curvature and curvature-derivatives constrained systems, where the dynamics projection filter is able to, on average, produce similar quality solution 50 times faster than DDP. We also show results from flight tests on an autonomous helicopter that solved these problems on-line while avoiding mountains at high speed as well as trees and buildings as it came in to land.

A κITE in the wind: Smooth trajectory optimization in a moving reference frame

A significant challenge for unmanned aerial vehicles capable of flying long distances is planning in a wind field. Although there has been a plethora of work on the individual topics of planning long routes, smooth trajectory optimization and planning in a wind field, it is difficult for these methods to scale to solve the combined problem. In this paper, we address the problem of planning long, dynamically feasible, time-optimal trajectories in the presence of wind (which creates a moving reference frame). We present an algorithm, κITE, that elegantly decouples the joint trajectory optimization problem into individual path optimization in a fixed ground frame and a velocity profile optimization in a moving reference frame. The key idea is to derive a decoupling framework that guarantees feasibility of the final fused trajectory. Our results show that κITE is able to produce high-quality solutions for planning with a helicopter flying at speeds of 50 m/s, handling winds up to 20 m/s and missions over 200 km. We validate our approach with real-world experiments on a full-scale helicopter with a pilot in the loop. Our approach paves the way forward for autonomous systems to exhibit pilot-like behavior when flying missions in winds aloft.

List prediction applied to motion planning

There is growing interest in applying machine learning to motion planning. Potential applications are predicting an initial seed for trajectory optimization, predicting an effective heuristic for search based planning, and even predicting a planning algorithm for adaptive motion planning systems. In these situations, providing only a single prediction is unsatisfactory. It leads to many scenarios where the prediction suffers a high loss. In this paper, we advocate list prediction. Each predictor in a list focusses on different regions in the space of environments. This overcomes the shortcoming of a single predictor, and improves overall performance. A framework for list prediction, ConseqOpt, already exists. Our contribution is an extensive domain-specific treatment. We provide a rigorous and clear exposition of the procedure for training a list of predictors. We provide experimental results on a spectrum of motion planning applications. Each application contributes to understanding the behavior of list prediction. We observe that the benefit of list prediction over a single prediction is significant, irrespective of the application.

Theoretical Limits of Speed and Resolution for Kinodynamic Planning in a Poisson Forest

The performance of a state lattice motion planning algorithm depends critically on the resolution of the lattice to ensure a balance between solution quality and computation time. There is currently no theoretical basis for selecting the resolution because of its dependence on the robot dynamics and the distribution of obstacles. In this paper, we examine the problem of motion planning on a resolution constrained lattice for a robot with non-linear dynamics operating in an environment with randomly generated disc shaped obstacles sampled from a homogeneous Poisson process. We present a unified framework for computing explicit solutions to two problems i) the critical planning resolution which guarantees the existence of an infinite collision free trajectory in the search graph ii) the critical speed limit which guarantees infinite collision free motion. In contrast to techniques used by Karaman and Frazzoli [11], we use a novel approach that maps the problem to parameters of directed asymmetric hexagonal lattice bond percolation. Since standard percolation theory offers no results for this lattice, we map the lattice to an infinite absorbing Markov chain and use results pertaining to its survival to obtain bounds on the parameters. As a result, we are able to derive theoretical expressions that relate the non-linear dynamics of a robot, the resolution of the search graph and the density of the Poisson process. We validate the theoretical bounds using Monte-Carlo simulations for single integrator and curvature constrained systems and are able to validate the previous results presented by Karaman and Frazzoli [11] independently using the novel connections introduced in this paper.