We introduce ET-Former, a novel end-to-end algorithm for semantic scene completion using a single monocular camera. Our approach generates a semantic occupancy map from single RGB observation while simultaneously providing uncertainty estimates for semantic predictions. By designing a triplane-based deformable attention mechanism, our approach improves geometric understanding of the scene than other SOTA approaches and reduces noise in semantic predictions. Additionally, through the use of a Conditional Variational AutoEncoder (CVAE), we estimate the uncertainties of these predictions. The generated semantic and uncertainty maps will help formulate navigation strategies that facilitate safe and permissible decision making in the future. Evaluated on the Semantic-KITTI dataset, ET-Former achieves the highest Intersection over Union (IoU) and mean IoU (mIoU) scores while maintaining the lowest GPU memory usage, surpassing state-of-the-art (SOTA) methods. It improves the SOTA scores of IoU from 44.71 to 51.49 and mIoU from 15.04 to 16.30 on SeamnticKITTI test, with a notably low training memory consumption of 10.9 GB, achieving at least a 25% reduction compared to previous methods. Project page: https://github.com/amazon-science/ET-Former.

In this paper, we present a probabilistic and unconstrained model predictive control formulation for robot navigation under uncertainty. We present (1) a closed-form approximation of the probability of collision that naturally models the propagation of uncertainty over the planning horizon and is computationally cheap to evaluate, and (2) a collision-cost formulation which provably preserves forward invariance (i.e., keeps the robot away from obstacles) when combined with the probability formulation. Notably, our formulation avoids hard constraints by construction, which in turn avoids abrupt transitions in robot behavior around the constraint boundaries ensuring graceful navigation. Further, we present proof for the forward invariance and the stability of the approach. We compare the efficacy of our method with the baseline [1], which the proposed approach builds on. We demonstrate that the approach results in confident and safe robot navigation in tight spaces by smoothly slowing down the robot in low survivability environments (e.g., tight corridors), but also allows it to move away from obstacles safely when needed.

When a mobile robot autonomously explores an indoor space to produce a localization and navigation map, it is important to create both a stable pose graph and a high-quality occupancy map that covers all the navigable areas. In this work, we propose a novel probabilistic active loop closure framework which attempts to maximally reduce pose graph uncertainty during exploration and improves occupancy map quality. We calculate a probabilistic reward of getting a loop closure at any pose on a pose graph, which considers both how much pose graph uncertainty would be reduced by getting a loop closure there, and the robot’s travel cost to navigate to that pose. By choosing poses that provide the largest rewards, we can maximally reduce pose graph uncertainty while avoiding long travel times. The effectiveness of the method is illustrated through on-device testing in various floor plans.

We study the impact of nonlinear saturation effect and receiver architecture on the performance of multiple-input and multiple-output (MIMO) wireless power transfer (WPT). We propose a generic hybrid combining receiver architecture where the receive antennas are grouped into multiple subarrays, each subarray consisting of multiple energy harvesting (EH) circuits. For efficient receiver reconfiguration, a power splitter is placed in between each antenna subarray and the associated EH circuits. To improve the WPT performance, we formulate an optimization problem for joint transmit and receive beamforming design along with receiver reconfiguration, which appears non-convex fractional programming. To resolve this, the optimization problem is reformulated into equivalent semi-definite programming problem with rank-one relaxation through quadratic transform, which provides a suboptimal solution for the original problem. Then, we propose an iterative algorithm for optimizing the joint beamforming design and receiver reconfiguration. We confirm that the WPT system optimization considering the nonlinearities existing in the proposed generic receiver structure yields better performance than when the nonlinearities and resulting receiver architecture are not taken into account.

Autonomous exploration to build a map of an unknown environment is a fundamental robotics problem. However, the quality of the map directly influences the quality of subsequent robot operation. Instability in a simultaneous localization and mapping (SLAM) system can lead to poor-quality maps and subsequent navigation failures during or after exploration. This becomes particularly noticeable in consumer robotics, where compute budget and limited field-of-view are very common. In this work, we propose (i) the concept of lighthouses: panoramic views with high visual information content that can be used to maintain the stability of the map locally in their neighborhoods and (ii) the final stabilization strategy for global pose graph stabilization. We call our novel exploration strategy SLAM-aware exploration (SAE) and evaluate its performance on real-world home environments.

We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We present a novel trajectory cost formulation that significantly reduces conservative and deadlocking behaviors and generates smooth trajectories. In particular, we propose a new collision probability function that effectively captures the risk associated with a given configuration and the time to avoid collisions based on the velocity direction. Moreover, we propose a terminal state cost based on the expected time-to-goal and time-to-collision values that helps in avoiding trajectories that could result in deadlock. We evaluate our cost formulation in multiple simulated scenarios, including narrow corridors with dynamic obstacles, and observe significantly improved navigation behavior and reduced deadlocks as compared to prior methods.

In this paper, we propose a unified simultaneous wireless information and power transfer (SWIPT) signal and its architecture design in order to take advantage of both single tone and multi-tone signaling by adjusting only the power allocation ratio of a unified signal. For this, we design a novel unified and integrated receiver architecture for the proposed unified SWIPT signaling, which consumes low power with an envelope detection. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition for low-power Internet-of-Things (IoT) devices. This will facilitate effective deployment of low-power IoT networks that concurrently supply both information and energy wirelessly to the devices by using the proposed unified SWIPT signaling and architecture.

In this article, we propose a unified simultaneous wireless information and power transfer (SWIPT) signaling and architecture to take advantage of both single tone and multitone signaling by adjusting only the power allocation ratio of a unified signal. Toward this, we design a unified and integrated receiver architecture for the proposed unified SWIPT signaling via an envelope detection with low power consumption. To lower the computational complexity of the receiver, we propose an adaptive control algorithm where the transmitter adjusts the communication mode through temporal convolutional network (TCN)-basedasymmetric processing. To this end, the transmitter optimizes the modulation index and power allocation ratio in short-term scale while updating the mode switching threshold in long-term scale. We demonstrate that the proposed unified SWIPT system improves the achievable rate under the self-powering condition at low-power Internet of Things (IoT) devices. Consequently we will facilitate the effective deployment of low-power IoT networks that concurrently supply both information and energywirelesslyto the devices by using the proposed unified SWIPT and adaptive control algorithm at the transmitter side.

Autonomous mobile robots need maps for effective, safe navigation, and SLAM in general is still an unsolved problem. Nonetheless, certain combinations of environmental characteristics and sensors admit tractable solutions. In particular, detection and tracking of linear features such as line segments (2D) or planar facets (3D) has been proven robust in many man-made environments. However, these types of features produce rank-deficient constraints, which create challenges for graph-based SLAM optimizers. We present techniques for using rank-deficient features and constraints more robustly by analyzing the approximate null-space of the constraints for each node in the factor graph representing the trajectory. We also extend auxiliary methods for correspondence calculations and map update routines, the combination of which yields state-of-the-art performance for a rank-deficient SLAM system. We present results from quantitative experiments comparing memory use, compute load, accuracy, and robustness for several ablation tests on real and simulated data.

We propose a heterogeneously reconfigurable energy harvester which contains multiple energy harvesting (EH) blocks, each comprised of multiple EH circuits in parallel to be optimized for different input power ranges. Practical EH circuits have their own favorable input power range due to turn-on sensitivity and saturation effects. To account for this, we present the necessary conditions for efficient configuration of an energy harvester, and then propose an algorithm for optimal reconfiguration of the energy harvester. Furthermore, adaptive time switching and mode switching problems are formulated in order to maximize the average achievable rate under the self-powering condition. Our proposed energy harvester and algorithm can be applied to low-power Internet-of-Things (IoT) devices with wireless EH capability for self-sustainability, which will be a key enabler for realizing a battery-free IoT network.

In this article, we propose a dual-mode simultaneous wireless information and power transfer (SWIPT) system with a deep-learning-based adaptive mode switching (MS) algorithm to exploit both advantages of single-tone and multitone SWIPT. For self-powering of low-energy Internet-of-Things (IoT) devices, a duty-cycling operation is used with nonlinear energy harvesting. For this, we employ a new energy-assisted single-tone modulation which simplifies the receiver structure for information decoding. Considering the symbol-error rate performance, we formulate an adaptive MS problem to maximize the achievable rate under the energy-causality constraint by adjusting the MS threshold. To relieve the computational burden of the receiver, we introduce asymmetric processing for adaptive MS, for which the transmitter adapts the communication mode based on the feedback from the receiver. We invoke deep learning for adaptive MS at the transmitter that iteratively updates the MS threshold in a long-term scale via deep long short-term memory (LSTM) recurrent neural network (RNN) while deciding on the communication mode and modulation index in a short-term scale. We demonstrate the achievable rate improvement under an energy-neutral operation while providing interesting insights into designing the adaptive MS algorithm for the dual-mode SWIPT system.

This paper proposes a new reconfigurable energy harvester for simultaneous wireless information and power transfer (SWIPT), where the energy harvester has multiple energy harvesting (EH) circuits in parallel and is adaptively reconfigured depending on the received power. Unlike the existing ideal linear EH model, where EH efficiency is a fixed constant regardless of the received power, we consider the practical nonlinear EH model. With the new reconfigurable energy harvester, we study adaptive mode switching between information decoding and EH over the fading channel. We optimize the problem of maximizing the average achievable rate under the energy causality condition, namely an average harvested energy constraint. Then we evaluate the rate-energy tradeoff for the proposed reconfigurable energy harvester. Numerical results demonstrate considerable performance gains through the proposed energy harvester. This implies that the SWIPT receiver can be self-powered effectively with the proposed one while meeting the energy causality safely.

In this paper, we propose a dual mode simultaneous wireless information and power transfer (SWIPT) system in which a sensor node monitors the received power and adaptively controls the communication mode. To this end, we design two types of single and multi-tone waveforms and propose a new transceiver architecture that realizes adaptive power management and information decoding (adaptive PM-&-ID) module. On top of this, we implement adaptive PM-&-ID policy algorithm which reflects non-linear energy harvesting (EH) model for both single and multi-tone waveforms. Our newly designed SWIPT can be applied to Internet-of-Things (IoT) network with RF EH capability for self-powering, leading to battery-free network. Numerical results show that significant performance gain can be achieved by the proposed dual mode SWIPT over the existing SWIPT design.

Fast and high-fidelity dynamic model is very useful for planning, control, and estimation. Here, we present a fixed-time-step, discrete-time dynamic model of differential-drive vehicle with friction for reliable velocity prediction, which is fast, stable, and easy to calibrate. Unlike existing methods which are predominantly formulated in the continuous-time domain (very often ignoring dry friction) that require numerical solver for digital implementation, our model is formulated directly in a fixed-time-step discrete-time setting, which greatly simplifies the implementation and minimizes computational cost. We also explicitly take into account friction, using the stable formulation developed by Kikuuwe [1]. Friction model, while non-trivial to implement, is necessary for predicting wheel locks and velocity steady-states which occur in real physical systems. In this paper, we present our dynamic model and evaluate it on a physical platform, a commercially-available electric powered wheelchair. We show that our model, which can run over 105times faster than real-time on a typical laptop, can accurately predict linear and angular velocities without drift. The calibration of our model requires only a time-series of wheel speed measurements (via encoders) and command inputs, making it readily deployable to physical mobile robots.

Here we present a non-holonomic distance function for unicycle-type vehicles, and use this distance function to extend the optimal path planner RRT* to handle nonholonomic constraints. The critical feature of our proposed distance function is that it is also a control-Lyapunov function. We show that this allows us to construct feedback policies that stabilizes the system to a target pose, and to generate the optimal path that respects the non-holonomic constraints of the system via the non-holonomic RRT*. The composition of the Lyapunov function that is obtained as a result of this planning process provides stabilizing feedback and the cost-to-go to the final destination in the neighborhood of the planned path, adding much flexibility and robustness to the plan.

We describe a new algorithm for occupancy grid mapping using LIDAR in the presence of glass and other non-diffuse surfaces. This is a major problem for robot navigation in many indoor environments due to the prevalence of glass paned doors, windows, and even glass walls, as well as mirrors and polished metal surfaces. Current formulations of occupancy grid mapping make the assumption that objects in the environment are detectable from all angles. However, glass and other specular surfaces are invisible to LIDAR at most angles and so become washed out as “noise”. We modify the standard occupancy grid algorithm to allow for mapping objects that are only visible from certain view angles, by tracking the subset of angles from which objects are reliably visible. We show that these angles can be determined reliably with a single pass through the environment, and that the information can be used to map both diffuse and specular surfaces.

The ability to follow or move alongside a person is a necessary skill for an autonomous mobile agent that works with human users. To accomplish the task, the robot must be able to track and follow the person it is accompanying while maneuvering through obstacles without collision. Also, the robot must be able to respect user preferences and exhibit behaviors that are intuitive and socially acceptable. That is, the robot is required to make complex decisions on-line, in environments that are almost always dynamic and uncertain in the presence of pedestrians. This paper discusses a versatile motion planning algorithm for person pacing, which refers to the capability to walk next to another person at user-preferred distance and orientation [1]. The algorithm is based on the Model Predictive Equilibrium Point Control (MPEPC) framework [2] which allows a robot to navigate gracefully in dynamic, uncertain, and structured environments. We show that with a simple task description for person pacing, an agent with the MPEPC navigation algorithm can make intelligent decisions on-line, maximizing the expected progress toward achieving the task while minimizing the action cost and the probability of collision. We present navigation examples generated from real data traces, where a wheelchair robot exhibits very reasonable behaviors across a wide range of situations.

An autonomous vehicle intended to carry passengers must be able to generate trajectories on-line that are safe, smooth and comfortable. Here, we propose a strategy for robot navigation in a structured, dynamic indoor environment, where the robot reasons about the near future and makes a locally optimal decision at each time step.

Although recent progress in 2D mobile robot navigation has been significant, the great majority of existing work focuses only on ensuring that the robot reaches its goal. But to make autonomous navigation truly successful, the “quality” of planned motion is important as well. Here, we develop and analyze a pose-following kinematic control law applicable to unicycle-type robots, such that the robot can generate intuitive, fast, smooth, and comfortable trajectories. The Lyapunov-based feedback control law is derived via singular perturbation. It is made up of three components: (i) egocentric polar coordinates with respect to an observer on the vehicle, (ii) a slow subsystem which describes the position of the vehicle, where the reference heading is obtained via state feedback, and (iii) a fast subsystem which describes the steering of the vehicle, where the vehicle heading is exponentially stabilized to the obtained reference heading. The resulting path is a smooth and intuitive curve, globally converging to an arbitrary target pose without singularities, from any given initial pose. Furthermore, we present a simple path following strategy based on the proposed control law to satisfy arbitrary velocity, acceleration and jerk bounds imposed by the user. Such requirements are important to any autonomous vehicle so as to avoid actuator overload and to make the path physically realizable, and they are critical for applications like autonomous wheelchairs where passengers can be physically fragile.