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Distributed Spatiotemporal Motion Planning for Spacecraft Swarms in Cluttered Environments
This paper focuses on trajectory planning for spacecraft swarms in cluttered environments, like debris fields or the asteroid belt. Our objective is to reconfigure the spacecraft swarm to a desired formation in a distributed manner while minimizing fuel and avoiding collisions among themselves and with the obstacles. In our prior work we proposed a novel distributed guidance algorithm for spacecraft swarms in static environments.1 In this paper, we present the Multi-Agent Moving-Obstacles Spherical Expansion and Sequential Convex Programming (MAMO SE–SCP) algorithm that extends our prior work to include spatiotemporal constraints such as time-varying, moving obstacles and desired time-varying terminal positions. In the MAMO SE–SCP algorithm, each agent uses a spherical-expansion-based sampling algorithm to cooperatively explore the time-varying environment, a distributed assignment algorithm to agree on the terminal position for each agent, and a sequential-convex-programming-based optimization step to compute the locally-optimal trajectories from the current location to the assigned time-varying terminal position while avoiding collision with other agent and the moving obstacles. Simulations results demonstrate that the proposed distributed algorithm can be used by a spacecraft swarm to achieve a time-varying, desired formation around an object of interest in a dynamic environment with many moving and tumbling obstacles.
Distributed fast motion planning for spacecraft swarms in cluttered environments
UNKNOWN
Model-Free Multi-Agent Motion Planning with Non-Uniform Sensors.
Abstract not provided.
Avoiding space robot collisions utilizing the NASA/GSFC tri-mode skin sensor
A capacitance based proximity sensor, the 'Capaciflector' (Vranish 92), has been developed at the Goddard Space Flight Center of NASA. We had investigated the use of this sensor for avoiding and maneuvering around unexpected objects (Mahalingam 92). The approach developed there would help in executing collision-free gross motions. Another important aspect of robot motion planning is fine motion planning. Let us classify manipulator robot motion planning into two groups at the task level: gross motion planning and fine motion planning. We use the term 'gross planning' where the major degrees of freedom of the robot execute large motions, for example, the motion of a robot in a pick and place type operation. We use the term 'fine motion' to indicate motions of the robot where the large dofs do not move much, and move far less than the mirror dofs, such as in inserting a peg in a hole. In this report we describe our experiments and experiences in this area.
Human-automated vehicle interactions
This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.
HUMAN-AUTOMATED VEHICLE INTERACTIONS
This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.
Firmware Architecture of the ARMADAS Bolting Robot
The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project, under development at NASA Ames Research Center, has demonstrated on-ground autonomous robotic assembly of extensive digital structures, and it is now moving forward towards in-space demonstration. The ARMADAS system comprises of the operation software, the operation user interface (opsUI), and a swarm of robots. The robotic system consists of a multitude of collaborative agents specifically designed to transport, place and bolt the building blocks, called voxels (volumetric pixels). This paper focuses on the bolting robot, referred to as Mobile Metamaterial Internal Co-Integrator (MMIC-I). MMIC-I is a battery-powered crawling robot. It navigates the structure through extension, contraction and gripping. Two distinct controller boards operate the robot's two symmetric modules, referred to as module A and B. Board A is the master board: it coordinates motion planning and motion primitives execution, hosts the WiFi client, performs periodic self-assessment and system idle check and triggers faults if anomalies are detected. Board B periodically sends a heartbeat to board A, through a wired communication channel that uses the Serial protocol. Additionally, board A's WiFi client receives heartbeat packet requests or motion/bolting commands from a dedicated server board, and acknowledges reception sending back a response heartbeat packet containing information about the overall robot status, e. g. electrical current and voltage values, target and actual angles, operating mode, fault status. Whenever a motion command is sent, the motion planning section of the firmware determines the current robot configuration, using Inertial Measurement Unit readings and the motors Pulse Width Modulation values. Afterwards, it calculates the list of primitives needed to reach the target state, and controls their execution in the proper order. MMIC-I can receive and execute motion and bolting commands only when it is in operational mode. MMIC-I has three operating modes: standby, operational and safed. Standby mode is automatically entered upon startup. While in standby mode, all motors are powered off, and the only accepted commands are the ones relative to a change of mode and heartbeat packet request. Fault detection causes the robot to automatically enter safed or standby mode. Whenever the detected fault occurs within a motion and requires immediate intervention, e. g. an over-current situation, the robot enters safed mode. Safed mode powers off all motors except for the locomotion module, thus preventing the robot from collapsing. Conversely, when the detected fault doesn't require immediate intervention (low battery warning, for instance), the robot enters standby mode after completing the ongoing motion. This paper provides a detailed discussion of MMIC-I's firmware architecture. It accurately describes the implementation approach for each module: sensor data reading, motor control and actuation, WiFi server-client communication, intra-boards Serial communication, operating modes and autonomous fault detection, motion planning, coordination and execution, etc. Moreover, in support of the software description, this paper includes a thorough characterization of MMIC-I's hardware and avionics.
Autonomous Emergency Landing for Fixed-Wing Aircraft with Energy-Constrained Closed-Loop Prediction
Here this paper presents a new approach for autonomous motion planning for aircraft suffering from a loss-of-thrust emergency. Specifically, we show how modifications to the Closed-Loop Rapidly exploring Random Trees (CL-RRT) framework combined with controlled energy dissipation can enable rapid and effective kinodynamic motion planning. This CL-RRT Glide algorithm uses closed-loop prediction not only for node connections but also to estimate the remaining energy and prune infeasible paths. This greatly speeds up the search process, which is essential for emergency situations. In addition, we improve the ability of the gliding aircraft to reach a goal position and energy state. We do so by creating a Dissipative Total Energy Control Scheme (TECS). Dissipative TECS enables the glider to lose excess altitude in order to reach a desired energy level. Simulation results illustrate how the proposed methods enable faster motion planning. We also integrate the system into a small unmanned aerial vehicle system and experimentally demonstrate autonomous glide planning and execution during a motor-failure event. This type of algorithm can primarily benefit unmanned aircraft but can also serve to assist pilots in stressful emergency situations.
High speed precision motion strategies for lightweight structures
Work during the recording period proceeded along the lines of the proposal, i.e., three aspects of high speed motion planning and control of flexible structures were explored: fine motion control, gross motion planning and control, and automation using light weight arms. In addition, modeling the large manipulator arm to be used in experiments and theory has lead to some contributions in that area. These aspects are reported below. Conference, workshop and journal submissions, and presentations related to this work were seven in number, and are listed. Copies of written papers and abstracts are included.
Real-time collision avoidance in teleoperated whole-sensitive robot arm manipulators
A hybrid robot teleoperation system is presented which makes use of the methodology of motion planning for whole-sensitive robots to assist the operator in generating collision-free motion in a master-slave robot arm manipulator system. The system combines operator commands with data from the sensitive skin to guarantee safe motion for the entire body of the robot arm. The arm avoids obstacles automatically and in real time and moves in a collision-free manner although no prior knowledge of the objects in the environment is available to the motion planning system and no constraints are imposed on the obstacle shapes. The operator is thus relieved of the task of providing safety of the robot arm and surrounding objects.
Graphics simulation and training aids for advanced teleoperation
Graphics displays can be of significant aid in accomplishing a teleoperation task throughout all three phases of off-line task analysis and planning, operator training, and online operation. In the first phase, graphics displays provide substantial aid to investigate work cell layout, motion planning with collision detection and with possible redundancy resolution, and planning for camera views. In the second phase, graphics displays can serve as very useful tools for introductory training of operators before training them on actual hardware. In the third phase, graphics displays can be used for previewing planned motions and monitoring actual motions in any desired viewing angle, or, when communication time delay prevails, for providing predictive graphics overlay on the actual camera view of the remote site to show the non-time-delayed consequences of commanded motions in real time. This paper addresses potential space applications of graphics displays in all three operational phases of advanced teleoperation. Possible applications are illustrated with techniques developed and demonstrated in the Advanced Teleoperation Laboratory at JPL. The examples described include task analysis and planning of a simulated Solar Maximum Satellite Repair task, a novel force-reflecting teleoperation simulator for operator training, and preview and predictive displays for on-line operations.
Towards Persistent Space Observations through Autonomous Multi-Agent Formations
Sensing platforms must advance in scale and sophistication in order to support increasingly ambitious missions across Earth and space science; intelligence, surveillance, and reconnaissance (ISR); and planetary exploration. Distributed, persistent observation platforms have the potential to play a pivotal role in next generation missions through improved area coverage, enhanced situational awareness, and faster identification of trends and changes. The Multi-Agent Clusters for Persistent Observations from Space (MACPOS) project at NASA Langley Research Center is developing key technologies for the autonomous, heterogeneous formations that will comprise such platforms. Research thrusts include dynamic formation negotiation for self-assembling clusters of agents, distributed motion planning, and coordinated trajectory execution. Adaptive leader-follower formation negotiation allows agents to cluster and break off as necessary to adapt to both nominal and new mission objectives. Coordinated motion planning and execution maintain the formation while ensuring safe separation distances among agents and obstacles in the environment. These capabilities align MACPOS with NASA’s initiative for space and surface in-situ assembly through fundamental technology development for autonomous multi-agent systems. This paper presents an overview and early progress for the MACPOS project. We describe the system architecture for both individual agents and the overall fleet. Design considerations are given for the planning, control, and metrology subsystems. Finally, we discuss planned project milestones and the expected course of development.
The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations
Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.
A Sampling Based Approach to Spacecraft Autonomous Maneuvering with Safety Specifications
This paper presents a methods for safe spacecraft autonomous maneuvering that leverages robotic motion-planning techniques to spacecraft control. Specifically the scenario we consider is an in-plan rendezvous of a chaser spacecraft in proximity to a target spacecraft at the origin of the Clohessy Wiltshire Hill frame. The trajectory for the chaser spacecraft is generated in a receding horizon fashion by executing a sampling based robotic motion planning algorithm name Fast Marching Trees (FMT) which efficiently grows a tree of trajectories over a set of probabillistically drawn samples in the state space. To enforce safety the tree is only grown over actively safe samples for which there exists a one-burn collision avoidance maneuver that circularizes the spacecraft orbit along a collision-free coasting arc and that can be executed under potential thrusters failures. The overall approach establishes a provably correct framework for the systematic encoding of safety specifications into the spacecraft trajectory generations process and appears amenable to real time implementation on orbit. Simulation results are presented for a two-fault tolerant spacecraft during autonomous approach to a single client in Low Earth Orbit.
Manipulation strategies for massive space payloads
Motion planning and control for the joints of flexible manipulators are discussed. Specific topics covered include control of a flexible braced manipulator, control of a small working robot on a large flexible manipulator to suppress vibrations, control strategies for ensuring cooperation among disparate manipulators, and motion planning for robots in free-fall.
Avoiding space robot collisions utilizing the NASA/GSFC tri-mode skin sensor
Sensor based robot motion planning research has primarily focused on mobile robots. Consider, however, the case of a robot manipulator expected to operate autonomously in a dynamic environment where unexpected collisions can occur with many parts of the robot. Only a sensor based system capable of generating collision free paths would be acceptable in such situations. Recently, work in this area has been reported in which a deterministic solution for 2DOF systems has been generated. The arm was sensitized with 'skin' of infra-red sensors. We have proposed a heuristic (potential field based) methodology for redundant robots with large DOF's. The key concepts are solving the path planning problem by cooperating global and local planning modules, the use of complete information from the sensors and partial (but appropriate) information from a world model, representation of objects with hyper-ellipsoids in the world model, and the use of variational planning. We intend to sensitize the robot arm with a 'skin' of capacitive proximity sensors. These sensors were developed at NASA, and are exceptionally suited for the space application. In the first part of the report, we discuss the development and modeling of the capacitive proximity sensor. In the second part we discuss the motion planning algorithm.
Comparison of Acoustic Models and Trajectory Generation Methods for an Acoustically-Aware Aircraft
This paper presents a comparison of trajectory generation methodologies using acoustic source noise models of different fidelity for motion planning for an acoustically-aware aircraft subject to constraints on the vehicle dynamic performance, mission, and acoustic footprint of the vehicle at a set of (three-dimensional) observer locations. The performance of a pre-mission Bézier curve-based planner and a (near) real-time stochastic model predictive control planner are compared. Additionally, a comparison is made between the motion planning performance using a lower-fidelity acoustic model based on propeller tip Mach number and omni-directional sound power radiation, and a hemisphere-based higher-fidelity acoustic model. It is demonstrated that the asymmetry in hemisphere-based acoustic model can be exploited for improved flight path planning and trajectory-tracking performance in the presence of acoustic constraints.