Safe motion using viability kernels (original) (raw)
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Viability-Based Guaranteed Safe Robot Navigation
Journal of Intelligent & Robotic Systems, 2018
Guaranteeing safe, i.e. collision-free, motion for robotic systems is usually tackled in the Inevitable Collision State framework. This paper explores the use of the more general Viability theory as an alternative when safe motion involves multiple motion constraints and not just collision avoidance. Central to Viability is the so-called viability kernel, i.e. the set of states of the robotic system for which there is at least one trajectory that satisfies the motion constraints forever. The paper presents an algorithm that computes off-line an approximation of the viability kernel that is both conservative and able to handle time-varying constraints such as moving obstacles. Then it demonstrates, for different robotic scenarios involving multiple motion constraints (collision avoidance, visibility, velocity), how to use the viability kernel computed off-line within an on-line reactive navigation scheme that can drive the robotic system without ever violating the motion constraints at hand.
Improved motion planning speed and safety using regions of inevitable collision
2008
Providing safety guarantees for autonomous robots operating in environments with moving obstacles is a difficult problem, particularly for underactuated systems or systems with drift due to momentum. The conventional approach to replanning in dynamic environments typically computes partial plans within the allotted CPU time and validates explored states through robot-obstacle collision checks. However, this approach cannot provide any safety guarantees for the robot beyond the finite planning horizon. This paper explores the approximate computation of regions of inevitable collision for state validation in a replanning framework for dynamic systems. We present experimental results that demonstrate the effectiveness of this technique in providing dramatically improved safety for partial plans in the domain of an underactuated dynamic vehicle. Figure 1. Top Left: Planning among moving obstacles for an underactuated spacecraft; Bottom Left: Cost maps over time showing the Regions of Inevitable Collision (RIC); Right: Detail of RIC combined cost map visualization.
Provably Safe Motions Strategies for Mobile Robots in Dynamic Domains
Springer Tracts in Advanced Robotics, 2007
We present in this paper a methodology for computing the maximum velocity profile over a trajectory planned for a mobile robot. Environment and robot dynamics as well as the constraints of the robot sensors determine the profile. The planned profile is indicative of maximum speeds that can be possessed by the robot along its path without colliding with any of the mobile objects that could intercept its future trajectory. The mobile objects could be arbitrary in number and the only information available is their maximum possible velocity. The velocity profile also enables to deform planned trajectories for better trajectory time. The methodology has been adopted for holonomic and non-holonomic motion planners. An extension of the approach to an online real-time scheme that modifies and adapts the path as well as velocities to changes in the environment such that both safety and execution time are not compromised is also presented for the holonomic case. Simulation and experimental results illustrate the efficacy of this methodology.
Provably safe navigation for mobile robots with limited field-of-views in dynamic environments
This paper addresses the problem of navigating in a provably safe manner a mobile robot with a limited fieldof-view placed in a unknown dynamic environment. In such a situation, absolute motion safety (in the sense that no collision will ever take place whatever happens in the environment) is impossible to guarantee in general. It is therefore settled for a weaker level of motion safety dubbed passive motion safety: it guarantees that, if a collision takes place, the robot will be at rest.
ArXiv, 2016
An autonomous navigation with proven collision avoidance in unknown and dynamic environments is still a challenge, particularly when there are moving obstacles. A popular approach to collision avoidance in the face of moving obstacles is based on model predictive algorithms, which, however, may be computationally expensive. Hence, we adopt a reactive potential field approach here. At every cycle, the proposed approach requires only current robot states relative to the closest obstacle point to find the potential field in the current position; thus, it is more computationally efficient and more suitable to scale up for multiple agent scenarios. Our main contribution here is to write the reactive potential field based motion controller as a hybrid automaton, and then formally verify its safety using differential dynamic logic. In particular, we can guarantee a passive safety property, which means that collisions cannot occur if the robot is to blame, namely a collision can occur only ...
Safety assessment of robot trajectories for navigation in uncertain and dynamic environments
Autonomous Robots, 2012
This paper presents a probabilistic threat assessment method for reasoning about the safety of robot trajectories in uncertain and dynamic environments. For safety evaluation, the overall collision probability is used to rank candidate trajectories by considering the collision probability of known objects as well as the collision probability beyond the planning horizon. Monte Carlo sampling is used to estimate the collision probabilities. This concept is applied to a navigation framework that generates and selects trajectories in order to reach the goal location while minimizing the collision probability. Simulation scenarios are used to validate the overall crash probability and show its necessity in the proposed navigation approach.
Real-time safety for human - robot interaction
ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005., 2005
This paper presents a strategy for ensuring safety during human-robot interaction in real time. A measure of danger during the interaction is explicitly computed, based on factors affecting the impact force during a potential collision between the human and the robot. This danger index is then used as an input to real-time trajectory generation when the index exceeds a predefined threshold. The danger index is formulated to produce a provably stable, fast response in the presence of multiple surrounding obstacles. A motion strategy to minimize the danger index is developed for articulated multi-degree-of-freedom robots. Simulations and experiments show the efficacy of this approach.
An Algorithm in the Process of Planning of Safety Pathway – a Collision-Free Pathway of a Robot
Advances in Science and Technology Research Journal, 2019
The paper was based on research tasks in the field of robotics. It approaches solving the collision states of a robot while operating equipment and applies the knowledge in the CATIA system environment. Motion Planning is of paramount importance in robotics, where a goal is to determine a collision-free, unobstructed path for a robot that works in an environment which contains obstacles. An obstacle can be an object that is found in the robot's workspace.
Reachable Sets for Safe, Real-Time Manipulator Trajectory Design
Robotics: Science and Systems XVI
For robotic arms to operate in arbitrary environments, especially near people, it is critical to certify the safety of their motion planning algorithms. However, there is often a trade-o↵ between safety and real-time performance; one can either carefully design safe plans, or rapidly generate potentiallyunsafe plans. This work presents a receding-horizon, real-time trajectory planner with safety guarantees, called ARMTD (Autonomous Reachability-based Manipulator Trajectory Design). The method first computes (o✏ine) a reachable set of parameterized trajectories for each joint of an arm. Each trajectory includes a fail-safe maneuver (braking to a stop). At runtime, in each receding-horizon planning iteration, ARMTD constructs a parameterized reachable set of the full arm in workspace and intersects it with obstacles to generate sub-di↵erentiable, provablyconservative collision-avoidance constraints on the trajectory parameters. ARMTD then performs trajectory optimization over the parameters, subject to these constraints. On a 6 degree-of-freedom arm, ARMTD outperforms CHOMP in simulation, never crashes, and completes a variety of real-time planning tasks on hardware.