Tag Archives: Obstacle Avoidance

Evaluating the safeness of a motion plan for mobile robot navigation

Brian Axelrod, Leslie Pack Kaelbling, and Tomás Lozano-Pérez Provably safe robot navigation with obstacle uncertainty, The International Journal of Robotics Research Vol 37, Issue 7 DOI: 10.1177/0278364918778338.

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most ϵ collision probability (ϵ-safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method’s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.

Real-time modification of user inputs in the teleoperation of an UAV in order to avoid obstacles with a reactive algorithm, transparently from the user control

Daman Bareiss, Joseph R. Bourne & Kam K. Leang, On-board model-based automatic collision avoidance: application in remotely-piloted unmanned aerial vehicles, Auton Robot (2017) 41:1539–1554, DOI: 10.1007/s10514-017-9614-4.

This paper focuses on real-world implementation and verification of a local, model-based stochastic automatic collision avoidance algorithm, with application in
remotely-piloted (tele-operated) unmanned aerial vehicles (UAVs). Automatic collision detection and avoidance for tele-operated UAVs can reduce the workload of pilots to allow them to focus on the task at hand, such as searching for victims in a search and rescue scenario following a natural disaster. The proposed algorithm takes the pilot’s input and exploits the robot’s dynamics to predict the robot’s trajectory for determining whether a collision will occur. Using on-board sensors for obstacle detection, if a collision is imminent, the algorithm modifies the pilot’s input to avoid the collision while attempting to maintain the pilot’s intent. The algorithm is implemented using a low-cost on-board computer, flight-control system, and a two-dimensional laser illuminated detection and ranging sensor for obstacle detection along the trajectory of the robot. The sensor data is processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Results from flight tests demonstrate the algorithm’s capabilities for teleoperated collision-free control of an experimental UAV.