Onboard Inertial Measurement Unit Research Articles

Investigating gait-responsive somatosensory cueing from a wearable device to improve walking in Parkinson’s disease

Freezing-of-gait (FOG) and impaired walking are common features of Parkinson’s disease (PD). Provision of external stimuli (cueing) can improve gait, however, many cueing methods are simplistic, increase task loading or have limited utility in a real-world setting. Closed-loop (automated) somatosensory cueing systems have the potential to deliver personalised, discrete cues at the appropriate time, without requiring user input. Further development of cue delivery methods and FOG-detection are required to achieve this. In this feasibility study, we aimed to test if FOG-initiated vibration cues applied to the lower-leg via wearable devices can improve gait in PD, and to develop real-time FOG-detection algorithms. 17 participants with Parkinson’s disease and daily FOG were recruited. During 1 h study sessions, participants undertook 4 complex walking circuits, each with a different intervention: continuous rhythmic vibration cueing (CC), responsive cueing (RC; cues initiated by the research team in response to FOG), device worn with no cueing (NC), or no device (ND). Study sessions were grouped into 3 stages/blocks (A-C), separated by a gap of several weeks, enabling improvements to circuit design and the cueing device to be implemented. Video and onboard inertial measurement unit (IMU) data were analyzed for FOG events and gait metrics. RC significantly improved circuit completion times demonstrating improved overall performance across a range of walking activities. Step frequency was significantly enhanced by RC during stages B and C. During stage C, > 10 FOG events were recorded in 45% of participants without cueing (NC), which was significantly reduced by RC. A machine learning framework achieved 83% sensitivity and 80% specificity for FOG detection using IMU data. Together, these data support the feasibility of closed-loop cueing approaches coupling real-time FOG detection with responsive somatosensory lower-leg cueing to improve gait in PD.

An Improved Method for Swing State Estimation in Multirotor Slung Load Applications

A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.

A Bionics-Based Recovery Strategy for Micro Air Vehicles

Micro air vehicles' movements can be significantly affected by physical contact with unknown moving objects from a random direction. To address this problem, low-latency sensors (cameras or radars) and high-speed processors are installed in six directions of aerial vehicles. However, this method is limited by hardware costs, load and energy. In this paper, a fast recovery strategy for micro air vehicles is proposed, which is inspired by the recovery manoeuvre of mosquitoes hit by raindrops. When hit by falling raindrops, mosquitoes will descend along the falling direction of raindrops to gradually counteract the kinetic energy of the impact induced by the raindrops. During the descent, mosquitoes will gradually tilt to one side but move in the opposite direction, and finally deviate from the falling route of raindrops. To simulate the recovery strategy, a micro aerial robot called RobMos is designed. The RobMos is an over-actuated system that can decouple the position control from the attitude control. Using the recovery strategy, the robot can quickly recover to a stable attitude after being impacted by unknown moving objects. An onboard inertial measurement unit (IMU) is used to estimate the impact location. Finally, the prototype can bear the impact of objects that are two times its own weight. Moreover, the generalizability of this strategy is also discussed, and it is proved that the RobMos can even recover attitude after being hit by the unknown moving object in a lateral direction.

Fault-Tolerant Control of a Variable-Pitch Quadrotor under Actuator Loss of Effectiveness and Wind Perturbations

The actuator fault-tolerant control problem for a variable-pitch quadrotor is addressed under uncertain conditions. Following a model-based approach, the plant nonlinear dynamics are faced with a disturbance observer-based control and a sequential quadratic programming control allocation, where only kinematic data of the onboard inertial measurement unit are required for the fault-tolerant control, i.e., it does not require the measurement of the motor speed nor the current drawn by the actuators. In the case of almost horizontal wind, a single observer handles both faults and the external disturbance. The estimation of the wind is fed forward by the controller, while the actuator fault estimation is exploited in the control allocation layer, which copes with the variable-pitch nonlinear dynamics, thrust saturation, and rate limits. Numerical simulations in the presence of measurement noise show the capability of the scheme to handle multiple actuator faults in a windy environment.

A comparative field study of free-fall cone and sphere penetrometers in soft sediment

This paper describes the field performance of two instrumented free-fall penetrometers (FFPs), which offer the possibility of low cost rapid strength profiling without requiring the equipment associated with conventional CPTs or drilling and sampling. The devices considered are a free-fall cone penetrometer (FFcP) and an instrumented free-fall sphere (IFFS). The performance of the FFPs in assessing soil undrained shear strength was investigated and compared through field trials in the soft sediments of the Swan River, Perth, Western Australia. The penetrometers achieved impact velocities of 1.4–5.8 m/s from drop heights of 0.7–3.0 m above the riverbed, and embedded to depths of up to 1.6 times the FFP length (1.45 m) and 4.5 times the IFFS diameter (0.89 m). The FFP data provided the shear strength indirectly using the acceleration measured from an onboard inertial measurement unit. The accuracy depends on the assumptions made regarding the magnitude of dynamic bearing and shaft frictional resistance. Uncertainty associated with dynamic bearing resistance causes a ±10% and ±13% variation in the calculated strength for the IFFS and the FFcP, respectively. However, the equivalent uncertainty in dynamic shaft resistance can cause a variation in the calculated strength of up to ±76% for the FFcP. The FFcP has higher embedment potential than the IFFS, but the IFFS has the advantage of a simpler interpretation without needing to account for dynamic shaft resistance.

Real-time propeller fault detection for multirotor drones based on vibration data analysis

This article presents a Fault Detection (FD) method to deal with propeller faults on multirotor drones in real-time. Several solutions have been proposed in the literature, however, they depend on additional sensors and/or dedicated hardware to deal with heavy computational complexity. So, they cannot be implemented in off-the-shelf commercial devices, i.e., without the aid of additional on-board sensors and/or extra computational power. The proposed method, instead, requires the on-board Inertial Measurement Unit (IMU) data only: by combining Finite Impulse Response (FIR), together with sparse classifiers, only a subset of the features is actually needed online and the FD is thus feasible in real-time.Design and tests are based on real flight data from a hexarotor, equipped with a conventional ArduPilot-based controller. The classification accuracy in testing is up to 93.37% (98.21%) with a binary tree (Linear Support Vector Machine (LSVM)). Moreover, the space and time complexity of the proposed method is low: on a PixHawk Cube flight controller, it requires less than 2% of the cycle time, and can then run in real-time. Finally, the proposed fault detection solution is model-free and it can be easily generalized to other multirotor vehicles.

A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments.

Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.

Effect of blade aspect ratio on the performance tradeoff between figure of merit and bending-torsion dynamics of wind turbines with synthetic jets

In this experimental study, two three-bladed rotors with a blade aspect ratio of 7.79 and 9.74 representing a 25% increase were tested. Each blade contains an S809 airfoil with zero pre-built twist and 20 high-performance synthetic jet actuators distributed along the span. The rotor was tested at four rotor speeds, Ω of 250, 500, 750, and 1,000 revolutions per minute (RPM) and three collective blade pitch angles, θc of 2, 5, and 8 degrees. Rotor thrust was measured using a high-capacity load cell, and blade bending and torsion was measured at four equally spaced radial locations on the blade defined by r/R= 0.24, 0.48, 0.72, and 0.96 using four onboard inertial measurement units. The spanwise flow over the suction surface of the blade was measured using laser Doppler velocimetry (LDV) techniques. The local Reynolds number produced with the test rotor speeds at the measurement locations ranges from 8.31 × 104≤Re∞≤ 9.49 × 105. It was found that rotor aerodynamic efficiency as quantified by the figure of merit is directly linked to blade aspect ratio and the magnitude of spanwise flow over the blade. The rotor with a higher blade aspect ratio produces less overall spanwise flow and is more aerodynamically efficient, however the blade also generates larger mean and fluctuating bending and torsion displacements, which are undesirable for aeroelastic stability. The use of synthetic jets on the blade mitigates the root mean square of the bending and pitch displacements above Ω= 250 RPM and θc= 2°. It was found that at Ω= 1,000 RPM, the blade structures undergo a second mode bending response indicating a condition of structural instability due to higher aerodynamic loading. At this rotor speed, flow control on the outboard of the blade reduces unsteady bending as quantified by arms up to a maximum of 36.5%, and unsteady torsion, θrms up to 22.9% compared to the baseline for the rotor with an aspect ratio of 9.74.

A satellite altimetry data assimilation approach to optimise sea state estimates from vessel motion

Estimates of directional wave spectra and related parameters can be obtained from ship motion data through the wave-buoy analogy approach. The fundamental input is the response amplitude operator (RAO), which translates ship response into a wave energy spectrum. While ship motion is routinely measured on ocean going vessels, the RAO is not directly available and it is approximated using ship hydrodynamic models. The lack of publicly available details of hull geometry and loading conditions can results in significant inaccuracy of this operator. Considering the reliability of remotely sensed wave height, here we propose an assimilation technique that uses satellite altimeter observations to calibrate the RAO and minimise its uncertainties. The method is applied to estimate sea state conditions during the Antarctic Circumnavigation Expedition by converting motion response of the icebreaker Akademik Tryoshnikov as recorded by the on-board inertial measurement unit. Comparison against concurrent sea state observations obtained from a marine radar device shows a good agreement for a variety of parameters including significant wave height, wave periods and mean wave direction.

Autonomous control of a prototype solar‐powered offshore autonomous underwater vehicle servicing platform via a low‐cost embedded architecture

AbstractThis paper describes the design, implementation, and field‐testing of a low‐cost autonomous control system on the Platform for Expanding Autonomous Underwater Vehicles (AUV) exploRation to Longer ranges (PEARL). PEARL is a floating, solar‐powered platform designed as a servicing station for AUVs. This paper describes the field testing of a prototype system currently without the ability to dock and recharge AUVs. In this prototype, autonomous operations are realized through the open‐source MOOS‐IvP marine robotics software running on an embedded architecture including a Raspberry Pi microcomputer, Arduino microcontroller, and onboard inertial measurement unit and GPS sensors. A solar charge controller monitors energy system statistics in real‐time. Field tests included station‐keeping and waypoint missions in a freshwater pond in Orleans, MA. An RMS tracking error of 0.9–2.2 m was achieved during waypoint pattern executions, with an average battery usage of 13% per hour. Net power consumption rate will change due to variations in currents and winds, as well as variations in solar irradiance. The implementation of an autonomous sun‐tracking mode to maximize solar panel flux while station‐keeping is also evaluated. The field testing results will inform the design of a full‐scale PEARL capable of docking with AUVs. This research helps pave the way for autonomous solar‐powered service stations in the ocean that can connect the underwater world with space.

Mechanical Design and Fabrication of a Data Buoy

A data buoy for measuring coastal wave-action has been designed and constructed. The buoy is essentially a 0.6 m diameter sphere that is made of fiber-glass with aluminum, steel and polymer parts. While the overall geometry of the buoy is superficially similar to that of existing commercial spherical buoys (e.g. Wave-Rider by Datawell), the present design is based on a simple robust structure and mass-market electronics. A significant dual-use feature of this design is that the structure is usable as a float in a wave-energy generation device. This is possible due to the essentially isolated central structure and an electronics enclosure that is sealed both from the external environment and from the bulk shell of the buoy. This also allows the electronics to be mounted in an easily- modified, rack configuration that can be removed from the buoy shell. In a wave-energy generation set-up the electronics rack would be removed and the central cylinder will become part of a linear-motion type generator, with at least +/- 25 cm of internal movement available to it that is waterproof (for near surface or shallow immersions). Alternatively, for large wave motions, the buoy can couple to an energy-generating device using flanges that mate with its bolt circle pattern. However, at the moment, the buoy enclosure is dedicated to wave-action measurement, i.e., a data buoy with an onboard inertial measurement unit (IMU) and a radio-link to send real-time data to a receiving unit that can be on a boat or at the seashore. Basic GPS data are also transmitted. This is of interest if the buoy is being deployed in a drifting (un-moored) mode. This paper will only discuss the mechanical design and fabrication of the structure.

Linear Quadratic Gaussian Control for UAVs With Improved State Estimation Against Gyroscope and Accelerometer Biases

This paper proposes a linear quadratic Gaussian control for trajectory tracking of a quadrotor UAV. It involves implementing an optimal linear quadratic regulator control with integral action in an inner-outer loop control architecture. The full-state multi-rate extended Kalman filter generates the feedback required for the optimal control. Biases in gyroscope and accelerometer measurements are also incorporated into the Kalman filter to avoid degradation of the closed-loop response and to provide accurate state estimates. The proposed control architecture is tested on an experimental test bed consisting of a quadrotor UAV platform. The onboard inertial measurement unit, altimeter, and motion capture system provides the necessary measurements. The recorded results validate the performance of the proposed control scheme with improved state feedback generated through the extended Kalman filter.

Lateral Misalignment and Yaw Angle Control Considering Vehicle Dynamics Based on Receiving Energy in Dynamic Wireless Power Transfer

Recently, dynamic wireless power transfer (DWPT) has been shown to be a promising technology for the widespread of electric vehicles (EVs). To utilize this technology, a critical challenge is to eliminate the coil misalignments, which cause a decrease in the coupling coefficient, thereby considerably degrading the power transfer efficiency. Considering the rectangular coils, this paper presents a new method to estimate and eliminate lateral misalignments. Based on the dynamics model of the vehicle, the lateral misalignment is estimated by the fusion of the DWPT current and the onboard inertial measurement unit. On the other hand, the misalignment and the yaw angle of the vehicle are simultaneously controlled by using rear in-wheel motors and the front active steering system. The effectiveness of the proposed method has been demonstrated by both simulations and experiments using a vehicle developed by our research group.

Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments.

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.

Deep learning of monocular depth, optical flow and ego-motion with geometric guidance for UAV navigation in dynamic environments

Computer vision-based depth estimation and visual odometry provide perceptual information useful for robot navigation tasks like obstacle avoidance. However, despite the proliferation of state-of-the-art convolutional neural network (CNN) models for monocular depth, ego-motion and optical flow estimation, a relatively low volume of work has been reported on their practical applications in unmanned aerial vehicle (UAV) navigation. This is due to well-known challenges — embedded hardware constraints, viewpoint variations, scarcity of aerial image datasets, and intricacies of dynamic environments. We address these limitations to facilitate real-world deployment of CNN in UAV navigation. First, we devise efficient confidence weighted adaptive network (Cowan) training framework that iteratively leverages intermediate prediction confidences to enforce cross-task consistency over corresponding image regions. This achieves competitive accuracy with a lightweight CNN capable of real-time execution on resource-constrained embedded systems. Second, we devise a test-time refinement method that adapts the network to dynamic environments while simultaneously improving accuracy. To accomplish this, we first update ego-motion using pose information from on-board inertial measurement unit (IMU). Then, we decompose the UAV’s motion into constituent vectors, and for each axis, we formulate geometric relationships between depth and translation. Based on this information, we triangulate corresponding points acquired through optical flow. Finally, we enforce geometric consistency between the initially updated pose and triangulated depth. Cowan with geometric guided refinement (Cowan-GGR) achieves significant accuracy and robustness. Field tests show the proposed model is capable of accurate depth and object-level motion perception in real-world dynamic environments, thus proving its efficacy in facilitating UAV navigation.

A Spatial Localization and Attitude Estimation System for Unmanned Aerial Vehicles Using a Single Dynamic Vision Sensor

This paper presents a three-dimensional (3D) localization and attitude estimation system to track a Unmanned Aerial Vehicle (UAV) using a single camera without prior knowledge of the environment. The hardware system consists of a Dynamic Vision Sensing (DVS) camera, a circle-shaped blinking marker made by Light-Emitting Diodes (LEDs), and a base station computer. The algorithm for spatial localization and attitude estimation includes a temporal video filter, triangulation-based location and attitude estimation, and 3D real-time plotting with a graphical user interface (GUI). The temporal video filter processes the image stream from the DVS camera to identify the frequency of the marker and removes the background image. The circle-shaped marker creates an ellipse in the image, whose diameter length and angles are utilized for calculating the location and attitude of the UAV, which offers a low-computing overhead. The proposed system has been evaluated in hardware flight testing. The results are compared with the benchmark data from both the infrared motion capturing system for localization and the on-board inertial measurement units of the UAV for attitude estimation. The accuracy and detection range surpasses similar state-of-the-art systems. The proposed method provides a simple yet accurate solution for tracking the location and attitude of a UAV.

Preventing Food Waste by the Shelf-Life Dynamical Control in Smart Homes

Currently, smart homes are designed and improved according to four main goals: residents’ wellness, security, comfort, and reduced energy waste. No focus has yet been reserved for one of the most impacting problems of recent years: domestic food waste. In this context, this letter proposes a case study that jointly involves recent advances in smart homes and the need for food waste reduction. The proposed system is divided into two main blocks: the sensing block and the actuation one. The sensing block consists of multiple nodes, which, in the proposed case study, work as dynamic labels being stuck to the good to be monitored. Each node embeds, on a miniaturized board, a set of sensors (i.e., temperature, humidity, and ambient light), whose outcomes are used to extract, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on-board</i> , the dynamic food shelf life (SL). The presence of an onboard inertial measurement unit also permits the nodes' localization inside the environment. If a sudden decrease in the food SL is recorded by the node and no human interventions occur in a preset timeout, the node is designed to send a call for intervention to the actuation block. In this letter, the actuation block is realized through an assistive robotic platform that can gather coordinates of food from sensor nodes, navigate to them to manipulate the goods, and actuate actions to improve their preservation conditions (increasing the perspective SL). The actuation block is also responsible for notifying the user of the bad storage conditions. The system has been experimentally tested in a laboratory environment. The whole mitigation chain successfully completed all the operations through the robotic platform in 60% of cases. Moreover, comparing food left in poor storage conditions and the same food managed by the our proposed system, an improvement of the food expiration date of ∼55% (on average) has been recorded.

Natural Navigation System Design for Indoor Mobile Robots

Natural navigation simply refers to free navigation without the necessity of tapes, magnets, reflectors, or even wires. Many autonomous vehicles possess this as world maps are readily available and provide a perfect basis for machine learning solutions. However, this is not so much the case for indoor applications. Here, paths are often dynamic and more constrained; therefore, requiring the continuous identification, mapping and localization of the surrounding area. This work focuses on developing an indoor natural navigation system; the localization is achieved with a fusion of the wheel’s odometry to the on-board Inertial Measurement Unit (IMU i.e., a combination of relative localization and absolute localization) using Unscented Kalman Filter (UKF) as system’s encoder’s accumulation of errors is desired to be nullified while employing a PID control in correcting reference state errors. The map is simultaneously constructed using laws of geometry based on static points obtained from a Lidar, subsequently converted to an occupancy grid layout for effective path planning. In operation, tangency is applied in the avoidance of dynamic obstacles. The simulation results obtained in this study confirms the possibility of a simple, educational, indoor navigation system approach easily integrable by other mobile robots of the differential drive model.

Range-aided ego-centric collaborative pose estimation for multiple robots

Robots’ simultaneous relative pose estimation has become an essential step in most robotic-oriented problems, such as map merging, collision avoidance, path planning, and multi-Simultaneous Localization and Mapping (SLAM). This article addresses the problem of 3D and ego-centric relative pose estimation for a team of robots equipped with Ultra WideBand (UWB) nodes. More specifically, the article introduces a novel optimization framework to obtain pose information based on the embodiment of UWB ranges, without relying on any fixed external infrastructure configuration of UWB anchors on the surrounding environment. In the proposed method, we demonstrate the validity through the utilization of a Micro Aerial Vehicle (MAV) and a ground vehicle that are equipped with multiple UWB transceivers, and each platform simultaneously acts as a based anchor for the other platform for extracting an ego-centric position estimation of the UWB nodes. Additionally, for the pose estimation, the obtained information is fused with the onboard Inertial Measurement Unit (IMU) measurements on each of the considered robotic platforms. Finally, the efficacy of the proposed theoretical framework is evaluated in multiple experiments, where the aerial and ground platforms are simultaneously and separately navigating, and the ego-centric collaborative pose-estimation is compared with a VICON ground truth positioning system.

Reconstructed Performance of the Supersonic Parachute of the Mars InSight Lander

On 26 November 2018, the Mars InSight lander successfully touched down at Elysium Planitia and began its 26 month prime mission. The entry, descent, and landing sequence of InSight included an 11.8 m supersonically deployed disk–gap–band parachute. This paper describes the reconstructed performance of the supersonic parachute of InSight on Mars. Measurements from the onboard inertial measurement unit along with prelaunch measurements of the parachute system and spacecraft, assumptions about the aerodynamics of the vehicle, and models for the Martian atmosphere have been used to reconstruct the trajectory of the spacecraft and the performance of the parachute system. The reconstruction results were compared against preflight predictions. Reconstruction of the InSight trajectory leading up to parachute deployment showed that the vehicle trimmed in a lift-down orientation during entry, and thus experienced greater deceleration than expected by most preflight simulations. This led to parachute deployment conditions that diverged from the nominal preflight predictions. The parachute was mortar deployed at a Mach number of approximately 1.5, below the nominal preflight expectation of 1.66. The peak inflation load was 45 kN, well below the 67 kN design limit load of the parachute. Following deployment of the parachute, the rotational rates of the vehicle and the dynamics of the system were in excellent agreement with preflight expectations.