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Abstract The objective of this paper is to present a full-stack, real-time motion planning framework for kinodynamic robots and then show how it is applied and demonstrated on a physical quadrotor system operating in a laboratory environment. The proposed framework utilizes an offline–online computation paradigm, neighborhood classification through machine learning, sampling-based motion planning with an optimal cost distance metric, and trajectory smoothing to achieve real-time planning for aerial vehicles. This framework accounts for dynamic obstacles with an event-based replanning structure and a locally reactive control layer that minimizes replanning events. The approach is demonstrated on a quadrotor navigating moving obstacles in an indoor space and stands as, arguably, one of the first demonstrations of full-online kinodynamic motion planning, with execution cycles of 3 Hz to 5 Hz. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory. Highlights • Full-stack framework for path planning and obstacle avoidance for agile robots. • Demonstrated on a quadrotor capable of dodging a fencing blade while flying indoors. • Fusion of sampling-based planning, machine learning, and reactive control. [ABSTRACT FROM AUTHOR]

Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road: a key challenge in doing so is accounting for uncertainty in human driver actions without unduly impacting planner performance. This article introduces a minimally interventional safety controller operating within an autonomous vehicle control stack with the role of ensuring collision-free interaction with an externally controlled (e.g., human-driven) counterpart while respecting static obstacles such as a road boundary wall. We leverage reachability analysis to construct a real-time (100 Hz) controller that serves the dual role of (i) tracking an input trajectory from a higher-level planning algorithm using model predictive control, and (ii) assuring safety by maintaining the availability of a collision-free escape maneuver as a persistent constraint regardless of whatever future actions the other car takes. A full-scale steer-by-wire platform is used to conduct traffic-weaving experiments wherein two cars, initially side-by-side, must swap lanes in a limited amount of time and distance, emulating cars merging onto/off of a highway. We demonstrate that, with our control stack, the autonomous vehicle is able to avoid collision even when the other car defies the planner's expectations and takes dangerous actions, either carelessly or with the intent to collide, and otherwise deviates minimally from the planned trajectory to the extent required to maintain safety. [ABSTRACT FROM AUTHOR]

The operation of today's robots entails interactions with humans, e.g., in autonomous driving amidst human-driven vehicles. To effectively do so, robots must proactively decode the intent of humans and concurrently leverage this knowledge for safe, cooperative task satisfaction: a problem we refer to as proactive decision making. However, simultaneous intent decoding and robotic control requires reasoning over several possible human behavioral models, resulting in high-dimensional state trajectories. In this paper, we address the proactive decision-making problem using a novel combination of formal methods, control, and data mining techniques. First, we distill high-dimensional state trajectories of human–robot interaction into concise, symbolic behavioral summaries that can be learned from data. Second, we leverage formal methods to model high-level agent goals, safe interaction, and information-seeking behavior with temporal logic formulas. Finally, we design a novel decision-making scheme that maintains a belief distribution over models of human behavior, and proactively plans informative actions. After showing several desirable theoretical properties, we apply our framework to a dataset of humans driving in crowded merging scenarios. For it, temporal logic models are generated and used to synthesize control strategies using tree-based value iteration and deep reinforcement learning. In addition, we illustrate how data-driven models of human responses to informative robot probes, such as from generative models such as conditional variational autoencoders, can be clustered with formal specifications. Results from simulated self-driving car scenarios demonstrate that data-driven strategies enable safe interaction, correct model identification, and significant dimensionality reduction. [ABSTRACT FROM AUTHOR]